name stringlengths 5 6 | title stringlengths 8 144 | abstract stringlengths 0 2.68k | fulltext stringlengths 1.78k 95k | keywords stringlengths 22 532 |
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588424 | Convergence of a Difference Scheme for Conservation Laws with a Discontinuous Flux. | Convergence is established for a scalar finite difference scheme, based on the Godunov or Engquist--Osher (EO) flux, for scalar conservation laws having a flux that is spatially dependent through a possibly discontinuous coefficient. Other works in this direction have established convergence for methods employing the s... | Introduction
. The subject of this paper is a nite dierence algorithm for
computing approximate solutions of the Cauchy problem for scalar conservation laws
of the form
1). The
ux k(x)f(u) has a possibly discontinuous spatial
dependence through the coe-cient k, which is allowed to have jump discontinuities.
A simple ph... | conservation laws;difference approximations;discontinuous coefficients |
588428 | Long-Time Energy Conservation of Numerical Methods for Oscillatory Differential Equations. | We consider second-order differential systems where high-frequency oscillations are generated by a linear part. We present a frequency expansion of the solution, and we discuss two invariants of the system that determine the coefficients of the frequency expansion. These invariants are related to the total energy and t... | Introduction
. Long-time near-conservation of the total energy and of adiabatic
invariants in numerical solutions to Hamiltonian differential equations is important
in a wide range of physical applications from molecular dynamics to nonlinear
wave propagation. Backward error analysis [BG94, HaL97, Rei98] has shown that... | Fermi-Pasta-Ulam problem;frequency expansion;backward error analysis;second-order symmetric methods;oscillatory differential equations;long-time energy conservation |
588460 | Iterative Substructuring Preconditioners for Mortar Element Methods in Two Dimensions. | The mortar methods are based on domain decomposition and they allow for the coupling of different variational approximations in different subdomains. The resulting methods are nonconforming but still yield optimal approximations. In this paper, we will discuss iterative substructuring algorithms for the algebraic syste... | Introduction
. Since the late nineteen eighties, interest has developed in non-overlapping
domain decomposition methods coupling different variational approximations
in different subdomains. The mortar element methods, see [10], have been designed
for this purpose and they allow us to combine different discretizations ... | domain decomposition;mortar finite element method;iterative substructuring |
588473 | Analysis of Numerical Errors in Large Eddy Simulation. | We consider the question of "numerical errors" in large eddy simulation. It is often claimed that straightforward discretization and solution using centered methods of models for large eddy motion can simulate the motion of turbulent flows with complexity independent of the Reynolds number and dependence only on the re... | Introduction
The laminar or turbulent
ow of an incompressible
uid is modeled by solutions (u; p) of the
incompressible Navier-Stokes equations:
in
r
in
in
@
Z
(1)
Here
is a bounded, simply connected domain with polygonal boundary
d is the
uid velocity, p
R is the
uid pressure,
f(x; t) is the (known) body force, u 0 (x)... | navier-stokes equations;turbulence;large eddy simulation;finite element methods |
588514 | Superconvergence of the Local Discontinuous Galerkin Method for Elliptic Problems on Cartesian Grids. | In this paper, we present a superconvergence result for the local discontinuous Galerkin (LDG) method for a model elliptic problem on Cartesian grids. We identify a special numerical flux for which the L2-norm of the gradient and the L2-norm of the potential are of orders k+1/2 and k+1, respectively, when tensor produc... | Introduction
. In this paper, we derive a priori error estimates of the Local
Discontinuous Galerkin (LDG) method on Cartesian grids for the following classical
model elliptic problem:
@n
where\Omega is a bounded domain of R d and n is the outward unit normal to its boundary
we assume that the (d \Gamma 1)-measure of \... | finite elements;elliptic problems;discontinuous Galerkin methods;cartesian grids;superconvergence |
588515 | Superlinear Convergence of Conjugate Gradients. | We give a theoretical explanation for superlinear convergence behavior observed while solving large symmetric systems of equations using the conjugate gradient method or other Krylov subspace methods. We present a new bound on the relative error after $n$ iterations. This bound is valid in an asymptotic sense when the ... | Introduction
. The Conjugate Gradient (CG) method is widely used for solving
systems of linear equations with a positive denite symmetric matrix A.
The CG method is popular as an iterative method for large systems, stemming e.g.
from the discretisation of boundary value problems for elliptic PDEs. The rate of
convergen... | superlinear convergence;conjugate gradients;toeplitz systems;krylov subspace methods;logarithmic potential theory |
588536 | Integral Operators on Sparse Grids. | In this paper we are concerned with the construction and use of wavelet approximation spaces for the fast evaluation of integral expressions. The spaces are based on biorthogonal anisotropic tensor product wavelets. We introduce sparse grid (hyperbolic cross) approximation spaces which are adapted not only to the smoo... | Introduction
. A naive Galerkin discretization of an integral operator
Z
with global kernel K leads to a dense stiness matrix. Hence, on a uniform full grid
with O(2 nJ ) unknowns (n dimension, J maximal level in a multiscale discretization),
the discrete operator has O(2 2nJ ) entries. This makes matrix vector multipl... | sparse grids;optimized approximation spaces;biorthogonal wavelets;hyperbolic cross approximation;compression;integral equations;boolean blending |
588564 | An A Priori Error Analysis of the Local Discontinuous Galerkin Method for Elliptic Problems. | In this paper, we present the first a priori error analysis for the local discontinuous Galerkin (LDG) method for a model elliptic problem. For arbitrary meshes with hanging nodes and elements of various shapes, we show that, for stabilization parameters of order one, the L2-norm of the gradient and the L2-norm of the... | Introduction
. In this paper, we present the first a priori error analysis of the
Local Discontinuous Galerkin (LDG) method for the following classical model elliptic
problem:
@n
where\Omega is a bounded domain of R d and n is the outward unit normal to its boundary
for the sake of simplicity, we assume that the (d \Ga... | finite elements;elliptic problems;discontinuous Galerkin methods |
588569 | Numerical Approximation of the Maximal Solutions for a Class of Degenerate Hamilton-Jacobi Equations. | In this paper we study an approximation scheme for a class of Hamilton--Jacobi problems for which uniqueness of the viscosity solution does not hold. This class includes the eikonal equation arising in the shape-from-shading problem. We show that, if an appropriate stability condition is satisfied, the scheme converg... | Introduction
Given a Hamilton-Jacobi equation, a general result due to Barles-Souganidis [3] says that
any "reasonable" approximation scheme (based f.e. on finite differences, finite elements,
finite volumes, discretization of characteristics, etc.) converges to the viscosity solution of
the equation. Besides some simp... | maximal solution;singular Hamilton-Jacobi equations;regularization;discretization error;numerical approximation |
588626 | Multilevel Boundary Functionals for Least-Squares Mixed Finite Element Methods. | For least-squares mixed finite element methods for the first-order system formulation of second-order elliptic problems, a technique for the weak enforcement of boundary conditions is presented. This approach is based on least-squares boundary functionals, which are equivalent to the H-1/2 and H1/2 norms on the trace ... | Introduction
. In the context of least-squares finite element methods for first-order
systems, boundary conditions can be enforced as essential boundary conditions
in the finite element spaces. This yields optimal order convergence of the Galerkin
approximations under suitable assumptions on the regularity of the probl... | multilevel boundary functionals;least-squares mixed finite element method;raviart-thomas spaces |
588632 | Detection of Edges in Spectral Data II. Nonlinear Enhancement. | We discuss a general framework for recovering edges in piecewise smooth functions with finitely many jump discontinuities, where $[f](x):=f(x+)-f(x-) \neq 0$. Our approach is based on two main aspects--- localization using appropriate concentration kernels and separation of scales by nonlinear enhancement. To detect su... | Introduction
We discuss a general framework for recovering edges from the spectral projections of piecewise smooth
functions. Our approach for edge detection is based on two fundamental aspects - localization to
the neighborhood of the edges using appropriate concentration kernels and separation of scales by
nonlinear ... | piecewise smoothness;concentration kernels;spectral expansions |
588637 | Multiplicative Schwarz Algorithms for the Galerkin Boundary Element Method. | We study the multiplicative Schwarz method for the h- and the p-version Galerkin boundary element method for a hypersingular and a weakly singular integral equation of the first kind. For both integral equations we prove that the contraction rate of the multiplicative Schwarz operator is strictly less than 1 for the h-... | Introduction
.
We study multiplicative Schwarz methods for the h and p versions of the Galerkin boundary
element method applied to hypersingular and weakly singular integral equations on open or
closed curves. These equations are integral reformulations for boundary value problems with
the Laplace equation and the Diri... | p-version Galerkin boundary element method;h-version Galerkin boundary element method;multiplicative Schwarz;multigrid algorithm |
588642 | Smoothing Methods and Semismooth Methods for Nondifferentiable Operator Equations. | We consider superlinearly convergent analogues of Newton methods for nondifferentiable operator equations in function spaces. The superlinear convergence analysis of semismooth methods for nondifferentiable equations described by a locally Lipschitzian operator in Rn is based on Rademacher's theorem which does not hold... | Introduction
. This paper considers the nonlinear operator equation
Y is a continuous mapping, X and Y are Banach spaces, and D
is an open domain in X . In a number of problems, the operator F is nondifferentiable.
For example, a class of such problems arising in optimal control problems for parabolic
partial different... | nonsmooth elliptic partial differential equations;superlinear convergence;semismooth methods;nondifferentiable operator equation;smoothing methods |
588815 | Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing. | We consider the one-dimensional bin packing problem under the discrete uniform distributions $U\{j,k\}$, $1 \leq j \leq k-1$, in which the bin capacity is $k$ and item sizes are chosen uniformly from the set $\{1,2,\ldots,j\}$. Note that for $0 < this is a discrete version of the previously studied continuous uniform d... | Introduction
. Suppose one is given items of sizes 1, 2, 3, . , j, one of each
size, and is asked to pack them into bins of capacity k with as little wasted space as
possible, i.e., one is asked to find a least cardinality partition (packing) of the set of
items such that the sizes of the items in each block (bin) sum ... | approximation algorithms;bin packing;average-case analysis;online |
588918 | Convergence Properties of an Augmented Lagrangian Algorithm for Optimization with a Combination of General Equality and Linear Constraints. | We consider the global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems. In these methods, linear and more general constraints are handled in different ways. The general constraints are combined with the objective function in an augmented Lagrangia... | Introduction
. Introduction
In this paper, we consider the problem of calculating a local minimizer of the
smooth function
where x is required to satisfy the general equality constraints
and the linear inequality constraints
Here f and c i map ! n into !, A is a p-by-n matrix and b 2 ! p .
A classical technique for sol... | constrained optimization;augmented Lagrangian methods;convergence theory;linear constraints |
588924 | The Effective Energy Transformation Scheme as a Special Continuation Approach to Global Optimization with Application to Molecular Conformation. | This paper discusses a generalization of the function transformation scheme used in Coleman, Shalloway, and Wu [Comput. Optim. Appl., 2 (1993), pp. 145--170; J. Global Optim., 4 (1994), pp. 171--185] and Shalloway [Global Optimization, C. Floudas and P. Pardalos, eds., Princeton University Press, 1992, pp. 433--477; Gl... | Introduction
We are interested in solving the global minimization problem for molecular
conformation, especially protein folding.
How protein folds is one of the key biophysical problems of the decade.
Protein folding is fundamental for almost all theoretical studies of proteins
and protein-related life processes. It h... | integral transformation;global/local minimization;continuation methods;molecular conformation |
588931 | An Unconstrained Convex Programming Approach to Linear Semi-Infinite Programming. | In this paper, an unconstrained convex programming dual approach for solving a class of linear semi-infinite programming problems is proposed. Both primal and dual convergence results are established under some basic assumptions. Numerical examples are also included to illustrate this approach. | Introduction
. Many linear semi-infinite programming problems including
the L1 and Chebychev approximation problems [14, 15] appear in the following "dual
Program (D)
is a compact set in R n , a(t)
a are continuous functions defined on T .
A corresponding "primal form" linear semi-infinite programming problem can be
re... | convex programming;entropy optimization;linear programming;semi-infinite programming |
588960 | A Potential Reduction Newton Method for Constrained Equations. | Extending our previous work [T. Wang, R. D. C. Monteiro, and J.-S. Pang, Math. Programming, 74 (1996), pp. 159--195], this paper presents a general potential reduction Newton method for solving a constrained system of nonlinear equations. A major convergence result for the method is established. Specializations of the ... | Introduction
In the paper [11], we have introduced the problem of solving a system of nonlinear equations
subject to additional constraints on the variables, i.e., a constrained system of equations. We
have demonstrated that constrained equations (CEs) provide a unifying framework for the study
of complementarity probl... | potential function;global convergence;interior point methods;primal-dual methods;complementarity problems;semidefinite programming;potential reduction algorithm;newton method;constrained equation;variational inequality |
588961 | A Practical Algorithm for General Large Scale Nonlinear Optimization Problems. | We provide an effective and efficient implementation of a sequential quadratic programming (SQP) algorithm for the general large scale nonlinear programming problem. In this algorithm the quadratic programming subproblems are solved by an interior point method that can be prematurely halted by a trust region constraint... | Introduction
. In a series of recent papers, [3], [6], and [8], the authors have
developed a new algorithmic approach for solving large, nonlinear, constrained optimization
problems. This proposed procedure is, in essence, a sequential quadratic
programming (SQP) method that uses an interior point algorithm for solving... | interior point;large scale;trust region;nonlinear programming;SQP;merit function |
589001 | A Multiple-Cut Analytic Center Cutting Plane Method for Semidefinite Feasibility Problems. | We consider the problem of finding a point in a nonempty bounded convex body $\Gamma$ in the cone of symmetric positive semidefinite matrices ${\cal S}^m_+$. Assume that $\Gamma$ is defined by a separating oracle, which, for any given $m\ti m$ symmetric matrix $\hat{Y}$, either confirms that $\hat Y \in \Gamma$ or ret... | Introduction
be the set of mm symmetric matrices and let S m
be its subset of symmetric positive
semidenite matrices. We consider the problem of nding a point in a convex subset of
We assume that contains a full-dimensional closed ball with radius > 0: The set
is implicitly dened by a separating oracle, which, for any... | multiple cuts;analytic center;cutting plane methods;semidefinite programming |
589002 | A Superlinearly Convergent Sequential Quadratically Constrained Quadratic Programming Algorithm for Degenerate Nonlinear Programming. | We present an algorithm that achieves superlinear convergence for nonlinear programs satisfying the Mangasarian--Fromovitz constraint qualification and the quadratic growth condition. This convergence result is obtained despite the potential lack of a locally convex augmented Lagrangian. The algorithm solves a successi... | Introduction
. Recently, there has been renewed interest in analyzing and
modifying the algorithms for constrained nonlinear optimization for cases where the
traditional regularity conditions do not hold [5, 12, 11, 20, 24, 23]. This research
has been motivated by the fact that large-scale nonlinear programming problem... | quadratic constraints;sequential quadratic programming;degenerate constraints;superlinear convergence |
589013 | A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm. | A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduction in the amount of computation required to generate a new iterate while the proposed scheme still en... | Introduction
Consider the inequality-constrained nonlinear programming problem
min f(x)
s.t.
are continuously
differentiable. Sequential Quadratic Programming (SQP) algorithms are
widely acknowledged to be among the most successful algorithms available
for solving (P ). For an excellent recent survey of SQP algorithms,... | feasible SQP;feasible iterates;sequential quadratic programming;FSQP;SQP |
589022 | Monotonicity of Fixed Point and Normal Mappings Associated with Variational Inequality and Its Application. | We prove sufficient conditions for the monotonicity and the strong monotonicity of fixed point and normal maps associated with variational inequality problems over a general closed convex set. Sufficient conditions for the strong monotonicity of their perturbed versions are also shown. These results inclu... | Introduction
. Given a continuous function f : R n ! R n and a closed convex
set K in R n ; the well-known finite-dimensional variational inequality, denoted by
f ), is to find an element x 2 K such that
It is well-known that the above problem can be reformulated as nonsmooth equations
such as the fixed point and nor... | iterative algorithm;fixed point and normal maps;strongly monotone maps;variational inequalities;cocoercive maps |
589026 | Second-Order Algorithms for Generalized Finite and Semi-Infinite Min-Max Problems. | We present two second-order algorithms, one for solving a class of finite generalized min-max problems and one for solving semi-infinite generalized min-max problems. Our algorithms make use of optimality functions based on second-order approximations to the cost function and of corresponding search direction function... | Introduction
As is also the case with ordinary min-max problems, generalized min-max problems can
be either finite or semi-infinite. Both are of the form
where
# is a smooth function and # n
# m is a nonsmooth, vector-valued
function. In the case of finite min-max problems, the components of #(-) are of the form 2
wher... | optimality functions;superlinear convergence;consistent approximations;generalized min-max problems;second-order methods |
589032 | A Primal-Dual Method for Large-Scale Image Reconstruction in Emission Tomography. | In emission tomography, images can be reconstructed from a set of measured projections using a maximum likelihood (ML) criterion. In this paper, we present a primal-dual algorithm for large-scale three-dimensional image reconstruction. The primal-dual method is specialized to the ML reconstruction problem. The reconstr... | Introduction
. In this paper we consider the image reconstruction problem
in emission tomography. This problem is encountered in the eld of nuclear medicine,
which is concerned with the study of organ function through radioactively labeled
\tracer" compounds. The quantity of interest in this problem is the spatial conc... | applications of nonlinear programming;parallel computation;tomography;primal-dual methods;estimation;large-scale problems |
589052 | On the Convergence Theory of Trust-Region-Based Algorithms for Equality-Constrained Optimization. | In a recent paper, Dennis, El-Alem, and Maciel proved global convergence to a stationary point for a general trust-region-based algorithm for equality-constrained optimization. This general algorithm is based on appropriate choices of trust-region subproblems and seems particularly suitable for large problems.This pape... | Introduction
. Trust-region algorithms have been proved to be efficient and
robust techniques to solve unconstrained optimization problems. An excellent survey
in this area is Mor'e [22]. Other classical references for convergence results are Carter
[3], Mor'e and Sorensen [23], Powell [26], and Shultz, Schnabel, and B... | equality-constrained optimization;second-order necessary optimality conditions;trust regions;local rate of convergence;hard case;SQP methods |
589060 | Algorithms for Constrained and Weighted Nonlinear Least Squares. | A hybrid algorithm consisting of a Gauss--Newton method and a second-order method for solving constrained and weighted nonlinear least squares problems is developed, analyzed, and tested. One of the advantages of the algorithm is that arbitrarily large weights can be handled and that the weights in the merit function d... | Introduction
. Assume that f : R n continuously differentiable
function and that diagonal matrix with weights
We will discuss the Gauss-Newton method and a second order method for
solving the problem
min
x2R
denotes the 2-norm. For simplicity, and without loss of generality, we
assume that the weights are normalized an... | nonlinear least squares;parameter estimation;optimization;weights |
589066 | On the Convergence of Pattern Search Algorithms. | We introduce an abstract definition of pattern search methods for solving nonlinear unconstrained optimization problems. Our definition unifies an important collection of optimization methods that neither compute nor explicitly approximate derivatives. We exploit our characterization of pattern search methods to establ... | Introduction
. We consider the familiar problem of minimizing a continuously
di#erentiable function f : R n
# R. Direct search methods for this problem
are methods that neither compute nor explicitly approximate derivatives of f . Our
interest is in a particular subset of direct search methods that we will call pattern... | coordinate search;evolutionary operation;pattern search;direct search methods;local variation;multidirectional search;convergence analysis;axial relaxation;unconstrained optimization;globalization strategies;downhill simplex search;alternating variable search |
589078 | Degenerate Nonlinear Programming with a Quadratic Growth Condition. | We show that the quadratic growth condition and the Mangasarian--Fromovitz constraint qualification (MFCQ) imply that local minima of nonlinear programs are isolated stationary points. As a result, when started sufficiently close to such points, an $L_\infty$ exact penalty sequential quadratic programming algorithm wil... | Introduction
Recently, there has been renewed interest in analyzing and modifying sequential
quadratic programming (SQP) algorithms for constrained nonlinear optimization
for cases where the traditional regularity conditions do not hold [14,13,
20,25]. This research has been motivated by the fact that large-scale nonli... | degeneracy;sequential quadratic programming;nonlinear programming;quadratic growth |
589079 | Bounds for Linear Matrix Inequalities. | For iterative sequences that converge to the solution set of a linear matrix inequality, we show that the distance of the iterates to the solution set is at most \( O(\epsilon ^{2^{-d}}) \). The nonnegative integer d is the so-called degree of singularity of the linear matrix inequality, and $\epsilon $ denotes the ... | Introduction
Linear matrix inequalities play an important role in system and control theory,
see the book by Boyd et al. [3]. Recently, considerable progress has been made
in optimization over linear matrix inequalities, i.e. semi-definite programming,
see [1, 6, 8, 9, 16, 19, 18, 23, 25] and the references cited there... | regularized duality;error bounds;linear matrix inequality;semidefinite programming |
589089 | A Feasible BFGS Interior Point Algorithm for Solving Convex Minimization Problems. | We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters $\mu$... | Introduction
. We consider the problem of minimizing a smooth convex function
on a convex set defined by inequality constraints. The problem is written as
# R is the function to minimize and c(x) # 0 means that each component
m) of c must be nonnegative at the solution. To simplify
the presentation and to avoid complic... | convex programming;superlinear convergence;constrained optimization;interior point algorithm;analytic center;BFGS quasi-Newton approximations;primal-dual method;line-search |
589092 | Auction Algorithms for Shortest Hyperpath Problems. | The auction-reduction algorithm is a strongly polynomial version of the auction method for the shortest path problem. In this paper we extend the auction-reduction algorithm to different types of shortest hyperpath problems in directed hypergraphs. The results of preliminary computational experiences show that the auc... | Introduction
The shortest hyperpath problem is the extension to directed hypergraphs [11] of the
classical shortest path problem (SPT) in directed graphs. Though not as pervasive
as SPT, shortest hyperpaths have several relevant applications. In particular,
they are at the core of traffic assignment algorithms for tran... | auction algorithms;shortest paths;directed hypergraphs;hyperpaths |
589094 | Constraint Qualifications for Semi-Infinite Systems of Convex Inequalities. | We introduce and study the Abadie constraint qualification, the weak Pshenichnyi--Levin--Valadier property, and related constraint qualifications for semi-infinite systems of convex inequalities and linear inequalities. Our main results are new characterizations of various constraint qualifications in terms of upper se... | Introduction
Let I) be a family of convex functions, where I is an arbitrary (but
nonempty) index set, and let us consider the system of \convex inequalities"
Throughout this paper we shall consider only the above framework, which is sucient for many
applications. However, let us mention that some of our results and pr... | convex Farkas-Minkowski systems;distance formulas;constraint qualifications;semi-infinite inequality systems |
589096 | Differential Stability of Two-Stage Stochastic Programs. | Two-stage stochastic programs with random right-hand side are considered. Optimal values and solution sets are regarded as mappings of the expected recourse functions and their perturbations, respectively. Conditions are identified implying that these mappings are directionally differentiable and semidifferentiable on ... | Introduction
Two-stage stochastic programming is concerned with problems that require a here-
and-now decision on the basis of given probabilistic information on the random data
without making further observations. The costs to be minimized consist of the direct
costs of the here-and-now (or first stage) decision as we... | semide-rivatives;sensitivity analysis;two-stage stochastic programs;directional derivatives;solution sets |
589099 | Multiple Cuts in the Analytic Center Cutting Plane Method. | We analyze the multiple cut generation scheme in the analytic center cutting plane method. We propose an optimal primal and dual updating direction when the cuts are central. The direction is optimal in the sense that it maximizes the product of the new dual slacks and of the new primal variables within the trust regio... | Introduction
The analytic center cutting plane (ACCPM) algorithm [5, 19] is an efficient
algorithm in practice [2, 4]. The complexity of related algorithms was given in
[1, 13], and subsequently in [6]. Extensions to deep cuts were given in [7] and
to very deep cuts in [8]. The method studied in [8] corresponds to the ... | cutting plane method;self-concordance;primal Newton algorithm;interior-point methods;analytic center;multiple cuts |
589112 | On the Accurate Identification of Active Constraints. | We consider nonlinear programs with inequality constraints, and we focus on the problem of identifying those constraints which will be active at an isolated local solution. The correct identification of active constraints is important from both a theoretical and a practical point of view. Such an identification remove... | Introduction
In this paper we consider the problem of identifying the constraints which are active
at an isolated stationary point -
x of the nonlinear program
where it is assumed that the functions f are at least
continuously differentiable. More specifically, we are interested in the following
question: Given an (x; ... | active constraints;constrained optimization;degeneracy;variational inequalities;identification of active constraints |
589114 | Robust Solutions to Uncertain Semidefinite Programs. | In this paper we consider semidefinite programs (SDPs) whose data depend on some unknown but bounded perturbation parameters. We seek "robust" solutions to such programs, that is, solutions which minimize the (worst-case) objective while satisfying the constraints for every possible value of parameters within the give... | Introduction
. A semidefinite program (SDP) consists of minimizing a linear
objective under a linear matrix inequality (LMI) constraint; precisely,
subject to F
(1)
- {0} and the symmetric matrices F
are given. SDPs are convex optimization problems and can be solved in polynomial
time with, e.g., primal-dual interior-p... | uncertainty;robustness;regularization;semidefinite programming;convex optimization |
589118 | Towards a Practical Volumetric Cutting Plane Method for Convex Programming. | We consider the volumetric cutting plane method for finding a point in a convex set ${\cal C}\subset\Re^n$ that is characterized by a separation oracle. We prove polynomiality of the algorithm with each added cut placed directly through the current point and show that this "central cut" version of the method can be imp... | Introduction
Let C ae ! n be a convex set. Given a point -
separation oracle for C either reports
that -
returns a separating hyperplane a 2 ! n such that a T x ? a T - x for every x 2 C.
The convex feasibility problem is to use such an oracle to find a point in C, or prove that
the volume of C must be less than that o... | convex programming;cutting plane method;volumetric barrier |
589127 | An Interior-Point Approach to Sensitivity Analysis in Degenerate Linear Programs. | We consider an interior-point approach to sensitivity analysis in linear programming developed by the authors. We investigate the quality of the interior-point bounds under degeneracy. In the case of a special type of degeneracy, we show that these bounds have the same nice asymptotic relationship with the optimal part... | Introduction
Sensitivity analysis (or post-optimality analysis) is the study of how the optimal solution
of an optimization problem changes with respect to the changes in the problem
data. The possible presence of errors in the problem data often makes sensitivity
analysis as important as solving the original problem i... | degeneracy;sensitivity analysis;linear programming;interior-point methods |
589159 | On Some Properties of Quadratic Programs with a Convex Quadratic Constraint. | In this paper we consider the problem of minimizing a (possibly nonconvex) quadratic function with a quadratic constraint. We point out some new properties of the problem. In particular, in the first part of the paper, we show that (i) given a KKT point that is not a global minimizer, it is easy to find a "better" fea... | Introduction
In this paper we study the problem of minimizing a general quadratic function q :
subject to an ellipsoidal constraint, that is
where H is a symmetric positive definite n \Theta n matrix and a is a positive scalar.
The interest in this problem initially arose in the context of trust region methods for
solv... | merit function;quadratic function;ell2-norm constraint |
589162 | Superlinear Convergence of a Symmetric Primal-Dual Path Following Algorithm for Semidefinite Programming. | This paper establishes the superlinear convergence of a symmetric primal-dual path following algorithm for semidefinite programming (SDP) under the assumptions that the semidefinite program has a strictly complementary primal-dual optimal solution and that the size of the central path neighborhood tends to zero. The in... | Introduction
Recently, there have been many interior point algorithms developed for semidefinite programming
(SDP), see for example [1, 2, 5, 9, 11, 13, 17]. These algorithms differ in their choices of scaling
matrix, the size of the central path neighborhoods, and stepsize rules, among others. In particular,
the algor... | path following;central path;superlinear convergence;semidefinite programming |
589163 | The Sequential Knapsack Polytope. | In this paper we describe the convex hull of all solutions of the integer bounded knapsack problem in the special case when the weights of the items are divisible. The corresponding inequalities are defined via an inductive scheme that can also be used in a more general setting. | Introduction
In this paper we deal with the integer bounded knapsack problem
a
and the numbers
a i are divisible, i.e., a i
a
n. In this case we say that the
knapsack problem has the divisibility property. It is also called the sequential
knapsack problem (see [1]). Whenever we are given a knapsack problem having
the d... | integer programming;linear programming formulation;knapsack problem;knapsack polytope;separation |
589172 | Modified Cholesky Factorizations in Interior-Point Algorithms for Linear Programming. | We investigate a modified Cholesky algorithm typical of those used in most interior-point codes for linear programming. Cholesky-based interior-point codes are popular for three reasons: their implementation requires only minimal changes to standard sparse Cholesky algorithms (allowing us to take full advantage of soft... | Introduction
. Most interior-point codes for linear programming share a common
feature: their major computational operation-solution of a large linear system
of equations-is performed by a direct sparse Cholesky algorithm. In this algorithm,
row and column orderings are determined a priori by well-known heuristics (min... | error analysis;interior-point algorithms and software;cholesky factorization;matrix perturbations |
589182 | Two-Step Algorithms for Nonlinear Optimization with Structured Applications. | In this paper we propose extensions to trust-region algorithms in which the classical step is augmented with a second step that we insist yields a decrease in the value of the objective function. The classical convergence theory for trust-region algorithms is adapted to this class of two-step algorithms. The algorithm... | Introduction
In nonlinear optimization problems with expensive function and gradient evaluations, it is desirable
to extract as much improvement as possible at each iteration of an algorithm. When the objective
function contains a subset of variables that occurs in a predictable functional form, a second,
computational... | spacer steps;expensive function evaluations;LANCELOT;circuit optimization;trust regions;two-step algorithms;minimax problems;line searches;slack variables |
589186 | A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on some of the Variables. | We propose a new algorithm, a reflective Newton method, for the minimization of a quadratic function of many variables subject to upper and lower bounds on some of the variables. The method applies to a general (indefinite) quadratic function for which a local minimizer subject to bounds is required and is particularl... | Introduction
. In this paper we propose a new algorithm for solving the box-
constrained quadratic programming problem
The matrix H is symmetric and, in general, indefinite; l
We denote the feasible region and the strict
ug. When H is indefinite we are interested in locating
a local minimizer.
Problem (1.1) arises as a... | interior-point method;interior Newton method;quadratic programming |
589192 | Convergence Properties of Minimization Algorithms for Convex Constraints Using a Structured Trust Region. | In this paper, we present a class of trust region algorithms for minimization problems within convex feasible regions in which the structure of the problem is explicitly used in the definition of the trust region. This development is intended to reflect the possibility that some parts of the problem may be more accur... | Introduction
Trust region algorithms have enjoyed a long and successful history as tools for the solution of non-
linear, nonconvex, optimization problems. They have been studied and applied to unconstrained
problems (see [7], [17], [25], [28], [29], [30], [31], [34], [35], [38]) and to problems involving various
class... | partial separability;trust region methods;large-scale optimization;convex constraints;structured problems |
589230 | Tensor Methods for Large, Sparse Unconstrained Optimization. | Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optim., 1 (1991), pp. 293--315], who described these methods for small- to moderate-sized problems. The major contribution of this paper is the extension of these methods to large, sparse unconstrained optimization problem... | Introduction
In this paper we describe tensor methods for solving the unconstrained optimization problem
where D is some open set containing x . We assume that f is at least twice continuously
differentiable, and n is large.
Tensor methods for unconstrained optimization are general purpose methods primarily intended
... | large-scale optimization;tensor methods;unconstrained optimization;sparse problems;singular problems |
589234 | A Superlinearly Convergent Primal-Dual Infeasible-Interior-Point Algorithm for Semidefinite Programming. | A primal-dual infeasible-interior-point path-following algorithm is proposed for solving semidefinite programming (SDP) problems. If the problem has a solution, then the algorithm is globally convergent. If the starting point is feasible or close to being feasible, the algorithm finds an optimal solution in at most $O(... | Introduction
In this paper we consider the semidefinite programming (SDP) problem:
and its associated dual problem:
are given data, and
are the primal and dual variables, respectively. By G ffl H we
denote the trace of (G T H). Without loss of generality, we assume that the matrices C and
are symmetric (otherwise, repl... | path-following;superlinear convergence;infeasible-interior-point algorithm;polynomiality;semidefinite programming |
589237 | BFGS with Update Skipping and Varying Memory. | We give conditions under which limited-memory quasi-Newton methods with exact line searches will terminate in n steps when minimizing n-dimensional quadratic functions. We show that although all Broyden family methods terminate in n steps in their full-memory versions, only BFGS does so with limited-memory. Additional... | Introduction
. The quasi-Newton family of algorithms remains a standard
workhorse for minimization. Many of these methods share the properties of finite
termination on strictly convex quadratic functions, a linear or superlinear rate of
convergence on general convex functions, and no need to store or evaluate the secon... | update skipping;limited-memory;broyden family;BFGS;minimization;quasi-Newton |
589240 | Interior Point Trajectories in Semidefinite Programming. | In this paper we study interior point trajectories in semidefinite programming (SDP) including the central path of an SDP. This work was inspired by the seminal work of Megiddo on linear programming trajectories [ Progress in Math. Programming: Interior-Point Algorithms and Related Methods, N. Megiddo, ed., Springer-V... | Introduction
The purpose of this paper is to study properties of the trajectories associated
with interior point methods for semidefinite programming (SDP) prob-
lems. Since many aspects of semidefinite programming find close analogs
in linear programming, several interior point methods designed for linear
programming ... | interior point methods;central path;semidefinite programming |
589254 | Optimality Conditions for Optimization Problems with Complementarity Constraints. | Optimization problems with complementarity constraints are closely related to optimization problems with variational inequality constraints and bilevel programming problems. In this paper, under mild constraint qualifications, we derive some necessary and sufficient optimality conditions involving the proximal coderiva... | Introduction
. The main purpose of this paper is to derive necessary and
su#cient optimality conditions for the optimization problem with complementarity
constraints (OPCC) defined as follows:
y, u)
s.t. #u, #(x, y, y, u) # 0
y, y, u) # 0, (x, y, u)
and# is a nonempty subset of R n+m+q .
(OPCC) is an optimization probl... | optimality conditions;optimization problems;bilevel programming problems;complementarity constraints;proximal normal cones |
589256 | Solving the Trust-Region Subproblem using the Lanczos Method. | The approximate minimization of a quadratic function within an ellipsoidal trust region is an important subproblem for many nonlinear programming methods. When the number of variables is large, the most widely used strategy is to trace the path of conjugate gradient iterates either to convergence or until it reaches th... | Introduction
Trust-region methods for unconstrained minimization are blessed with both strong theoretical
convergence properties and a good reputation in practice. The main computational step in these
methods is to find an approximate minimizer of some model of the true objective function within
a "trust" region for wh... | trust-region subproblem;conjugate gradients;preconditioning;lanczos method |
589272 | Superlinearly Convergent Algorithms for Solving Singular Equations and Smooth Reformulations of Complementarity Problems. | We propose a new algorithm for solving smooth nonlinear equations in the case where their solutions can be singular. Compared to other techniques for computing singular solutions, a distinctive feature of our approach is that we do not employ second derivatives of the equation mapping in the algorithm and we do not ass... | Introduction
. In this paper we are interested in solving nonlinear equations
in the case where their solutions can be singular, and smoothness requirements are
weaker than those usually assumed in this context. Our development is partially
motivated by the nonlinear complementarity problem, which we consider in detail... | singularity;superlinear convergence;complementarity;reformulation;nonlinear equations;regularity |
589276 | A Polynomial Time Algorithm for Shaped Partition Problems. | We consider the class of shaped partition problems of partitioning n given vectors in d-dimensional criteria space into p parts so as to maximize an arbitrary objective function which is convex on the sum of vectors in each part, subject to arbitrary constraints on the number of elements in each part. This class has br... | Introduction
The Partition Problem concerns the partitioning of vectors A in d-space into p parts
so as to maximize an objective function which is convex on the sum of vectors in each part;
see [3]. Each vector A i represents d numerical attributes associated with the ith element
of the set ng to be partitioned. Each o... | polynomial time;optimization;polytope;convex;programming;enumeration;partition;separation;cluster |
589281 | Cut Size Statistics of Graph Bisection Heuristics. | We investigate the statistical properties of cut sizes generated by heuristic algorithms which solve the graph bisection problem approximately. On an ensemble of sparse random graphs, we find empirically that the distribution of the cut sizes found by "local" algorithms becomes peaked as the number of vertices in the g... | Introduction
. Algorithms for tackling combinatorial optimization problems [27] may be divided
into two classes. Exact algorithms such as exhaustive search, branch-and-bound, or branch-and-cut,
form the first class; they determine (exactly) the optimum of the cost function which is to be minimized.
However, for NP-hard... | graph partitioning;heuristics;ranking;self-averaging |
589287 | On the Local Convergence of a Predictor-Corrector Method for Semidefinite Programming. | We study the local convergence of a predictor-corrector algorithm for semidefinite programming problems based on the Monteiro--Zhang unified direction whose polynomial convergence was recently established by Monteiro. Under strict complementarity and nondegeneracy assumptions superlinear convergence with Q-order 1.5 is... | Introduction
The study of superlinear convergence of interior-point methods for linear programming (LP)
was initiated in the early 90s in an effort to explain the fact that interior point methods
tend to perform significantly better in practice than indicated by the polynomial complexity
bounds. This discrepancy is due... | interior point method;superlinear convergence;semidefinite programming |
589294 | A Spectral Bundle Method for Semidefinite Programming. | A central drawback of primal-dual interior point methods for semidefinite programs is their lack of ability to exploit problem structure in cost and coefficient matrices. This restricts applicability to problems of small dimension. Typically, semidefinite relaxations arising in combinatorial applications have sparse an... | Introduction
. The development of interior point methods for semidefinite
programming [19, 31, 1, 46] has increased interest in semidefinite modeling techniques
in several fields such as control theory, eigenvalue optimization, and combinatorial
optimization. In fact, interior point methods proved to be very useful and... | proximal bundle method;eigenvalue optimization;semidefinite programming;convex optimization;large-scale problems |
589295 | Cones of Matrices and Successive Convex Relaxations of Nonconvex Sets. | Let F be a compact subset of the n-dimensional Euclidean space Rn represented by (finitely or infinitely many) quadratic inequalities. We propose two methods, one based on successive semidefinite programming (SDP) relaxations and the other on successive linear programming (LP) relaxations. Each of our methods generates... | Introduction
. Consider a maximization problem with a linear objective function
c T x:
maximize c T x subject to x # F,
where c denotes a constant vector in the n-dimensional Euclidean space R n and
F a subset of R n . We can reduce a more general maximization problem with a
nonlinear objective function f(x) to a maxim... | nonconvex quadratic optimization problem;global optimization;semi-infinite programming;linear matrix inequality;bilinear matrix inequality;semidefinite programming |
589300 | Global Error Bounds for Convex Conic Problems. | This paper aims at deriving and proving some Lipschitzian-type error bounds for convex conic problems in a simple way. First, it is shown that if the recession directions satisfy Slater's condition, then a global Lipschitzian-type error bound holds. Alternatively, if the feasible region is bounded, then the ordinary Sl... | Introduction
In optimization theory it is often desirable to measure the distance to the solution set from a certain
given point. In general, this distance can be difficult to assess, since one may not have a complete
knowledge about the solution set. However, if the form of the solution set is explicitly given, then
i... | convex conic problems;error bound;condition number |
589314 | Minimizing a Quadratic Over a Sphere. | A new method, the sequential subspace method (SSM), is developed for the problem of minimizing a quadratic over a sphere. In our scheme, the quadratic is minimized over a subspace which is adjusted in successive iterations to ensure convergence to an optimum. When a sequential quadratic programming iterate is included ... | Introduction
In this paper we consider the problem of minimizing a quadratic over a sphere:
subject to kxk
where A is a symmetric n n matrix, b 2 R n , T denotes transpose, and k k
is the Euclidean norm. This minimization problem is often called the trust region
subproblem since it must be solved in each step of a tr... | symmetric successive overrelaxation;sparse optimization;arnoldi m orthogonalization;gauss-seidel;minimal residual;large-scale optimization;trust region subproblem;quadratic programming;krylov space;preconditioning;quadratic optimization |
589318 | Rescaling and Stepsize Selection in Proximal Methods Using Separable Generalized Distances. | This paper presents a convergence proof technique for a broad class of proximal algorithms in which the perturbation term is separable and may contain barriers enforcing interval constraints. There are two key ingredients in the analysis: a mild regularity condition on the differential behavior of the barrier as one a... | Introduction
denote the possibly unbounded n-dimensional "box" ([a
This paper considers two closely-related
problems, the minimization problem
min f(x)
(1)
is a closed proper convex function, and the variational inequality
where T is a (possibly set-valued) maximal monotone operator, and NB (x) denotes the cone
of vect... | convex programming;varphi-divergence;proximal algorithms;variational inequalities;bregman distances |
589323 | On the Complexity of a Practical Interior-Point Method. | The theory of self-concordance in convex optimization has been used to analyze the complexity of interior-point methods based on Newton's method. For large problems, it may be impractical to use Newton's method; here we analyze a truncated-Newton method, in which an approximation to the Newton search direction is used.... | Introduction
. In their 1993 book [16], Nesterov and Nemirovsky derive complexity
results for convex optimization problems. Their basic algorithm is an interior-point
method where each subproblem is solved using a damped Newton method. If a
nonlinear optimization problem is large (and hence complexity is an important i... | convex programming;interior-point method;truncated-Newton method;self-concordance;large-scale optimization;complexity |
589351 | An Unsymmetrized Multifrontal LU Factorization. | A well-known approach to computing the LU factorization of a general unsymmetric matrix A is to build the elimination tree associated with the pattern of the symmetric matrix A + AT and use it as a computational graph to drive the numerical factorization. This approach, although very efficient on a large range of unsym... | Introduction
We consider the direct solution of sparse linear equations based on a multifrontal approach.
The systems are of the form A is an n n unsymmetric sparse matrix. The
multifrontal method has been developed by Du and Reid [11, 12] for computing the solution
of indenite sparse symmetric linear equations using ... | elimination tree;gaussian elimination;unsymmetric matrices;multifrontal methods;sparse linear equations |
589352 | Improved Symbolic and Numerical Factorization Algorithms for Unsymmetric Sparse Matrices. | We present algorithms for the symbolic and numerical factorization phases in the direct solution of sparse unsymmetric systems of linear equations. We have modified a classical symbolic factorization algorithm for unsymmetric matrices to inexpensively compute minimal elimination structures. We give an efficient algor... | Introduction
. Typical
dire ct
solve rs for
ge ne ral
sparse syste ms of
line ar
e quations of
the form
have four distinct
phase s: analysis, comprising orde
ring for fill-in
re duction and symbolic factorization;
nume rical factorization of
the sparse coe #cie nt matrix A into triangular factors L and U using
Gaussian... | sparse matrix factorization;parallel sparse solvers;multifrontal methods |
589360 | Numerical Approximation of an SQP-Type Method for Parameter | This paper deals with the numerical approximation of the Levenberg--Marquardt SQP (LMSQP) method for parameter identification problems, which has been presented and analyzed in [M. Burger and W. Mhlhuber, Inverse Problems, pp. 943--969]. It is shown that a Galerkin-type discretization leads to a convergent approximatio... | Introduction
Parameter identication denotes the procedure of determining unknown parameters appearing
in an underlying state equation (usually a partial dierential equation), from indirect
measurements related to the solution of this equation. Such problems frequently appear in
many applications, where mathematical mod... | parameter identification;iterative regularization;galerkin methods;sequential quadratic programming;indefinite systems |
589719 | Mappings for conflict-free access of paths in bidimensional arrays, circular lists, and complete trees. | Since the divergence between the processor speed and the memory access rate is progressively increasing, an efficient partition of the main memory into multibanks is useful to improve the overall system performance. The effectiveness of the multibank partition can be degraded by memory conflicts, that occur when there ... | Introduction
In recent years, the traditional divergence between the processor speed and the memory access rate is
progressively increasing. Thus, an efficient organization of the main memory is important to achieve
high-speed computations. For this purpose, the main memory can be equipped with cache memories
which hav... | bidimensional array;conflict-free access;complete tree;path template;multibank memory system;mapping scheme;frequency assignment;circular list |
589754 | Compiler-optimized simulation of large-scale applications on high performance architectures. | In this paper, we propose and evaluate practical, automatic techniques that exploit compiler analysis to facilitate simulation of very large message-passing systems. We use compiler techniques and a compiler-synthesized static task graph model to identify the subset of the computations whose values have no significant ... | Introduction
Predicting parallel application performance is an essential step in developing large applications on highly scalable
parallel architectures, in sizing the system configurations necessary for large problem sizes, or in analyzing
alternative architectures for such systems. Considerable research is being done... | performance modeling;parallel simulation;parallelizing compilers |
589795 | Local behavior of the Newton method on two equivalent systems from linear programming. | Newton's method is a fundamental technique underlying many numerical methods for solving systems of nonlinear equations and optimization problems. However, it is often not fully appreciated that Newton's method can produce significantly different behavior when applied to equivalent systems, i.e., problems with the same... | Introduction
Newton's method is generally accepted as an effective tool for solving a system of nonlinear
It is a locally and quadratically convergent method
under reasonable assumptions (see e.g. Dennis and Schnabel (Ref. 1)). It is often not fully
appreciated, however, that Newton's method can exhibit significantly d... | equivalent systems;linear programming;newton's method;sphere of convergence |
589926 | The dyadic stream merging algorithm. | We study the stream merging problem for media-on-demand servers. Clients requesting media from the server arrive by a Poisson process, and delivery to the clients starts immediately. Clients are prepared to receive up to two streams at any time, one or both being fed into a buffer cache. We present an on-line algorithm... | INTRODUCTION
At a sequence of random times, clients request content streaming from a
given media server, e.g., videos from a video-on-demand server, with delivery
for each client to begin immediately. To reduce the potentially heavy
tra-c burden created by these media streams, it is clearly desirable to
combine streams... | video-on-demand;average-case analysis;stream merging |
590354 | Symbolic representation of user-defined time granularities. | In the recent literature on time representation, an effort has been made to characterize the notion of time granularity and the relationships between granularities. The main goals are having a common framework for their specification, and allowing the interoperability of systems adopting different time granularities. T... | Introduction
There is a wide agreement in the AI and database
community on the requirement for a data/knowledge
representation system of supporting standard as well
as user-defined time granularities. Examples of standard
time granularities are days, weeks, months,
while user defined granularities may include business-... | time granularity;knowledge representation;temporal reasoning;time representation |
590507 | Probabilistic Default Reasoning with Conditional Constraints. | We present an approach to reasoning from statistical and subjective knowledge, which is based on a combination of probabilistic reasoning from conditional constraints with approaches to default reasoning from conditional knowledge bases. More precisely, we introduce the notions of i>z-, lexicographic, and conditional e... | Introduction
In this paper, we elaborate a combination of probabilistic
reasoning from conditional constraints with approaches to
default reasoning from conditional knowledge bases. As a
main result, this combination provides new notions of entailment
for conditional constraints, which respect the ideas
of classical de... | conditional constraint;system Z;probabilistic default reasoning;conditional entailment;lexicographic entailment |
590522 | Coloration Neighbourhood Search With Forward Checking. | Two contrasting search paradigms for solving combinatorial problems are i>systematic backtracking and i>local search. The former is often effective on highly structured problems because of its ability to exploit consistency techniques, while the latter tends to scale better on very large problems. Neither approach is i... | Introduction
Graph colouring is an NP-hard combinatorial optimisation problem with
real-world applications such as timetabling, scheduling, frequency assign-
ment, computer register allocation, printed circuit board testing and pattern
matching. A graph E) consists of a set V of vertices and a set E
of edges between ve... | forward checking;graph colouring;coloration neighbourhood |
590526 | Approximate Qualitative Temporal Reasoning. | We partition the time-line in different ways, for example, into minutes, hours, days, etc. When reasoning about relations between events and processes we often reason about their location within such partitions. For example, i>x happened yesterday and i>y happened today, consequently i>x and i>y are disjoint. Reasoning... | Introduction
Every temporal object and every spatio-temporal object is
located at a unique region of time bounded by the begin
and the end of its existence. In every moment of time
a spatio-temporal object, o, is exactly located at a single
region, x, of space (Casati & Varzi 1995). This region
is the exact or precise ... | approximate reasoning;ontology;temporal relations;qualitative reasoning;granularity |
590552 | Refreshment policies for web content caches. | Web content caches are often placed between end users and origin servers as a mean to reduce server load, network usage, and ultimately, user-perceived latency. Cached objects typically have associated expiration times, after which they are considered stale and must be validated with a remote server (origin or another ... | INTRODUCTION
Caches are often placed between end-users and origin servers
as a mean to reduce user-perceived latency, server load, and
network usage (see Figure 1). Among these dierent performance
objectives of caches, improving end-user Web experience
is gradually becoming the most pronounced. Many
organizations are d... | validation requests;HTTP;web caching;user-perceived latency;refreshment policies |
590776 | Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF. | Current inductive machine learning algorithms typically use greedy search with limited lookahead. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELIEFF, an extension of RELIEF... | Introduction
Inductive learning algorithms typically use a
greedy search strategy to overcome the combinatorial
explosion during the search for good hy-
potheses. The heuristic function that estimates
the potential successors of the current state in
the search space has a major role in the greedy
search. Current induct... | impurity function;learning from examples;RELIEFF;estimating attributes;empirical evaluation |
590793 | Learning Structure from Data and Its Application to Ozone Prediction. | In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutu... | Introduction
Learning is defined as "any process by which a
system improves its performance" [1]. Since the
first days of research in artificial intelligence, the
ability to learn has been considered as one of the
essential attributes of an "intelligent system", and
a considerable amount of research has been done
in th... | structure learning;bayesian networks;predictive systems;decision trees;atmospheric pollution |
590817 | Evolving Neural Networks to Play Go. | Go is a difficult game for computers to master, and the best go programs are still weaker than the average human player. Since the traditional game playing techniques have proven inadequate, new approaches to computer go need to be studied. This paper presents a new approach to learning to play go. The SANE (Symbiotic,... | Introduction
Go is hard. For computers at least, this is true. Though the game has not received the level
of attention that computer chess, for example, has received, considerable effort has gone
into trying to create strong go playing programs. Yet, despite this effort, the best computer
programs are still relatively ... | neuro-evolution;game playing;sequential decision making;symbiotic evolution |
590834 | Multiple Adaptive Agents for Tactical Driving. | Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However, there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Man... | Introduction
The task of driving can be characterized as consisting
of three levels: strategic, tactical and operational
[13]. At the highest (strategic) level,
a route is planned and goals are determined; at
the intermediate (tactical) level, maneuvers are selected
to achieve short-term objectives - such as
deciding w... | distributed AI;evolutionary algorithms;intelligent vehicles;simulation |
590846 | Incremental Feature Selection. | Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the ex... | Introduction
Feature selection is about finding useful (relevant) features that describe an application
domain. The problem of feature selection can formally be defined as
selecting a minimum set of M relevant features from N original features where
M N such that the probability distribution of different classes given... | dimensionality reduction;pattern recognition;feature selection;machine learning |
590876 | A Neural Network Diagnosis Approach for Analog Circuits. | This paper presents a neural network system for the diagnosis of analog circuits and shows how the performance of such a system can be affected by the choice of different techniques used by its submodules. In particular we discuss the influence of feature extraction techniques such as Fourier Transforms, Wavelets and P... | Introduction
During the past years, the authors have been involved
in several projects on analog circuit diagnosis
and quality control of electrical components.
The aim of this paper is to present the diagnostic
system developed by them and to show how
the performance of such a system can be affected
by the choice of d... | multiple fault diagnosis;analog circuits;neural networks |
590909 | Knowledge Extraction from Transducer Neural Networks. | Previously neural networks have shown interesting performance results for tasks such as classification, but they still suffer from an insufficient focus on the structure of the knowledge represented therein. In this paper, we analyze various knowledge extraction techniques in detail and we develop new transducer extrac... | Introduction
There has been a lot of interest lately in knowledge
structures and their representation in articial
neural networks [Holldobler, 1990, Kurfe, 1991,
Sperduti et al., 1995, Wermter, 1995, Hallam,
1995, Medsker, 1995, Sun, 1995, Wermter et al.,
1996, Elman et al., 1996, Craven, 1996, Wermter,
1999]. Articial... | symbolic interpretation;SRN networks;analysis of connectionist learning;knowledge extraction;neural network learning |
590930 | Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes. | Genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. Under their initial formulation, the search space solutions are coded using the binary ... | INTRODUCTION
.
Genetic algorithms (GAs) are general purpose search algorithms which use principles inspired by
natural genetic populations to evolve solutions for problems ([Goldberg (1989a), Holland (1992)]).
The basic idea is to maintain a population of chromosomes, which represent candidate solutions
for the specifi... | mutation operator;premature convergence;real-coded genetic algorithms |
590937 | Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions. | The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recogni... | INTRODUCTION
Language inference is a classical problem in machine learning, and continues to be an
important and active research topic. The basic problem is, given a set of example behaviours
or strings, automatically infer a corresponding language (grammar, automata,
expression,.) which generates or recognizes those e... | genetic programming;stochastic regular expressions |
590954 | Decision-Theoretic Planning for Autonomous Robotic Surveillance. | In this paper, we introduce a decision-theoretic strategy for surveillance as a first step towards automating the planning of the movement of an autonomous surveillance robot. In our opinion, this particular application is interesting in its own right, but it also provides a test-case for formalisms aimed at dealing bo... | Introduction
In several projects, robots are employed to perform surveillance tasks. See, for example,
[5, 6, 7]. However, in most of these projects, the robots are typically used as a kind
of flexible sensor platform controlled by some external human operator who makes the
high-level decisions on where to go, and how ... | autonomous robots;surveillance;decision-theoretic planning |
590960 | A Reusable Multi-Agent Architecture for Active Intelligent Websites. | In this paper a reusable multi-agent architecture for intelligent Websites is presented and illustrated for an electronic department store. The architecture has been designed and implemented using the compositional design method for multi-agent systems DESIRE. The agents within this architecture are based on a generic ... | Introduction
Most current business Websites are mainly based on navigation across hyperlinks. A closer
analysis of such conventional Websites reveals some of their shortcomings. For example,
customer relationships experts may be disappointed about the unpersonal treatment of
customers at the Website; customers are wand... | information agent;intelligent website |
591005 | Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning. | In this paper we advocate the use of multi-dimensional modal logics as a framework for knowledge representation and, in particular, for representing spatio-temporal information. We construct a two-dimensional logic capable of describing topological relationships that change over time. This logic, called (Propositional ... | Introduction
It is widely accepted that many kinds of AI applications require high-level
reasoning involving spatial and temporal concepts (see e.g. (Hayes,
1979; Hobbs and Moore, 1985; Davis, 1990)). Several approaches have
been applied to representing these concepts: some researchers have developed
specialised comput... | modal logic;multi-dimensional logic;spatio-temporal reasoning |
591458 | Slipping and Tripping Reflexes for Bipedal Robots. | Many robot applications require legged robots to traverse rough or unmodeled terrain. This paper explores strategies that would enable legged robots to respond to two common types of surface contact error: slipping and tripping. Because of the rapid response required and the difficulty of sensing uneven terrain, we pro... | Introduction
R OUGH terrain occurs not only in natural environments
but also in environments that have been constructed
or modified for human use. Currently, most legged
robots lack the control techniques that would allow them
to behave robustly on such relatively simple rough terrain
as stairs, curbs, grass, and slope... | rough terrain;tripping;slipping;reactive control;reflexes;biped locomotion |
591464 | Globally Consistent Range Scan Alignment for Environment Mapping. | A robot exploring an unknown environment may need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is aligned to a cumulative gl... | Introduction
1.1 Problem Definition
The general problem we want to solve is to let a mobile robot explore an unknown environment
using range sensing and build a map of the environment from sensor data. In this paper, we
address the issue of consistent alignment of data frames so that they can be integrated to form
a wo... | range scan alignment;mapping;range scan registration;sensor-based mobile robotics;laser range scanning |
591512 | Sensor-Based Control Architecture for a Car-Like Vehicle. | This paper presents a control architecture endowing a car-like vehicle moving in a dynamic and partially known environment with autonomous motion capabilities. Like most recent control architectures for autonomous robot systems, it combines three functional components: a set of basic real-time skills, a reactive execut... | Introduction
Autonomy in general and motion autonomy in
particular has been a long standing issue in
Robotics. In the late sixties-early seventies,
Shakey (Nilsson, 1984) was one of the first robots
able to move and perform simple tasks au-
tonomously. Ever since, many authors have proposed
control architectures to end... | motion autonomy;control architecture;car-like vehicle |
591532 | A Robotic Excavator for Autonomous Truck Loading. | Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focussed on selected parts of the problem. In this paper, we present... | Introduction
The surface mining of metals, quarrying of rock, and construction
of highways require the rapid removal and handling
of massive quantities of soil, ore, and rock. Typically,
explosive or mechanical techniques are used to pulverize
the material, and digging machines such as excavators load
the material into... | laser rangefinder;manipulator;dig planning;autonomous excavation;software architecture;robotic excavator;integrated robotic system |
591811 | Grounded Symbolic Communication between Heterogeneous Cooperating Robots. | In this paper, we describe the implementation of a heterogeneous cooperative multi-robot system that was designed with a goal of engineering a grounded symbolic representation in a bottom-up fashion. The system comprises two autonomous mobile robots that perform cooperative cleaning. Experiments demonstrate successful ... | Introduction
The Behavior-based approach to robotics has proven that
it is possible to build systems that can achieve tasks
robustly, react in real-time and operate reliably. The
sophistication of applications implemented ranges from
simple reactivity to tasks involving topological map
building and navigation. Converse... | learning;cooperative robotics;heterogeneous systems;symbolic communication;representation;mobile robots;symbol grounding;behavior-based |
591902 | Theory of Mind for a Humanoid Robot. | If we are to build human-like robots that can interact naturally with people, our robots must know not only about the properties of objects but also the properties of animate agents in the world. One of the fundamental social skills for humans is the attribution of beliefs, goals, and desires to other people. This set ... | Introduction
Human social dynamics rely upon the ability to correctly attribute beliefs, goals, and percepts to other people.
This set of metarepresentational abilities, which have been collectively called a "theory of mind" or the ability to
"mentalize", allows us to understand the actions and expressions of others wi... | social interaction;humanoid robots;visual perception |
591962 | Searching for a Global Search Algorithm. | We report on a case study to assess the use of an advanced knowledge-based software design technique with programmers who have not participated in the techniques development. We use the KIDS approach to algorithm design to construct two global search algorithms that route baggage through a transportation net. Construct... | Introduction
Advanced techniques to support software construction
will only be widely accepted by practitioners if
they can be successfully used by software engineers
who were not involved in their development and did
not get on-site training by their inventors. Experience
has to be gained how knowledge-based methods c... | program synthesis;KIDS;scheduling;formal methods |
592010 | Extending Design Environments to Software Architecture Design. | Designing a complex software system is a cognitively challenging task; thus, designers need cognitive support to create good designs. Domain-oriented design environments are cooperative problem-solving systems that support designers in complex design tasks. In this paper we present the architecture and facilities of Ar... | Figure
1.Design environment facilities of Janus, adapted from Fischer, 1994 .
nizational guidelines, and the opinions of fellow project stakeholders and domain
experts.
Design environments suchasFramer Lemke and Fischer, 1990 , Janus Fischer,
1994 , Hydra Fischer et al., 1993 , and VDDE Voice Dialog Design Environment... | human-computer interaction;domain-oriented design environments;evolutionary design;software architecture;human cognitive skills |
592026 | Efficient Specification-Based Component Retrieval. | In this paper we present a mechanism for making specification-based component retrieval more efficient by limiting the amount of theorem proving required at query time. This is done by using a classification scheme to reduce the number of specification matching proofs that are required to process a query. Components ar... | Introduction
The concept of component reuse is fundamental to all engineering disciplines. Components provide levels of
abstraction used to effectively construct increasingly complex systems. Software Engineering is no exception,
where a main focus has been providing languages and methodologies to help software designe... | component retrieval;formal specification;software reuse |
592038 | Efficient Implementations of Software Architectures via Partial Evaluation. | The notion of flexibility (that is, the ability to adapt to changing requirements or execution contexts) is recognized as a key concern in structuring software, and many architectures have been designed to that effect. However, the corresponding implementations often come with performance and code size overheads. The s... | Introduction
What is partial evaluation?
Partial evaluation is a technique to partially execute a program, when only some
of its input data are available. Consider a program p requiring two inputs, x 1
and x 2 . When specific values d 1 and d 2 are given for the two inputs, we can
run the program, producing a result. W... | interpreters;software architectures;program specialization;genericity;adaptability;selective broadcast;partial evaluation;software layers;extensibility;pattern matching |
592047 | The Model-Composition Problem in User-Interface Generation. | Automated user-interface generation environments have been criticized for their failure to deliver rich and powerful interactive applications. To specify more powerful systems, designers require multiple specialized modeling notations. The model-composition problem is concerned with automatically synthesizing powerful,... | Introduction
Building user interfaces (UIs) is time consuming and costly. In systems with graphical
UIs (GUIs), nearly 50% of source code lines and development time can be
attributed to the UI [14]. GUIs are usually built from a fixed set of modules
composed in regular ways. Hence, GUI construction is a natural target ... | model-based;user interface;multi-paradigm;code generation |
592049 | Explanation-Based Scenario Generation for Reactive System Models. | Reactive systems control many useful and complex real-world devices. Tool-supported specification modeling helps software engineers design such systems correctly. One such tool, a scenario generator, constructs an input event sequence for the spec model that reaches a state satisfying given criteria. It can uncover cou... | Introduction
Reactive systems control many useful and complex
real-world devices, such as telephone switches, air and
space craft, and software agents. Such feature-rich
systems are difficult to design correctly, particularly
when distinct functional features are designed by different
people at different times over the... | scenario generation;validation;reactive system;explanation-based generalization |
592050 | Specification-Based Browsing of Software Component Libraries. | Specification-based retrieval provides exact content-oriented access to component libraries but requires too much deductive power. Specification-based browsing evades this bottleneck by moving any deduction into an off-line indexing phase. In this paper, we show how match relations are used to build an appropriate inde... | Introduction
Large software libraries represent valuable assets but the larger they grow, the
harder it becomes to capitalize them for reuse purposes. The main problems are
to keep the overview over the library and to extract appropriate components.
This requires better library organizations and retrieval algorithms th... | browsing;software reuse;formal specifications;retrieval;software component libraries |
592961 | QoS and Contention-Aware Multi-Resource Reservation. | To provide Quality of Service (QoS) guarantee in distributed services, it is necessary to reserve multiple computing and communication resources for each service session. Meanwhile, techniques have been available for the reservation and enforcement of various types of resources. Therefore, there is a need to create an ... | Introduction
With the advances in resource reservation and scheduling
techniques, it is possible to provide end-to-end Quality
of Service (QoS) guarantees for distributed applications
and services. Various resource reservation and scheduling
frameworks have been proposed for individual system resources
such as CPU [7, ... | resource contention;distributed service;resource reservation |
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