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--- |
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license: mit |
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task_categories: |
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- feature-extraction |
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language: |
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- aa |
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tags: |
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- Optimization |
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- Solver |
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- Tunner |
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pretty_name: BenLOC |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Datasets of ML4MOC |
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Presolved Data is stored in `.\instance`. The folder structure after the datasets are set up looks as follows |
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```bash |
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instances/ |
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MIPLIB/ -> 1065 instances |
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set_cover/ -> 3994 instances |
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independent_set/ -> 1604 instances |
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nn_verification/ -> 3104 instances |
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load_balancing/ -> 2286 instances |
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``` |
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### Dataset Description |
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#### MIPLIB |
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Heterogeneous dataset from [MIPLIB 2017](https://miplib.zib.de/), a well-established benchmark for evaluating MILP solvers. The dataset includes a diverse set of particularly challenging mixed-integer programming (MIP) instances, each known for its computational difficulty. |
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#### Set Covering |
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This dataset consists of instances of the classic Set Covering Problem, which can be found [here](https://github.com/ds4dm/learn2branch/tree/master). Each instance requires finding the minimum number of sets that cover all elements in a universe. The problem is formulated as a MIP problem. |
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#### Maximum Independent Set |
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This dataset addresses the Maximum Independent Set Problem, which can be found [here](https://github.com/ds4dm/learn2branch/tree/master). Each instance is modeled as a MIP, with the objective of maximizing the size of the independent set. |
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#### NN Verification |
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This “Neural Network Verification” dataset is to verify whether a neural network is robust to input perturbations can be posed as a MIP. The MIP formulation is described in the paper [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (Gowal et al., 2018)](https://arxiv.org/abs/1810.12715). Each input on which to verify the network gives rise to a different MIP. |
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#### Load Balancing |
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This dataset is from [NeurIPS 2021 Competition](https://github.com/ds4dm/ml4co-competition). This problem deals with apportioning workloads. The apportionment is required to be robust to any worker’s failure. Each instance problem is modeled as a MILP, using a bin-packing with an apportionment formulation. |
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### Dataset Spliting |
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Each dataset was split into a training set $D_{\text{train}}$ and a testing set $D_{\text{test}}$, following an approximate 80-20 split. Moreover, we split the dataset by time and "optimality", which means according to the proportion of optimality for each parameter is similar in training and testing sets. This ensures a balanced representation of both temporal variations and the highest levels of parameter efficiency in our data partitions. |
