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Found 6 papers, 1 papers with code
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing.
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems.
Local Branching Relaxation Heuristics for Integer Linear Programs
LNS relies on heuristics to select neighborhoods to search in.
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts.
Learning Pseudo-Backdoors for Mixed Integer Programs
In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
MIPaaL: Mixed Integer Program as a Layer
It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.
