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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 11411150 of 1277 papers

TitleStatusHype
Learning to Remove Cuts in Integer Linear ProgrammingCode0
Reheated Gradient-based Discrete Sampling for Combinatorial OptimizationCode0
Differentiable Quadratic Optimization For The Maximum Independent Set ProblemCode0
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and ApplicationsCode0
Automated quantum programming via reinforcement learning for combinatorial optimizationCode0
Reinforcement Learning Assisted Recursive QAOACode0
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Attack Graph ObfuscationCode0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Implementing a GPU-based parallel MAX-MIN Ant SystemCode0
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