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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 201210 of 1277 papers

TitleStatusHype
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural NetworksCode0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble LearningCode0
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment ProblemCode0
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set ProgrammingCode0
A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over GraphsCode0
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
A Formal Perspective on Byte-Pair EncodingCode0
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