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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 11611170 of 1277 papers

TitleStatusHype
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Graph Adversarial Immunization for Certifiable RobustnessCode0
Global Optimal Path-Based Clustering AlgorithmCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Revisiting Robust Model Fitting Using Truncated LossCode0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
Dynamic Programming on a Quantum Annealer: Solving the RBC ModelCode0
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
Dynamic Learning of Sequential Choice Bandit Problem under Marketing FatigueCode0
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