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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 181190 of 1277 papers

TitleStatusHype
Synthesizing Interpretable Control Policies through Large Language Model Guided SearchCode0
Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination0
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs0
Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected AttributesCode1
MG-Net: Learn to Customize QAOA with Circuit Depth AwarenessCode0
A 2-approximation algorithm for the softwired parsimony problem on binary, tree-child phylogenetic networks0
Multi-objective Evolution of Heuristic Using Large Language Model0
Quantum evolutionary algorithm for TSP combinatorial optimisation problem0
Extended Deep Submodular Functions0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
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