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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 891900 of 1277 papers

TitleStatusHype
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation0
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning0
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling0
Accelerating Matroid Optimization through Fast Imprecise Oracles0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms0
A Class of Linear Programs Solvable by Coordinate-Wise Minimization0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles0
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem0
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