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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 251260 of 1277 papers

TitleStatusHype
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms0
Futureproof Static Memory PlanningCode0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning0
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems0
Unsupervised Learning for Quadratic Assignment0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware0
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