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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 4150 of 1277 papers

TitleStatusHype
Learning with Local Search MCMC Layers0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings0
Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering0
XX^t Can Be FasterCode0
A Generative Neural Annealer for Black-Box Combinatorial Optimization0
Preference Optimization for Combinatorial Optimization Problems0
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning0
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