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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 531540 of 1277 papers

TitleStatusHype
A Class of Linear Programs Solvable by Coordinate-Wise Minimization0
Embed and Project: Discrete Sampling with Universal Hashing0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits0
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization0
Efficient Optimization with Higher-Order Ising Machines0
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization0
Efficient Optimization Accelerator Framework for Multistate Ising Problems0
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent0
Efficient learning by implicit exploration in bandit problems with side observations0
Bottleneck potentials in Markov Random Fields0
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