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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

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Showing 351–360 of 1277 papers

TitleStatusHype
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Differentiable Scaffolding Tree for Molecule Optimization—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
Differentially Private Partial Set Cover with Applications to Facility Location—0
DIFFRAC: a discriminative and flexible framework for clustering—0
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits—0
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture—0
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing—0
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