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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 31–40 of 1277 papers

TitleStatusHype
Demand Selection for VRP with Emission QuotaCode0
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking—0
STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization—0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms—0
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial OptimizationCode0
Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs—0
A Quantum-Enhanced Power Flow and Optimal Power Flow based on Combinatorial Reformulation—0
Neural Quantum Digital Twins for Optimizing Quantum Annealing—0
Normalized Cut with Reinforcement Learning in Constrained Action Space—0
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