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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 851860 of 1277 papers

TitleStatusHype
Combinatorial optimization and reasoning with graph neural networks0
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning0
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems0
Directed percolation and numerical stability of simulations of digital memcomputing machines0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems0
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems0
Noisy intermediate-scale quantum (NISQ) algorithms0
mRNA Codon Optimization on Quantum Comptuers0
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