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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 411420 of 1277 papers

TitleStatusHype
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks0
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover0
Combinatorial optimization for low bit-width neural networks0
Ant Colony Optimization and Hypergraph Covering Problems0
A Generative Neural Annealer for Black-Box Combinatorial Optimization0
A Compositional Algorithm for the Conflict-Free Electric Vehicle Routing Problem0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms0
An SMT Based Compositional Algorithm to Solve a Conflict-Free Electric Vehicle Routing Problem0
Combinatorial optimization and reasoning with graph neural networks0
An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem0
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