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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 10711080 of 1277 papers

TitleStatusHype
Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment ProblemCode0
Differentiable Model Selection for Ensemble LearningCode0
All-to-all reconfigurability with sparse and higher-order Ising machinesCode0
Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation AlgorithmCode0
A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over GraphsCode0
Scalable Robust Kidney ExchangeCode0
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set ProgrammingCode0
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