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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 661670 of 1277 papers

TitleStatusHype
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches0
Provable Non-Convex Optimization and Algorithm Validation via Submodularity0
Pruning Random Forests for Prediction on a Budget0
PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach0
QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks0
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis0
QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems0
Quadratically constrained quadratic programming for classification using particle swarms and applications0
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features0
Quantum Annealing for Single Image Super-Resolution0
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