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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 371380 of 1277 papers

TitleStatusHype
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing0
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm0
Diversity from Human Feedback0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize0
Deep Momentum Uncertainty Hashing0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem0
A Tutorial about Random Neural Networks in Supervised Learning0
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms0
Duality between Feature Selection and Data Clustering0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems0
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