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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 791800 of 1277 papers

TitleStatusHype
Structural Self-adaptation for Decentralized Pervasive Intelligence0
Structured Q-learning For Antibody Design0
Submodular Functions: from Discrete to Continous Domains0
Submodular Information Selection for Hypothesis Testing with Misclassification Penalties0
Submodularity of a Set Label Disagreement Function0
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning0
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size0
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve0
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems0
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization0
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