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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 801810 of 1277 papers

TitleStatusHype
Symmetry Breaking and Equivariant Neural Networks0
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization0
Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems0
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls0
Target Languages (vs. Inductive Biases) for Learning to Act and Plan0
Classical Simulation of Variational Quantum Classifiers using Tensor Rings0
Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc0
The backtracking survey propagation algorithm for solving random K-SAT problems0
The Cakewalk Method0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery0
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