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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 12111220 of 1277 papers

TitleStatusHype
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
Fast Parallel Algorithms for Statistical Subset Selection ProblemsCode0
Temporal Sequencing of DocumentsCode0
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution ShiftsCode0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume ImagesCode0
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin SplittingCode0
Policy-Based Self-Competition for Planning ProblemsCode0
MG-Net: Learn to Customize QAOA with Circuit Depth AwarenessCode0
Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear ConstraintsCode0
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