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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

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Showing 1151–1175 of 1277 papers

TitleStatusHype
Sample Complexity of Automated Mechanism Design—0
On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems—0
Local Perturb-and-MAP for Structured Prediction—0
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve—0
Random-Key Cuckoo Search for the Travelling Salesman Problem—0
Combinatorial Topic Models using Small-Variance Asymptotics—0
Solving Optimization Problems by the Public Goods Game—0
Reinforcement learning based local search for grouping problems: A case study on graph coloring—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint—0
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems—0
Efficient Algorithms for Adversarial Contextual Learning—0
Mapping Tractography Across Subjects—0
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models—0
Towards Integrated Glance To Restructuring in Combinatorial Optimization—0
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization—0
Smooth and Strong: MAP Inference with Linear Convergence—0
Stochastic Online Greedy Learning with Semi-bandit Feedbacks—0
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems—0
Submodular Functions: from Discrete to Continous Domains—0
Streaming, Distributed Variational Inference for Bayesian Nonparametrics—0
Learning-based Compressive Subsampling—0
Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection—0
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Minimum Weight Perfect Matching via Blossom Belief Propagation—0
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