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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 11611170 of 1277 papers

TitleStatusHype
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems0
Efficient Algorithms for Adversarial Contextual Learning0
Mapping Tractography Across Subjects0
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models0
Towards Integrated Glance To Restructuring in Combinatorial Optimization0
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization0
Smooth and Strong: MAP Inference with Linear Convergence0
Stochastic Online Greedy Learning with Semi-bandit Feedbacks0
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems0
Submodular Functions: from Discrete to Continous Domains0
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