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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 1051–1075 of 1277 papers

TitleStatusHype
Balanced Crossover Operators in Genetic AlgorithmsCode0
Structural Self-adaptation for Decentralized Pervasive Intelligence—0
Bottleneck potentials in Markov Random Fields—0
An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing—0
Addressing Model Vulnerability to Distributional Shifts over Image Transformation SetsCode0
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem—0
Dynamic Learning of Sequential Choice Bandit Problem under Marketing FatigueCode0
Iterated two-phase local search for the Set-Union Knapsack Problem—0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems—0
Attack Graph ObfuscationCode0
Fast Parallel Algorithms for Statistical Subset Selection ProblemsCode0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy gamesCode0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application—0
Algorithms Inspired by Nature: A Survey—0
Mixed Uncertainty Sets for Robust Combinatorial Optimization—0
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon—0
Scalable Robust Kidney ExchangeCode0
Dynamic Assortment Optimization with Changing Contextual Information—0
Differentiable Greedy Networks—0
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction—0
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume ImagesCode0
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