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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 10411050 of 1277 papers

TitleStatusHype
Training Hard-Threshold Networks with Combinatorial Search in a Discrete Target Propagation Setting0
Balanced Crossover Operators in Genetic AlgorithmsCode0
Structural Self-adaptation for Decentralized Pervasive Intelligence0
Bottleneck potentials in Markov Random Fields0
An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing0
Addressing Model Vulnerability to Distributional Shifts over Image Transformation SetsCode0
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem0
Dynamic Learning of Sequential Choice Bandit Problem under Marketing FatigueCode0
Iterated two-phase local search for the Set-Union Knapsack Problem0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems0
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