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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 911920 of 1277 papers

TitleStatusHype
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks0
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem0
A Distribution Evolutionary Algorithm for the Graph Coloring Problem0
A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant Colony Optimization Technique0
A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization0
AED: An Anytime Evolutionary DCOP Algorithm0
A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design0
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms0
A General Large Neighborhood Search Framework for Solving Integer Linear Programs0
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