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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 591600 of 1277 papers

TitleStatusHype
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity0
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis0
Initialization Method for Factorization Machine Based on Low-Rank Approximation for Constructing a Corrected Approximate Ising Model0
Hierarchical Clustering: Objective Functions and Algorithms0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization0
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications0
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem0
Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach0
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