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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 551–575 of 1277 papers

TitleStatusHype
A new dog learns old tricks: RL finds classic optimization algorithms—0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms—0
Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry—0
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming—0
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method—0
Effective anytime algorithm for multiobjective combinatorial optimization problems—0
Binary matrix factorization on special purpose hardware—0
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems—0
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure—0
Early years of Biased Random-Key Genetic Algorithms: A systematic review—0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules—0
A Distribution Evolutionary Algorithm for the Graph Coloring Problem—0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization—0
Dynamic Submodular Maximization—0
BiGrad: Differentiating through Bilevel Optimization Programming—0
Biased Random-Key Genetic Algorithms: A Review—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
Dynamic Feature Selection for Dependency Parsing—0
Dynamic Assortment Optimization with Changing Contextual Information—0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling—0
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing—0
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem—0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning—0
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching—0
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions—0
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