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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 276–300 of 1277 papers

TitleStatusHype
Boosting Combinatorial Problem Modeling with Machine Learning—0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning—0
A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms—0
Robust Metric Learning by Smooth Optimization—0
Deep Reinforcement Learning for Modelling Protein Complexes—0
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming—0
Binary matrix factorization on special purpose hardware—0
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems—0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules—0
A Distribution Evolutionary Algorithm for the Graph Coloring Problem—0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning—0
BiGrad: Differentiating through Bilevel Optimization Programming—0
Biased Random-Key Genetic Algorithms: A Review—0
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing—0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling—0
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem—0
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching—0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem—0
Bayesian preference elicitation for multiobjective combinatorial optimization—0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem—0
Accelerating Matroid Optimization through Fast Imprecise Oracles—0
Bayesian Optimization for Macro Placement—0
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