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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 91100 of 1277 papers

TitleStatusHype
Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial OptimizationCode1
Attention, Learn to Solve Routing Problems!Code1
ASP: Learn a Universal Neural Solver!Code1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Quantum approximate optimization via learning-based adaptive optimizationCode1
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
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