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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 10911100 of 1277 papers

TitleStatusHype
Currency Arbitrage Optimization using Quantum Annealing, QAOA and Constraint Mapping0
Box Facets and Cut Facets of Lifted Multicut Polytopes0
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem0
On a class of data-driven mixed-integer programming problems under uncertainty: a distributionally robust approach0
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization0
D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning0
Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation0
Decision-focused Graph Neural Networks for Combinatorial Optimization0
Bayesian Meta-Prior Learning Using Empirical Bayes0
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