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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 361370 of 1277 papers

TitleStatusHype
All-to-all reconfigurability with sparse and higher-order Ising machinesCode0
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach0
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization RegimeCode1
An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making0
RIGA: A Regret-Based Interactive Genetic Algorithm0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems0
Computing with Residue Numbers in High-Dimensional Representation0
Temporal Sequencing of DocumentsCode0
Pointer Networks with Q-Learning for Combinatorial Optimization0
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