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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 711–720 of 1277 papers

TitleStatusHype
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit SynthesisCode1
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly—0
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Learning-based Memetic Algorithm for Hard-label Textual Attack—0
Robust Correlation Clustering with Asymmetric Noise—0
Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation AlgorithmCode0
Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum AnnealingCode0
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method—0
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