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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 776–800 of 1277 papers

TitleStatusHype
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
Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation AlgorithmCode0
Robust Correlation Clustering with Asymmetric Noise—0
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
Time Complexity Analysis of Evolutionary Algorithms for 2-Hop (1,2)-Minimum Spanning Tree Problem—0
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation—0
Differentiable Scaffolding Tree for Molecule Optimization—0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs—0
Neural Extensions: Training Neural Networks with Set Functions—0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows—0
Generative Adversarial Training for Neural Combinatorial Optimization Models—0
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property—0
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization—0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK—0
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs—0
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention—0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles—0
Preference Conditioned Neural Multi-objective Combinatorial Optimization—0
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems—0
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems—0
Differentiable Scaffolding Tree for Molecular Optimization—0
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits—0
Target Languages (vs. Inductive Biases) for Learning to Act and Plan—0
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