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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 751–800 of 1277 papers

TitleStatusHype
Multidimensional Assignment Problem for multipartite entity resolution—0
Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization—0
Constrained Machine Learning: The Bagel Framework—0
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation—0
Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model—0
Solving Graph-based Public Goods Games with Tree Search and Imitation LearningCode0
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs—0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks—0
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation—0
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks—0
BiGrad: Differentiating through Bilevel Optimization Programming—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle—0
Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades—0
Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors—0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Sample Selection for Fair and Robust Training—0
Interpretable Decision Trees Through MaxSAT—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly—0
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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