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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 851–875 of 1277 papers

TitleStatusHype
Combinatorial optimization and reasoning with graph neural networks—0
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Directed percolation and numerical stability of simulations of digital memcomputing machines—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems—0
Noisy intermediate-scale quantum (NISQ) algorithms—0
mRNA Codon Optimization on Quantum Comptuers—0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—0
Learning DNN networks using un-rectifying ReLU with compressed sensing application—0
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach—0
Learning General Policies from Small Examples Without SupervisionCode0
Learning to Make Decisions via Submodular Regularization—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation—0
Neural Knapsack: A Neural Network Based Solver for the Knapsack ProblemCode0
Fixed Priority Global Scheduling from a Deep Learning Perspective—0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize—0
Content Provider Dynamics and Coordination in Recommendation Ecosystems—0
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