SOTAVerified

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 401410 of 1277 papers

TitleStatusHype
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
Implementation of digital MemComputing using standard electronic componentsCode0
Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off0
Constraints First: A New MDD-based Model to Generate Sentences Under Constraints0
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks0
Sub-universal variational circuits for combinatorial optimization problemsCode0
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local PolicyCode1
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement LearningCode0
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem0
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Show:102550
← PrevPage 41 of 128Next →

No leaderboard results yet.