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 871880 of 1277 papers

TitleStatusHype
Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation0
Neural Knapsack: A Neural Network Based Solver for the Knapsack ProblemCode0
Fixed Priority Global Scheduling from a Deep Learning Perspective0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize0
Content Provider Dynamics and Coordination in Recommendation Ecosystems0
Dynamic Submodular Maximization0
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations0
Modular Multi Target Tracking Using LSTM Networks0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization0
A Knowledge Representation Approach to Automated Mathematical Modelling0
Show:102550
← PrevPage 88 of 128Next →

No leaderboard results yet.