NVIDIA's GPU-accelerated cuOpt engine discovers new solutions for four MIPLIB benchmark problems, outperforming CPU solvers with 22% lower objective gaps. NVIDIA's cuOpt optimization engine has found ...

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...

Abstract: In this paper, we consider linear programming problems with fuzzy objective function coefficients. In this case, the optimal solution set is defined as a fuzzy set. A new method to find the ...

NVIDIA's cuOpt leverages GPU technology to drastically accelerate linear programming, achieving performance up to 5,000 times faster than traditional CPU-based solutions. The landscape of linear ...

Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States Department of Chemical and Biological Engineering, Princeton University, Princeton, ...

Integer linear programming can help find the answer to a variety of real-world problems. Now researchers have found a much faster way to do it. The traveling salesperson problem is one of the oldest ...

ABSTRACT: A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi ...

A simple simplex solver written in Javascript. It can solve linear programs and mixed integer programs using the revised simplex method and branch and bound techniques.