Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge—involving thousands of potential routes through hundreds of city ...

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

John C. Urschel is an assistant professor in the MIT Math Department and a Junior Fellow at the Harvard Society of Fellows. He previously played for the Baltimore Ravens. This interview has been ...

ABSTRACT: This paper deals with linear programming techniques and their application in optimizing lecture rooms in an institution. This linear programming formulated based on the available secondary ...

Abstract: In this work, we extend the simplex algorithm of linear programming for finding a local minimum of a concave quadratic function subject to box constraints. In order to test the performance ...

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 ...

A Comprehensive Linear Programming Solver Program, Incorporating Diverse Algorithms: Graphical Method, Dantzig's Simplex Method, Bland's Simplex Method, Two-Phase Simplex Method, Dual Method, Dual ...

67-year-programming language ranks in the top 10 of the Tiobe index of programming language popularity for two months running. Fortran’s return to the top 10 in Tiobe’s monthly index of language ...