However, standard FOMs, such as the primal-dual hybrid gradient (PDHG) method, are not yet reliable for LP problems, solving only a small fraction of instances. Google researchers introduce PDLP ...

Learning to Reformulate Linear Programming This repository contains an independent implementation of the paper "Accelerating Linear Programming Solving by Exploiting the Performance Variability via ...

HPR-LP: A GPU Solver for Linear Programming. Contribute to PolyU-IOR/HPR-LP development by creating an account on GitHub.

Linear Programs (LPs) are one of the major building blocks of AI and have championed recent strides in differentiable optimizers for learning systems. While efficient solvers exist for even ...

We use mixed-precision technique, which is used to exploit the high single precision performance of modern processors, to build the first sparse mixed-precision linear programming solver on the Cell ...

It operates according to a divide-and-conquer principle by building a tree-like structure with nodes that represent linear programming (LP) problems. A LP solver commonly used to process the nodes is ...

If your LP problem involves integer variables, you'll need a solver that supports mixed-integer linear programming (MILP) or mixed-integer quadratic programming (MIQP) if the problem is quadratic ...