9 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
PDLP: A Practical First-Order Method for Large-Scale Linear Programming
David Applegate, Mateo Díaz, Oliver Hinder +4
We present PDLP, a practical first-order method for linear programming (LP) designed to solve large-scale LP problems. PDLP is based on the primal-dual hybrid gradient (PDHG) metho…
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
David Applegate, Mateo Díaz, Oliver Hinder +4
We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addit…
Solving Mixed Integer Programs Using Neural Networks
Vinod Nair, Sergey Bartunov, Felix Gimeno +16
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice.…
Operator splitting for a homogeneous embedding of the linear complementarity problem
Brendan O'Donoghue
The linear complementarity problem (LCP) is a general set membership problem that includes quadratic cone programming as a special case. In this work we consider a homogeneous embe…
Hamiltonian descent for composite objectives
Brendan O'Donoghue, Chris J. Maddison
In optimization the duality gap between the primal and the dual problems is a measure of the suboptimality of any primal-dual point. In classical mechanics the equations of motion…
Globally Convergent Type-I Anderson Acceleration for Non-Smooth Fixed-Point Iterations
Junzi Zhang, Brendan O'Donoghue, Stephen Boyd
We consider the application of the type-I Anderson acceleration to solving general non-smooth fixed-point problems. By interleaving with safe-guarding steps, and employing a Powell…