DSPDHG extends PDHG and SPDHG with doubly stochastic block updates and proves O(1/K) ergodic convergence for the expected restricted primal-dual gap plus linear convergence for a restarted variant under quadratic growth.
Hpr-lp: An implementation of an hpr method for solving linear programming
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Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.
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On the convergence of doubly stochastic Primal-Dual Hybrid Gradient Method
DSPDHG extends PDHG and SPDHG with doubly stochastic block updates and proves O(1/K) ergodic convergence for the expected restricted primal-dual gap plus linear convergence for a restarted variant under quadratic growth.
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Empirical Asymptotic Runtime Analysis of Linear Programming Algorithms
Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.
- Accessible Complexity Bounds for Restarted PDHG on Linear Programs with a Unique Optimizer