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- W3201998051 abstract "We propose a solution strategy for linear systems arising in interior method optimization, which is suitable for implementation on hardware accelerators such as graphical processing units (GPUs). The current gold standard for solving these systems is the LDL^T factorization. However, LDL^T requires pivoting during factorization, which substantially increases communication cost and degrades performance on GPUs. Our novel approach solves a large indefinite system by solving multiple smaller positive definite systems, using an iterative solve for the Schur complement and an inner direct solve (via Cholesky factorization) within each iteration. Cholesky is stable without pivoting, thereby reducing communication and allowing reuse of the symbolic factorization. We demonstrate the practicality of our approach and show that on large systems it can efficiently utilize GPUs and outperform LDL^T factorization of the full system." @default.
- W3201998051 created "2021-10-11" @default.
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- W3201998051 date "2021-10-07" @default.
- W3201998051 modified "2023-09-27" @default.
- W3201998051 title "A Hybrid Direct-Iterative Method for Solving KKT Linear Systems." @default.
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