{"ID":2883792,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08062","arxiv_id":"2508.08062","title":"Anderson Accelerated Primal-Dual Hybrid Gradient for solving LP","abstract":"We present the Anderson Accelerated Primal-Dual Hybrid Gradient (AA-PDHG), a fixed-point-based framework designed to overcome the slow convergence of the standard PDHG method for the solution of linear programming (LP) problems. We establish the global convergence of AA-PDHG under a safeguard condition. In addition, we propose a filtered variant (FAA-PDHG) that applies angle and length filtering to preserve the uniform boundedness of the coefficient matrix, a property crucial for guaranteeing convergence. Numerical results show that both AA-PDHG and FAA-PDHG deliver significant speedups over vanilla PDHG for large-scale LP instances.","short_abstract":"We present the Anderson Accelerated Primal-Dual Hybrid Gradient (AA-PDHG), a fixed-point-based framework designed to overcome the slow convergence of the standard PDHG method for the solution of linear programming (LP) problems. We establish the global convergence of AA-PDHG under a safeguard condition. In addition, we...","url_abs":"https://arxiv.org/abs/2508.08062","url_pdf":"https://arxiv.org/pdf/2508.08062v1","authors":"[\"Yingxin Zhou\",\"Stefano Cipolla\",\"Phan Tu Vuong\"]","published":"2025-08-11T15:06:48Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"math.NA\"]","methods":"[]","has_code":false}