{"ID":2824186,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23166","arxiv_id":"2512.23166","title":"A Proximal-Gradient Method for Solving Regularized Optimization Problems with General Constraints","abstract":"We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to compute trial steps and uses a merit function to determine step acceptance or rejection. Under various assumptions, we establish a worst-case iteration complexity result, prove that limit points are first-order KKT points, and show that manifold identification and active-set identification properties hold. Preliminary numerical experiments on a subset of the CUTEst test problems and sparse canonical correlation analysis problems demonstrate the promising performance of our approach.","short_abstract":"We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to compute trial steps and uses a merit function to determine step acceptance or rejection. Under various assumptions, we establish a worst-case it...","url_abs":"https://arxiv.org/abs/2512.23166","url_pdf":"https://arxiv.org/pdf/2512.23166v2","authors":"[\"Frank E. Curtis\",\"Xiaoyi Qu\",\"Daniel P. Robinson\"]","published":"2025-12-29T03:13:48Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}