{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:53:48Z","timestamp":1762196028025,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&D Program of Zhejiang Province of China","award":["2023C01024"],"award-info":[{"award-number":["2023C01024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Process industries increasingly face large-scale nonlinear programs with high dimensionality and tight constraints. This study reports on the design and implementation of a reduced-space sequential quadratic programming (RSQP) solver for such settings. The solver couples a column-reordering space-decomposition strategy with sparse-matrix storage\/kernels, and is implemented in a modular C++ framework that supports range\/null-space splitting, line search, and convergence checks. We evaluate six small-scale benchmarks with non-convex\/exponential characteristics, a set of variable-dimension tests up to 128 k variables, and an industrial reverse-osmosis (RO) optimization. On small problems, RSQP attains an accuracy comparable to a full-space sequential quadratic programming (SQP) baseline. In variable-dimension tests, the solver shows favorable scaling when moving from 64 k to 128 k variables; under dynamically varying degrees of freedom, the iteration count decreases by about 62% with notable time savings. In the RO case, daily operating cost decreases by 4.98% and 1.46% across two scenarios while satisfying water-quality constraints. These results indicate that consolidating established RSQP components with column reordering and sparse computation yields a practical, scalable solver for large-scale process optimization.<\/jats:p>","DOI":"10.3390\/a18110699","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:21:46Z","timestamp":1762194106000},"page":"699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Design and Implementation of a Reduced-Space SQP Solver with Column Reordering for Large-Scale Process Optimization"],"prefix":"10.3390","volume":"18","author":[{"given":"Chuanlei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Ao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Aipeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3377-1904","authenticated-orcid":false,"given":"Xiaoqing","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Haokun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref_1","first-page":"337","article-title":"The necessary way to realize great leap forward development of process industries","volume":"29","author":"Gui","year":"2015","journal-title":"China Sci. 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[Ph.D. Thesis, Zhejiang University]."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/699\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:33:37Z","timestamp":1762194817000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/699"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,3]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["a18110699"],"URL":"https:\/\/doi.org\/10.3390\/a18110699","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,3]]}}}