Design and Implementation of a Reduced-Space SQP Solver with Column Reordering for Large-Scale Process Optimization

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-de...

Full description

Saved in:
Bibliographic Details
Published in:Algorithms Vol. 18; no. 11; p. 699
Main Authors: Zhao, Chuanlei, Liu, Ao, Jiang, Aipeng, Zheng, Xiaoqing, Wang, Haokun, Zhao, Rui
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
Algorithms
Approximation
Case studies
Coal-fired power plants
column reordering
Constraints
Decomposition
Design
Efficiency
large-scale nonlinear optimization
Mathematical models
Nonlinear programming
Optimization
Osmosis
Parameter estimation
Quadratic programming
reduced-space SQP
reverse osmosis
Solvers
sparse matrices
Sparsity
Variables
Water quality
China
ISSN:1999-4893, 1999-4893
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1999-4893
1999-4893
DOI:10.3390/a18110699