User guide for SQOPT 7: Software for large-scale linear and quadratic programming. SQOPT is a software package for minimizing a convex quadratic function subject to both equality and inequality constraints. SQOPT may also be used for linear programming and for finding a feasible point for a set of linear equalities and inequalities. SQOPT uses a two-phase, active-set, reduced-Hessian method. It is most efficient on problems with relatively few degrees of freedom (for example, if only some of the variables appear in the quadratic term, or the number of active constraints and bounds is nearly as large as the number of variables). However, unlike previous versions of SQOPT, there is no limit on the number of degrees of freedom. SQOPT is primarily intended for large linear and quadratic problems with sparse constraint matrices. A quadratic term 1/2x’Hx in the objective function is represented by a user subroutine that returns the product Hx for a given vector x. SQOPT uses stable numerical methods throughout and includes a reliable basis package (for maintaining sparse LU factors of the basis matrix), a practical anti-degeneracy procedure, scaling, and elastic bounds on any number of constraints and variables. SQOPT is part of the SNOPT package for large-scale nonlinearly constrained optimization. The source code is re-entrant and suitable for any machine with a Fortran 77, 90, or 95 compiler. (The f2c translation can be used with a C compiler.) SQOPT may be called from a driver program in Fortran, C, or Matlab. It can also be used as a stand-alone package, reading data in the MPS format used by commercial mathematical programming systems.

References in zbMATH (referenced in 11 articles )

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  1. Lin, Tianyi; Ma, Shiqian; Zhang, Shuzhong: An extragradient-based alternating direction method for convex minimization (2017)
  2. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  3. Curtis, Frank E.; Han, Zheng; Robinson, Daniel P.: A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization (2015)
  4. Gill, Philip E.; Wong, Elizabeth: Methods for convex and general quadratic programming (2015)
  5. Han, Xiaocong; Zingg, David W.: An adaptive geometry parametrization for aerodynamic shape optimization (2014)
  6. Bondell, Howard D.; Reich, Brian J.: Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR (2008)
  7. Tibshirani, Robert; Wang, Pei: Spatial smoothing and hot spot detection for CGH data using the fused lasso (2008)
  8. Friedman, Jerome; Hastie, Trevor; Höfling, Holger; Tibshirani, Robert: Pathwise coordinate optimization (2007)
  9. Schröder, Andreas: Error controlled adaptive $h$- and $hp$-finite elements methods for contact problems with applications in production engineering. (2006)
  10. Tibshirani, Robert; Saunders, Michael; Rosset, Saharon; Zhu, Ji; Knight, Keith: Sparsity and smoothness via the fused lasso (2005)
  11. Oh, Seyoung; Yun, Jae Heon; Chung, Sei-Young: A quadratic approximation for protein sequence to structure mapping. (2003)