SQPlab

The SQPlab (pronounce S-Q-P-lab) software presented in these pages is a modest Matlab implementation of the SQP algorithm for solving constrained optimization problems. The functions defining the problem can be nonlinear and nonconvex, but must be differentiable. A particular attention will be paid to problems with an optimal control structure. SQP stands for Sequential Quadratic Programming, a method invented in the mid-seventies, which can be viewed as the Newton approach applied to the optimality conditions of the optimization problem. Each iteration of the SQP algorithm requires finding a solution to a quadratic program (QP). This is a simpler optimization problem, which has a quadratic objective and linear constraints. This QP is still difficult to solve however; in particular it is NP-hard when the quadratic objective is nonconvex. On the other hand, as a Newton method, the SQP algorithm converges very rapidly, meaning that it requires few iterations (hence QP solves) to find an approximate solution with a good precision (this is particularly true when second derivatives are used). Therefore, one can say that the SQP algorithm is an appropriate approach when the evaluation of the functions defining the nonlinear optimization problem, and their derivatives, is time consuming. Indeed, in this case, the time spent in finding the solution to the QP’s is counterbalanced by the time spent in evaluating nonlinear functions. Since the functions are evaluated once at each iteration, one can then benefit from the small number of iterations required by the method. If the rule above does not apply, a nonlinear interior point algorithm can do better. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 72 articles , 1 standard article )

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  1. Apkarian, Pierre; Noll, Dominikus; Ravanbod, Laleh: Nonsmooth bundle trust-region algorithm with applications to robust stability (2016)
  2. de Oliveira, Welington; Solodov, Mikhail: A doubly stabilized bundle method for nonsmooth convex optimization (2016)
  3. Fliege, Jörg; Vaz, A.Ismael F.: A method for constrained multiobjective optimization based on SQP techniques (2016)
  4. Griewank, Andreas; Walther, Andrea; Fiege, Sabrina; Bosse, Torsten: On Lipschitz optimization based on gray-box piecewise linearization (2016)
  5. Hojny, Christopher; Pfetsch, Marc E.: A polyhedral investigation of star colorings (2016)
  6. Izmailov, A.F.; Solodov, M.V.; Uskov, E.I.: Globalizing stabilized sequential quadratic programming method by smooth primal-dual exact penalty function (2016)
  7. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)
  8. Izmailov, A.F.; Solodov, M.V.: Newton-type methods: a broader view (2015)
  9. Kolosnitcyn, Anton Vasilevich: Using of modified simplex imbeddings method for solving special class of convex non-differentiable optimization problems (2015)
  10. Strekalovsky, A.S.; Gruzdeva, T.V.; Orlov, A.V.: On the problem polyhedral separability: a numerical solution (2015)
  11. Tahanan, Milad; van Ackooij, Wim; Frangioni, Antonio; Lacalandra, Fabrizio: Large-scale unit commitment under uncertainty (2015)
  12. Wang, Yuting; Garcia, Alfredo: Interactive model-based search for global optimization (2015)
  13. Chen, Zhenhua; An, Kaiqi; Liu, Yuan; Chen, Wenbin: Adjoint method for an inverse problem of CCPF model (2014)
  14. Couckuyt, Ivo; Deschrijver, Dirk; Dhaene, Tom: Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization (2014)
  15. Fercoq, Olivier: Perron vector optimization applied to search engines (2014)
  16. Luna, Juan Pablo; Sagastizábal, Claudia; Solodov, Mikhail: A class of Dantzig-Wolfe type decomposition methods for variational inequality problems (2014)
  17. Pázman, Andrej; Pronzato, Luc: Optimum design accounting for the global nonlinear behavior of the model (2014)
  18. Shen, Chungen; Zhang, Lei-Hong; Wang, Bo; Shao, Wenqiong: Global and local convergence of a nonmonotone SQP method for constrained nonlinear optimization (2014)
  19. van Ackooij, Wim: Decomposition approaches for block-structured chance-constrained programs with application to hydro-thermal unit commitment (2014)
  20. Yu, Yan; Yu, Bo; Dong, Bo: Robust continuation methods for tracing solution curves of parameterized systems (2014)

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