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 113 articles , 1 standard article )

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  1. Izmailov, A. F.; Solodov, M. V.: On attraction of linearly constrained Lagrangian methods and of stabilized and quasi-Newton SQP methods to critical multipliers (2011)
  2. Métivier, Ludovic: Interlocked optimization and fast gradient algorithm for a seismic inverse problem (2011)
  3. Parente, Lisandro A.; Lotito, Pablo A.; Mayorano, Fernando J.; Rubiales, Aldo J.; Solodov, Mikhail V.: The hybrid proximal decomposition method applied to the computation of a Nash equilibrium for hydrothermal electricity markets (2011)
  4. Shams, Ramtin; Sadeghi, Parastoo: On optimization of finite-difference time-domain (FDTD) computation on heterogeneous and GPU clusters (2011)
  5. Sriperumbudur, Bharath K.; Torres, David A.; Lanckriet, Gert R. G.: A majorization-minimization approach to the sparse generalized eigenvalue problem (2011)
  6. Alvarez-Vázquez, Lino J.; Fernández, Francisco J.; Martínez, Aurea: Optimal management of a bioreactor for eutrophicated water treatment: a numerical approach (2010)
  7. Amstutz, Samuel: A penalty method for topology optimization subject to a pointwise state constraint (2010)
  8. Benmansour, F.; Carlier, G.; Peyré, G.; Santambrogio, F.: Derivatives with respect to metrics and applications: subgradient marching algorithm (2010)
  9. Emiel, Grégory; Sagastizábal, Claudia: Incremental-like bundle methods with application to energy planning (2010)
  10. Fernández, D.; Izmailov, A. F.; Solodov, M. V.: Sharp primal superlinear convergence results for some Newtonian methods for constrained optimization (2010)
  11. Izmailov, A. F.; Solodov, M. V.: Inexact Josephy-Newton framework for generalized equations and its applications to local analysis of Newtonian methods for constrained optimization (2010)
  12. Karas, Elizabeth W.; Gonzaga, Clóvis C.; Ribeiro, Ademir A.: Local convergence of filter methods for equality constrained non-linear programming (2010)
  13. Lelièvre, Tony; Rousset, Mathias; Stoltz, Gabriel: Free energy computations. A mathematical perspective (2010)
  14. Noll, Dominikus: Cutting plane oracles to minimize non-smooth non-convex functions (2010)
  15. Belloni, Alexandre; Sagastizábal, Claudia: Dynamic bundle methods (2009)
  16. Benmansour, Fethallah; Carlier, Guillaume; Peyré, Gabriel; Santambrogio, Filippo: Numerical approximation of continuous traffic congestion equilibria (2009)
  17. Daniilidis, Aris; Sagastizábal, Claudia; Solodov, Mikhail: Identifying structure of nonsmooth convex functions by the bundle technique (2009)
  18. Kim, Hyun Keol; Hielscher, Andreas H.: A PDE-constrained SQP algorithm for optical tomography based on the frequency-domain equation of radiative transfer (2009)
  19. Ouria, Ahad; Toufigh, Mohammad M.: Application of Nelder-Mead simplex method for unconfined seepage problems (2009)
  20. Schlenkrich, Sebastian; Walther, Andrea: Global convergence of quasi-Newton methods based on adjoint Broyden updates (2009)