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28
Sequential Quadratic Programming
, 1995
"... this paper we examine the underlying ideas of the SQP method and the theory that establishes it as a framework from which effective algorithms can ..."
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Cited by 114 (2 self)
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this paper we examine the underlying ideas of the SQP method and the theory that establishes it as a framework from which effective algorithms can
Local convergence of SQP methods for Mathematical Programs with Equilibrium Constraints
, 2002
"... Recently, it has been shown that Nonlinear Programming solvers can successfully solve a range of Mathematical Programs with Equilibrium Constraints (MPECs). ..."
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Cited by 55 (17 self)
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Recently, it has been shown that Nonlinear Programming solvers can successfully solve a range of Mathematical Programs with Equilibrium Constraints (MPECs).
Stabilized Sequential Quadratic Programming
 Computational Optimization and Applications
, 1998
"... . Recently, Wright proposed a stabilized sequential quadratic programming algorithm for inequality constrained optimization. Assuming the MangasarianFromovitz constraint qualification and the existence of a strictly positive multiplier (but possibly dependent constraint gradients), he proved a local ..."
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Cited by 38 (0 self)
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. Recently, Wright proposed a stabilized sequential quadratic programming algorithm for inequality constrained optimization. Assuming the MangasarianFromovitz constraint qualification and the existence of a strictly positive multiplier (but possibly dependent constraint gradients), he proved a local quadratic convergence result. In this paper, we establish quadratic convergence in cases where both strict complementarity and the MangasarianFromovitz constraint qualification do not hold. The constraints on the stabilization parameter are relaxed, and linear convergence is demonstrated when the parameter is kept fixed. We show that the analysis of this method can be carried out using recent results for the stability of variational problems. Key words. Sequential quadratic programming, quadratic convergence, superlinear convergence, degenerate optimization, stabilized SQP, error estimation To appear in Computational Optimization and Applications This paper is dedicated to Olvi L. Manga...
On Combining Feasibility, Descent and Superlinear Convergence in Inequality Constrained Optimization
 Mathematical Programming
, 1993
"... . Extension of quasiNewton techniques from unconstrained to constrained optimization via Sequential Quadratic Programming (SQP) presents several difficulties. Among these are the possible inconsistency, away from the solution, of first order approximations to the constraints, resulting in infeasibi ..."
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Cited by 29 (1 self)
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. Extension of quasiNewton techniques from unconstrained to constrained optimization via Sequential Quadratic Programming (SQP) presents several difficulties. Among these are the possible inconsistency, away from the solution, of first order approximations to the constraints, resulting in infeasibility of the quadratic programs; and the task of selecting a suitable merit function, to induce global convergence. In the case of inequality constrained optimization, both of these difficulties disappear if the algorithm is forced to generate iterates that all satisfy the constraints, and that yield monotonically decreasing objective function values. (Feasibility of the successive iterates is in fact required in many contexts such as in realtime applications or when the objective function is not well defined outside the feasible set). It has been recently shown that this can be achieved while preserving local twostep superlinear convergence. In this note, the essential ingredients for an S...
A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
 SIAM Journal on Optimization
, 2001
"... . A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduction in the amount of computation required to generate a new iterate while the pr ..."
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Cited by 23 (0 self)
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. A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduction in the amount of computation required to generate a new iterate while the proposed scheme still enjoys the same global and fast local convergence properties. A preliminary implementation has been tested and some promising numerical results are reported. Key words. sequential quadratic programming, SQP, feasible iterates, feasible SQP, FSQP AMS subject classifications. 49M37, 65K05, 65K10, 90C30, 90C53 PII. S1052623498344562 1.
Quadratically And Superlinearly Convergent Algorithms For The Solution Of Inequality Constrained Minimization Problems
, 1995
"... . In this paper some Newton and quasiNewton algorithms for the solution of inequality constrained minimization problems are considered. All the algorithms described produce sequences fx k g converging qsuperlinearly to the solution. Furthermore, under mild assumptions, a qquadratic convergence ra ..."
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Cited by 17 (6 self)
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. In this paper some Newton and quasiNewton algorithms for the solution of inequality constrained minimization problems are considered. All the algorithms described produce sequences fx k g converging qsuperlinearly to the solution. Furthermore, under mild assumptions, a qquadratic convergence rate in x is also attained. Other features of these algorithms are that the solution of linear systems of equations only is required at each iteration and that the strict complementarity assumption is never invoked. First the superlinear or quadratic convergence rate of a Newtonlike algorithm is proved. Then, a simpler version of this algorithm is studied and it is shown that it is superlinearly convergent. Finally, quasiNewton versions of the previous algorithms are considered and, provided the sequence defined by the algorithms converges, a characterization of superlinear convergence extending the result of Boggs, Tolle and Wang is given. Key Words. Inequality constrained optimization, New...
SQP methods for largescale nonlinear programming
, 1999
"... We compare and contrast a number of recent sequential quadratic programming (SQP) methods that have been proposed for the solution of largescale nonlinear programming problems. Both linesearch and trustregion approaches are considered, as are the implications of interiorpoint and quadratic progr ..."
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Cited by 9 (0 self)
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We compare and contrast a number of recent sequential quadratic programming (SQP) methods that have been proposed for the solution of largescale nonlinear programming problems. Both linesearch and trustregion approaches are considered, as are the implications of interiorpoint and quadratic programming methods.
INEXACT JOSEPHY–NEWTON FRAMEWORK FOR GENERERALIZED EQUATIONS AND ITS APPLICATIONS TO LOCAL ANALYSIS OF NEWTONIAN METHODS FOR CONSTRAINED OPTIMIZATION ∗
, 2008
"... We propose and analyze a perturbed version of the classical JosephyNewton method for solving generalized equations. This perturbed framework is convenient to treat in a unified way standard sequential quadratic programming, its stabilzed version, sequential quadratically constrained quadratic progr ..."
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Cited by 8 (6 self)
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We propose and analyze a perturbed version of the classical JosephyNewton method for solving generalized equations. This perturbed framework is convenient to treat in a unified way standard sequential quadratic programming, its stabilzed version, sequential quadratically constrained quadratic programming, and linearly constrained Lagrangian methods. For the linearly constrained Lagrangian methods, in particular, we obtain superlinear convergence under the secondorder sufficient optimality condition and the strict Mangasarian–Fromovitz constraint qualification, while previous results in the literature assume (in addition to secondorder sufficiency) the stronger linear independence constraint qualification as well as the strict complementarity condition. For the sequential quadratically constrained quadratic programming methods, we prove primaldual superlinear/quadratic convergence under the same assumptions as above, which also gives a new result.
A Parallel Inexact Newton Method for Stochastic Programs with Recourse
 Oper. Res
, 1996
"... A parallel inexact Newton method with a line search is proposed for twostage quadratic stochastic programs with recourse. A lattice rule is used for the numerical evaluation of multidimensional integrals, and a parallel iterative method is used to solve the quadratic programming subproblems. Althou ..."
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Cited by 6 (5 self)
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A parallel inexact Newton method with a line search is proposed for twostage quadratic stochastic programs with recourse. A lattice rule is used for the numerical evaluation of multidimensional integrals, and a parallel iterative method is used to solve the quadratic programming subproblems. Although the objective only has a locally Lipschitz gradient, global convergence and local superlinear convergence of the method are established. Furthermore, the method provides an error estimate which does not require much extra computation. The performance of the method is illustrated on a CM5 parallel computer. Keywords: Stochastic programming, inexact Newton method, parallel quadratic programming, numerical integration. Short title: Newton's method for stochastic programs This work was supported by the Australian Research Council and the numerical experiments were done on the Sydney Regional Centre for Parallel Computing CM5. 1 Introduction Twostage quadratic stochastic programs with re...
An iterative workingset method for LargeScale NonConvex quadratic programming
, 2001
"... We consider a workingset method for solving largescale quadratic programming problems for which there is no requirement that the objective function be convex. The methods are iterative at two levels, one level relating to the selection of the current working set, and the second due to the method u ..."
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Cited by 6 (1 self)
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We consider a workingset method for solving largescale quadratic programming problems for which there is no requirement that the objective function be convex. The methods are iterative at two levels, one level relating to the selection of the current working set, and the second due to the method used to solve the equalityconstrained problem for this working set. A preconditioned conjugate gradient method is used for this inner iteration, with the preconditioner chosen especially to ensure feasibility of the iterates. The preconditioner is updated at the conclusion of each outer iteration to ensure that this feasibility requirement persists. The wellknown equivalence between the conjugategradient and Lanczos methods is exploited when nding directions of negative curvature. Details of an implementation  the Fortran 90 package QPA in the forthcoming GALAHAD library  are given.