In this paper we consider infinite dimensional optimization problems with equality constraints. The basic underlying algorithm is a reduced SQP method. We give a global convergence proof in Hilbert space and extend this analysis to inexact reduced SQP methods. These methods are useful when discretizing the infinte dimensional problems and solving the resulting large scale discretized optimization problems. The convergence analysis takes into account the Maratos effect which occurs for nonsmooth norms. The inexact reduced SQP method is applied to a discretized parabolic control problem. (orig.)SIGLEAvailable from TIB Hannover: RR 1843(95-20) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium...
AbstractIn this work, an improved SQP method is proposed for solving minimax problems, and a new met...
Most reduced Hessian methods for equality constrained problems use a basis for the null space of th...
This thesis extends the design and the global convergence analysis of a class of trust-region sequen...
In this research we present a trust region algorithm for solving the equality constrained optimizati...
results for a second-derivative SQP method for minimizing the exact ℓ1-merit function for a fixed va...
In [19], we gave global convergence results for a second-derivative SQP method for minimizing the ex...
In NA 08/18, we gave global convergence results for a second-derivative SQP method for minimizing th...
International audienceIn view of solving nonsmooth and nonconvex problems involving complex constrai...
In this paper we present two new classes of SQP secant methods for the equality constrained optimiza...
A mechanism for proving global convergence in SQP--filter methods for nonlinear programming (NLP) is...
AbstractIn this paper, a variant of SQP method for solving inequality constrained optimization is pr...
Inequality constrained optimization, Quasi-Newton method, Reduced Hessian method, Sequential quadrat...
A globally convergent algorithm based on the stabilized sequential quadratic programming (sSQP) meth...
Sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constr...
Summarization: The chapter deals with the parametric linear-convex mathematical programming (MP) pro...
AbstractIn this work, an improved SQP method is proposed for solving minimax problems, and a new met...
Most reduced Hessian methods for equality constrained problems use a basis for the null space of th...
This thesis extends the design and the global convergence analysis of a class of trust-region sequen...
In this research we present a trust region algorithm for solving the equality constrained optimizati...
results for a second-derivative SQP method for minimizing the exact ℓ1-merit function for a fixed va...
In [19], we gave global convergence results for a second-derivative SQP method for minimizing the ex...
In NA 08/18, we gave global convergence results for a second-derivative SQP method for minimizing th...
International audienceIn view of solving nonsmooth and nonconvex problems involving complex constrai...
In this paper we present two new classes of SQP secant methods for the equality constrained optimiza...
A mechanism for proving global convergence in SQP--filter methods for nonlinear programming (NLP) is...
AbstractIn this paper, a variant of SQP method for solving inequality constrained optimization is pr...
Inequality constrained optimization, Quasi-Newton method, Reduced Hessian method, Sequential quadrat...
A globally convergent algorithm based on the stabilized sequential quadratic programming (sSQP) meth...
Sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constr...
Summarization: The chapter deals with the parametric linear-convex mathematical programming (MP) pro...
AbstractIn this work, an improved SQP method is proposed for solving minimax problems, and a new met...
Most reduced Hessian methods for equality constrained problems use a basis for the null space of th...
This thesis extends the design and the global convergence analysis of a class of trust-region sequen...