Titlebook: Algorithms for Constrained Minimization of Smooth Nonlinear Functions; A. G. Buckley,J.- L. Goffin Book 1982Latest edition Springer-Verlag

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Reduced quasi-Newton methods with feasibility improvement for nonlinearly constrained optimization,imating a restriction of the Hessian of the Lagrangian. Each iterate is obtained by a search along a simple curve defined by a quasi-Newton direction and a feasibility improving direction; an exact penalty function is used to determine the stepsize. The method can be viewed as an efficient approxima

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Computation of the search direction in constrained optimization algorithms,wever, there is considerable variation in the precise nature of the quadratic program to be solved. Furthermore, significant differences exist in the procedures advocated to ensure that the search direction is well defined, and some algorithms abandom the quadratic programming approach for particula

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A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints,le. The algorithm is of the projected Lagrangian type, involving a sequence of sparse, linearly constrained subproblems whose objective functions include a modified Lagrangian term and a modified quadratic penalty function..The algorithm has been implemented in a general purpose FORTRAN program call

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