Displaying similar documents to “On superlinear multiplier update methods for partial augmented Lagrangian techniques.”

On diagonally preconditioning the 2-steps BFGS method with accumulated steps for supra-scale linearly constrained nonlinear programming.

Laureano F. Escudero (1982)

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We present an algorithm for supra-scale linearly constrained nonlinear programming (LNCP) based on the Limited-Storage Quasi-Newton's method. In large-scale programming solving the reduced Newton equation at each iteration can be expensive and may not be justified when far from a local solution; besides, the amount of storage required by the reduced Hessian matrix, and even the computing time for its Quasi-Newton approximation, may be prohibitive. An alternative based on the reduced...

On diagonally-preconditioning the truncated-Newton method for super-scale linearly constrained nonlinear programming.

Laureano F. Escudero (1982)

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We present an algorithm for super-scale linearly constrained nonlinear programming (LCNP) based on Newton's method. In large scale programming solving Newton's equation at each iteration can be expensive and may not be justified when far from a local solution; we briefly review the current existing methodologies, such that by classifying the problems in small-scale, super-scale and supra-scale problems we suggest the methods that, based on our own computational experience, are more suitable...

Lagrange multipliers estimates for constrained minimization.

Laureano F. Escudero (1981)

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We discuss in this work the first-order, second-order and pseudo-second-order estimations of Lagrange multipliers in nonlinear constrained minimization. The paper also justifies estimations and strategies that are used by two nonlinear programming algorithms that are also briefly described.

Zero or near-to-zero Lagrange multipliers in linearly constrained nonlinear programming.

Laureano F. Escudero (1982)

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We discuss in this work the using of Lagrange multipliers estimates in linearly constrained nonlinear programming algorithms and the implication of zero or near-to-zero Lagrange multipliers. Some methods for estimating the tendency of the multipliers are proposed in the context of a given algorithm.