Now showing items 1-10 of 54
Aspects of Optimization-Based CADCS.
With the recent dramatic increase in available computing power, numerical optimization has become an attractive tool for the design of complex engineering systems. Yet, generalized use of numerical optimization techniques ...
On the Stability of Polynomials with Uncoupled Perturbations in the Coefficients of Even and Odd Powers.
In this note, we present some results concerning the stability (in Hurwitz' sense) of a family of polynomials with even and odd coefficients subject to uncoupled perturbations. It is shown that the stability of an appropriate ...
A New Formula for the Structured Singular Value.
included In TR 85-2
On Feasibility, Descent and Superlinear Convergence in Inequality Constrained Optimization.
Extension of quasi-Newton techniques from unconstrained to constrained optimization via Sequential Quadratic Programming (SQP) presents several difficulties. Among these are the possible inconsistency, away from the solution, ...
On Robust Stability of Linear State Space Models.
The structured singular value (MU), introduced by Doyle  allows to analyze robust stability and performance of linear systems affected by parametric as well as dynamic uncertainty. While exact computation of MU can be ...
A Measure of Worst-Case HPerformance and of Largest Acceptable Uncertainty
The structured singular value (SSV or ) is know to be an effective tool for assessing robust performance of linear time- invariant models subject to structured uncertainty. Yet all a single analysis provides is a bound ݠon ...
Nonmonotone Line Search for Minimax Problems
It was recently shown that, in the solution of smooth constrained optimization problems by sequential programming (SQP), the Maratos effect can be prevented by means of a certain nonmonotone (more precisely, four-step ...
Linear Fractional Transformations for the Approximation of Various Uncertainty Sets
Recently, it was shown that the structured singular value framework can be extended to the case when information on the phase of the uncertainty is available, and a computable upper bound on the corresponding "phase sensitive ...
A Simple quadratically convergent Interior Point Algorithm for Linear Programming and Convex quadratic Programming
An algorithm for linear programming (LP) and convex quadratic programming (CQP) is proposed, based on an interior point iteration introduced more than ten years ago by J. Herskovits for the solution of nonlinear programming ...
User's Guide for FSQP Version 2.0 A Fortran Code for Solving Optimization Problems, Possibly Minimax, with General Inequality Constraints and Linear Equality Constraints, Generating Feasible Iterates
FSQP 2.0 is a set of Fortran subroutines for the minimization of the maximum of a set of smooth objective functions (possibly a single one) subject to nonlinear smooth inequality constraints, linear inequality and linear ...