Analysis of unconstrained NMPC schemes with incomplete optimization *

Higher Order Iteration Schemes for Unconstrained Optimization

Using a predictor-corrector tactic, this paper derives new iteration schemes for unconstrained optimization. It yields a point (predictor) by some line search from the current point; then with the two points it constructs a quadratic interpolation curve to approximate some ODE trajectory; it finally determines a new point (corrector) by searching along the quadratic curve. In particular, this p...

Interval Analysis: Unconstrained and Constrained Optimization

INTERVAL ANALYSIS: UNCON-STRAINED AND CONSTRAINED OPTIMIZATION Introduction. Interval algorithms for constrained and un-constrained optimization are based on adaptive, exhaustive search of the domain. Their overall structure is virtually identical to Lipschitz optimization as in [4], since interval evaluations of an objective function φ over an interval vector x correspond to estimation of the ...

Problems of Unconstrained Optimization

In this paper we give an review on convergence problems of un-constrained optimization algorithms, including line search algorithms and trust region algorithms. Recent results on convergence of conjugate gradient methods are discussed. Some well-known convergence problems of variable metric methods and recent eeorts made on these problems are also presented.

Notes on Unconstrained Optimization

A local minimum is a point at which the value of the function is less than or equal to all immediately nearby or surrounding function values. A global minimum realizes the smallest possible value of the function over all feasible inputs. In general, based purely on local knowledge of a function and its behavior, it can be very difficult to distinguish whether you have discovered a local or a gl...

Dealing with singularities in nonlinear unconstrained optimization