Implementing Generating Set Search Methods for Linearly Constrained Minimization
نویسندگان
چکیده
منابع مشابه
Implementing Generating Set Search Methods for Linearly Constrained Minimization
We discuss an implementation of a derivative-free generating set search method for linearly constrained minimization with no assumption of nondegeneracy placed on the constraints. The convergence guarantees for generating set search methods require that the set of search directions possesses certain geometrical properties that allow it to approximate the feasible region near the current iterate...
متن کاملPattern Search Methods for Linearly Constrained Minimization
We extend pattern search methods to linearly constrained minimization. We develop a general class of feasible point pattern search algorithms and prove global convergence to a KarushKuhn-Tucker point. As in the case of unconstrained minimization, pattern search methods for linearly constrained problems accomplish this without explicit recourse to the gradient or the directional derivative of th...
متن کاملStationarity Results for Generating Set Search for Linearly Constrained Optimization
We derive new stationarity results for derivative-free, generating set search methods for linearly constrained optimization. We show that a particular measure of stationarity is of the same order as the step length at an identifiable subset of the iterations. Thus, even in the absence of explicit knowledge of the derivatives of the objective function, we still have information about stationarit...
متن کاملAsynchronous Parallel Generating Set Search for Linearly Constrained Optimization
We describe an asynchronous parallel derivative-free algorithm for linearly constrained optimization. Generating set search (GSS) is the basis of our method. At each iteration, a GSS algorithm computes a set of search directions and corresponding trial points and then evaluates the objective function value at each trial point. Asynchronous versions of the algorithm have been developed in the un...
متن کاملActive Set Identification for Linearly Constrained Minimization Without Explicit Derivatives
We consider active set identification for linearly constrained optimization problems in the absence of explicit information about the derivative of the objective function. We begin by presenting some general results on active set identification that are not tied to any particular algorithm. These general results are sufficiently strong that, given a sequence of iterates converging to a Karush–K...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2007
ISSN: 1064-8275,1095-7197
DOI: 10.1137/050635432