Stationarity Results for Generating Set Search for Linearly Constrained Optimization
نویسندگان
چکیده
منابع مشابه
Stationarity 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 کاملImplementing 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 کاملEvolutionary pattern search algorithms for unconstrained and linearly constrained optimization
Redescribe aconvergence theory forevolutionary pattern search algorithms (EPS.4S) ona broad class of unconstrained and linearlyconstrainedproblems. EPSAS adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAS is inspired by recent analysesof pattern search methods. Our analysis significantly extends the previous co...
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 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 Optimization
سال: 2007
ISSN: 1052-6234,1095-7189
DOI: 10.1137/s1052623403433638