An active set limited memory BFGS algorithm for bound constrained optimization
نویسندگان
چکیده
منابع مشابه
A LIMITED MEMORY ALGORITHM FOR BOUND CONSTRAINED OPTIMIZATION by
An algorithm for solving large nonlinear optimization problems with simple bounds is de scribed It is based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function It is shown how to take advan tage of the form of the limited memory approximation to implement the algorithm e ciently The results of numerical tests on a set of l...
متن کاملA Limited Memory Algorithm for Bound Constrained Optimization
An algorithm for solving large nonlinear optimization problems with simple bounds is de scribed It is based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function It is shown how to take advan tage of the form of the limited memory approximation to implement the algorithm e ciently The results of numerical tests on a set of l...
متن کاملA Two-Stage Active-Set Algorithm for Bound-Constrained Optimization
In this paper, we describe a two-stage method for solving optimization problems with bound constraints. It combines the active-set estimate described in [1] with a modification of the nonmonotone line search framework recently proposed in [2]. In the first stage, the algorithm exploits a property of the active-set estimate that ensures a significant reduction of the objective function when sett...
متن کاملLimited Memory Bundle Method for Large Bound Constrained Nonsmooth Optimization
1. Abstract Practical optimization problems often involve nonsmooth functions of hundreds or thousands of variables. As a rule, the variables in such large problems are restricted to certain meaningful intervals. In the report [Haarala, Mäkelä, 2006] we have described an efficient adaptive limited memory bundle method for large-scale nonsmooth, possibly nonconvex, bound constrained optimization...
متن کاملA subspace limited memory quasi-Newton algorithm for large-scale nonlinear bound constrained optimization
In this paper we propose a subspace limited memory quasi-Newton method for solving large-scale optimization with simple bounds on the variables. The limited memory quasi-Newton method is used to update the variables with indices outside of the active set, while the projected gradient method is used to update the active variables. The search direction consists of three parts: a subspace quasi-Ne...
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: Applied Mathematical Modelling
سال: 2011
ISSN: 0307-904X
DOI: 10.1016/j.apm.2011.01.036