Implementation of trust region methods in optimization

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

This project presents a new approach to Quasi-Newton methods forunconstrained optimization. Quasi-Newton Methods update at each iteration theexisting Hessian approximation (or its inverse) cheaply by integrating data derivedfrom the previously completed one, which is soon ignored. These methods are basedon the so-called Secant equation. In our project we focus on solving a criticalsubproblem of the Quasi-Newton algorithm that requires determining a proper,suitable step size that takes from the current approximation to the minimum to a new'better' one. The subproblem can either be posed as doing a Line Search along somegenerated search direction in order to determine a minimum along the search vector.Another technique, on which we focus primarily in this work, is to use a Trust Regionmethod that directly computes the step vector without doing a focused Line Search.The subproblem is critical to the numerical success of Q-N methods. We emphasizefeatures of successful implementation to pinpoint assess merits of Trust Regionmethods. Our Numerical Results reveal that Trust Region algorithms seem tomarkedly improve as the dimension of the problem increases, while for small dimensional problems performance of both methods is comparable.

روابط وملفات

التعريف والنوع

رقم الوثيقة

0a7cc512-bc15-4539-b26c-3f36868b1bb2

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2214090_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Mohammed Omar Hajj ","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Issam Moghrabi","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Implementation of trust region methods in optimization

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

methods in optimization

إشراف وإعداد

الإشراف

Issam Moghrabi

الإعداد

Mohammed Omar Hajj

الاقتباسات الببليوغرافية