Descent methods for convex optimization problems in Banach spaces | Zendy

Mahvish Ali | Zendy

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Descent methods for convex optimization problems in Banach spaces

Author(s) -

Mahvish Ali

Publication year - 2005

Publication title -

international journal of mathematics and mathematical sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.21

H-Index - 39

eISSN - 1687-0425

pISSN - 0161-1712

DOI - 10.1155/ijmms.2005.2347

Subject(s) - mathematics , banach space , convexity , convex optimization , regular polygon , convex function , uniformly convex space , mathematical optimization , descent (aeronautics) , convergence (economics) , class (philosophy) , optimization problem , proximal gradient methods for learning , regularization (linguistics) , subderivative , lp space , pure mathematics , banach manifold , computer science , artificial intelligence , geometry , engineering , economic growth , financial economics , economics , aerospace engineering

We consider optimization problems in Banach spaces, whose cost functions are convex and smooth, but do not possess strengthened convexity properties. We propose a general class of iterative methods, which are based on combining descent and regularization approaches and provide strong convergence of iteration sequences to a solution of the initial problem

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