Bregman monotone optimization algorithms

被引:223
作者
Bauschke, HH [1 ]
Borwein, JM
Combettes, PL
机构
[1] Univ Guelph, Dept Math & Stat, Guelph, ON N1G 2W1, Canada
[2] Simon Fraser Univ, Ctr Expt & Construct Math, Burnaby, BC V5A 1S6, Canada
[3] Univ Paris 06, Lab Jacques Louis Lions, F-75005 Paris, France
关键词
Banach space; block-iterative method; Bregman distance; Bregman monotone; Bregman projection; B-class operator; convex feasibility problem; essentially smooth function; essentially strict convex function; Fejer monotone; Legendre function; monotone operator; proximal mapping; proximal point algorithm; resolvent; subgradient projection;
D O I
10.1137/S0363012902407120
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A broad class of optimization algorithms based on Bregman distances in Banach spaces is unified around the notion of Bregman monotonicity. A systematic investigation of this notion leads to a simplified analysis of numerous algorithms and to the development of a new class of parallel block-iterative surrogate Bregman projection schemes. Another key contribution is the introduction of a class of operators that is shown to be intrinsically tied to the notion of Bregman monotonicity and to include the operators commonly found in Bregman optimization methods. Special emphasis is placed on the viability of the algorithms and the importance of Legendre functions in this regard. Various applications are discussed.
引用
收藏
页码:596 / 636
页数:41
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