Keyword : convex feasibility problem


Multi-Domain Adaptive Learning Based on Feasibility Splitting and Adaptive Projected Subgradient Method
Masahiro YUKAWA Konstantinos SLAVAKIS Isao YAMADA 
Publication:   IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2010/02/01
Vol. E93-A  No. 2 ; pp. 456-466
Type of Manuscript:  PAPER
Category: Digital Signal Processing
Keyword: 
adaptive algorithmconvex projectionprojected gradient methodconvex feasibility problem
 Summary | Full Text:PDF(339.5KB)

A Deep Monotone Approximation Operator Based on the Best Quadratic Lower Bound of Convex Functions
Masao YAMAGISHI Isao YAMADA 
Publication:   IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2008/08/01
Vol. E91-A  No. 8 ; pp. 1858-1866
Type of Manuscript:  Special Section PAPER (Special Section on Signal Processing)
Category: 
Keyword: 
monotone approximationattracting mappingsubgradient projectionconvex feasibility problemadaptive filtering
 Summary | Full Text:PDF(299.3KB)

Convex Feasibility Problem with Prioritized Hard Constraints--Double Layered Projected Gradient Method
Nobuhiko OGURA Isao YAMADA 
Publication:   IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2004/04/01
Vol. E87-A  No. 4 ; pp. 872-878
Type of Manuscript:  PAPER
Category: Numerical Analysis and Optimization
Keyword: 
convex projectionconvex feasibility problemhard constraintnonexpansive mappingfixed point theorem
 Summary | Full Text:PDF(238.1KB)