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Convex Analysis and Monotone Operator Theory in Hilbert Spaces
TLDR
This book provides a largely self-contained account of the main results of convex analysis and optimization in Hilbert space. Expand
On Projection Algorithms for Solving Convex Feasibility Problems
TLDR
Unify, generalize, and review convex feasibility algorithms, a very broad and flexible framework is investigated. Expand
A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications
TLDR
In this paper we introduce a framework which allows to circumvent the intricate question of Lipschitz continuity of gradients by using an elegant and easy to check convexity condition which captures the geometry of the constraints. Expand
The Approximation of Fixed Points of Compositions of Nonexpansive Mappings in Hilbert Space
Determining fixed points of nonexpansive mappings is a frequent problem in mathematics and physical sciences. An algorithm for finding common fixed points of nonexpansive mappings in Hilbert space,Expand
SIAM Journal on Optimization
TLDR
The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. Expand
A Weak-to-Strong Convergence Principle for Fejé-Monotone Methods in Hilbert Spaces
TLDR
We consider a wide class of iterative methods arising in numerical mathematics and optimization that are known to converge only weakly. Expand
On the convergence of von Neumann's alternating projection algorithm for two sets
We give several unifying results, interpretations, and examples regarding the convergence of the von Neumann alternating projection algorithm for two arbitrary closed convex nonempty subsets of aExpand
Bregman Monotone Optimization Algorithms
TLDR
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. Expand
Dykstra's Alternating Projection Algorithm for Two Sets
We analyze Dykstra?s algorithm for two arbitrary closed convex sets in a Hilbert space. Our technique also applies to von Neumann?s algorithm. Various convergence results follow. An example allowsExpand
Strong conical hull intersection property, bounded linear regularity, Jameson’s property (G), and error bounds in convex optimization
TLDR
The strong conical hull intersection property and bounded linear regularity are properties of a collection of finitely many closed convex intersecting sets in Euclidean space. Expand
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