Best approximation mappings in Hilbert spaces
Heinz H. Bauschke, Hui Ouyang, and Xianfu Wang

TL;DR
This paper extends the concept of best approximation mappings (BAM) from finite-dimensional affine subspaces to general convex sets in Hilbert spaces, establishing their properties, convergence, and connections with other mappings.
Contribution
It generalizes BAM to Hilbert spaces, explores compositions and convex combinations, and links BAM with circumcenter mappings, advancing convergence analysis in convex optimization.
Findings
BAM linearly converges to the nearest fixed point.
Finite composition of BAMs in Hilbert spaces remains a BAM.
Convex combinations of BAMs are also BAMs.
Abstract
The notion of best approximation mapping (BAM) with respect to a closed affine subspace in finite-dimensional space was introduced by Behling, Bello Cruz and Santos to show the linear convergence of the block-wise circumcentered-reflection method. The best approximation mapping possesses two critical properties of the circumcenter mapping for linear convergence. Because the iteration sequence of BAM linearly converges, the BAM is interesting in its own right. In this paper, we naturally extend the definition of BAM from closed affine subspace to nonempty closed convex set and from to general Hilbert space. We discover that the convex set associated with the BAM must be the fixed point set of the BAM. Hence, the iteration sequence generated by a BAM linearly converges to the nearest fixed point of the BAM. Connections between BAMs and other mappings generating…
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Taxonomy
TopicsOptimization and Variational Analysis · Sparse and Compressive Sensing Techniques · Advanced Optimization Algorithms Research
