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Found 8 papers, 2 papers with code
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences
One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of probability measures.
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
We present a new, stochastic variant of the projective splitting (PS) family of algorithms for monotone inclusion problems.
Single-Forward-Step Projective Splitting: Exploiting Cocoercivity
In the convex optimization context, cocoercivity is equivalent to Lipschitz differentiability.
Projective Splitting with Forward Steps only Requires Continuity
A recent innovation in projective splitting algorithms for monotone operator inclusions has been the development of a procedure using two forward steps instead of the customary proximal steps for operators that are Lipschitz continuous.
Convergence Rates for Projective Splitting
Second, for strongly monotone inclusions, strong convergence is established as well as an ergodic $O(1/\sqrt{k})$ convergence rate for the distance of the iterates to the solution.
Projective Splitting with Forward Steps: Asynchronous and Block-Iterative Operator Splitting
Forward steps can be used for any Lipschitz-continuous operators provided the stepsize is bounded by the inverse of the Lipschitz constant.
Faster Subgradient Methods for Functions with Hölderian Growth
Thirdly we develop a novel "descending stairs" stepsize which obtains this faster convergence rate and also obtains linear convergence for the special case of weakly sharp functions.
Local and Global Convergence of a General Inertial Proximal Splitting Scheme
Our local analysis is applicable to certain recent variants of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), for which we establish active manifold identification and local linear convergence.
