Publications associées à A survey and comparison of contemporary algorithms for computing the matrix geometric mean

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Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

A survey and comparison of contemporary algorithms for computing the matrix geometric mean

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Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Faster Algorithms for Integer Programs with Block Structure

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

Asynchronous updates for stochastic gradient descent

Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis

Quantization Design for Distributed Optimization

Wave Pipelining for Majority-based Beyond-CMOS Technologies

Memetic Viability Evolution for Constrained Optimization

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Quantization Design for Distributed Optimization

Wave Pipelining for Majority-based Beyond-CMOS Technologies

Faster Algorithms for Integer Programs with Block Structure

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Asynchronous updates for stochastic gradient descent

Memetic Viability Evolution for Constrained Optimization

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure