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

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

We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particular, our algorithm can be specialized to invert positive definite matrices i ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Faster Algorithms for Integer Programs with Block Structure

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Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik2018

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

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The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome ...

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Asynchronous updates for stochastic gradient descent

Alberto Silvio Chiappa

Finding convergence rates for numerical optimization algorithms is an important task, because it gives a justification to their use in solving practical problems, while also providing a way to compare their efficiency. This is especially useful in an async ...

2017

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

Ali H. Sayed, Bicheng Ying, Kun Yuan

Part I of this work developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algorithm was shown to be applicable to a larger set of combination policies than ea ...

2017

Quantization Design for Distributed Optimization

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbors and the channel has a limited data-rate. A co ...

2017

Wave Pipelining for Majority-based Beyond-CMOS Technologies

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The performance of some emerging nanotechnolo- gies benefits from wave pipelining. The design of such circuits re- quires new models and algorithms. Thus we show how Majority- Inverter Graphs (MIG) can be used for this purpose and we extend the related opt ...

2017

Memetic Viability Evolution for Constrained Optimization

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The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrai ...

Institute of Electrical and Electronics Engineers2016

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

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

Michael Maximilian Steinlechner

In this thesis, we present a Riemannian framework for the solution of high-dimensional optimization problems with an underlying low-rank tensor structure. Here, the high-dimensionality refers to the size of the search space, while the cost function is scal ...

EPFL2016

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

Memetic Viability Evolution for Constrained Optimization

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

Faster Algorithms for Integer Programs with Block Structure

Asynchronous updates for stochastic gradient descent

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

Quantization Design for Distributed Optimization

Wave Pipelining for Majority-based Beyond-CMOS Technologies

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