Publications related to UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Colin Neil Jones, Milan Korda, Georgios Stathopoulos

This work presents an algorithmic scheme for solving the infinite-time constrained linear quadratic regulation problem. We employ an accelerated version of a popular proximal gradient scheme, commonly known as the Forward-Backward Splitting (FBS), and prov ...

Institute of Electrical and Electronics Engineers2017

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

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper examines the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic g ...

IEEE2016

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

The article examines in some detail the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the stand ...

2016

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Fabio Nobile, Lorenzo Tamellini

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of term ...

2016

Path-following gradient-based decomposition algorithms for separable convex optimization

Quoc Tran Dinh

A new decomposition optimization algorithm, called path-following gradient-based decomposition, is proposed to solve separable convex optimization problems. Unlike path-following Newton methods considered in the literature, this algorithm does not require ...

Springer Verlag2014

Splitting methods in control

Colin Neil Jones, Ye Pu, Georgios Stathopoulos, Alexander Szücs

The need for optimal control of processes under a restricted amount of resources renders first order optimization methods a viable option. Although computationally cheap, these methods typically suffer from slow convergence rates. In this work we discuss t ...

IEEE2014

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Volkan Cevher, Anastasios Kyrillidis

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

IEEE2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

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Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Asynchronous updates for stochastic gradient descent

On the influence of momentum acceleration on online learning

On the influence of momentum acceleration on online learning

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Path-following gradient-based decomposition algorithms for separable convex optimization

Splitting methods in control

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Path-following gradient-based decomposition algorithms for separable convex optimization

On the influence of momentum acceleration on online learning

On the influence of momentum acceleration on online learning

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Splitting methods in control

Asynchronous updates for stochastic gradient descent

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions