Publications related to Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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

Inner approximations of the region of attraction for polynomial dynamical systems

Colin Neil Jones, Milan Korda

In a previous work we developed a convex infinite dimensional linear programming (LP) approach to approximating the region of attraction (ROA) of polynomial dynamical systems subject to compact basic semialgebraic state constraints. Finite dimensional rela ...

2013

A convex optimization approach for image recovery from nonlinear measurements in optical interferometry

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Anna Auria Rasclosa

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bi-spectrum measurements. We formulate a linear version of the problem for the order-3 tensor formed by the tensor product of the ...

2013

Adaptive stochastic convex optimization over networks

Ali H. Sayed

In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a distributed diffus ...

IEEE2013

Non-convex optimization for robust multi-view imaging

Pierre Vandergheynst, Gilles Puy

We study the multi-view imaging problem where one has to reconstruct a set of l images, representing a single scene, from a few measurements made at different viewpoints. We first express the solution of the problem as the minimizer of a non-convex objecti ...

2013

Non-convex optimization for robust multi-view imaging

Pierre Vandergheynst, Gilles Puy

We study the multi-view imaging problem where one has to reconstruct a set of l images, representing a single scene, from a few measurements made at different viewpoints. We first express the solution of the problem as the minimizer of a non-convex objecti ...

2013

Probabilistic inverse reinforcement learning in unknown environments

Christos Dimitrakakis

We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to sol ...

2013

Design and operations of gas transmission networks

Frédéric Louis François Babonneau

Problems dealing with the design and the operations of gas transmission networks are challenging. The difficulty mainly arises from the simultaneous modeling of gas transmission laws and of the investment costs. The combination of the two yields a non-line ...

2013

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

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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

Inner approximations of the region of attraction for polynomial dynamical systems

A convex optimization approach for image recovery from nonlinear measurements in optical interferometry

Adaptive stochastic convex optimization over networks

Non-convex optimization for robust multi-view imaging

Non-convex optimization for robust multi-view imaging

Probabilistic inverse reinforcement learning in unknown environments

Design and operations of gas transmission networks

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

Inner approximations of the region of attraction for polynomial dynamical systems

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

A convex optimization approach for image recovery from nonlinear measurements in optical interferometry

Probabilistic inverse reinforcement learning in unknown environments

Design and operations of gas transmission networks

Non-convex optimization for robust multi-view imaging

Adaptive stochastic convex optimization over networks

Non-convex optimization for robust multi-view imaging

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