Publications associées à Stochastic Three-Composite Convex Minimization

Approximate dynamic programming via sum of squares programming

We describe an approximate dynamic programming method for stochastic control problems on infinite state and input spaces. The optimal value function is approximated by a linear combination of basis functions with coefficients as decision variables. By rela ...

2013

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

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

To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

IEEE2013

Regularized Bundle Methods for Convex and Non-Convex Risks

Machine learning is most often cast as an optimization problem. Ideally, one expects a convex objective function to rely on efficient convex optimizers with nice guarantees such as no local optima. Yet, non-convexity is very frequent in practice and it may ...

2012

Contactless deflection sensing of concave and convex shapes assisted by soft mirrors

Dario Floreano, Ramon Pericet Camara, Michal Karol Dobrzynski, Ionut Halasz

Typical deflection sensors like strain gauges or devices based on optical fibers require physical contact with the deflected substrate during the measurement process. Such contact, however, impacts on the softness of the substrate and may falsify the measu ...

2012

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

2011

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

Idiap2011

Robust Ultrasound Travel-time Tomography Using the Bent Ray Model

Martin Vetterli, Olivier Roy, Ali Hormati, Ivana Jovanovic

We present a bent ray reconstruction algorithm for an ultrasound tomography (UT) scanner designed for breast screening. The scanner consists of a circular array of transmitters and receivers which encloses the object to be imaged. By solving a nonlinear sy ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2010

Continuity Properties of Law-Invariant (Quasi-)Convex Risk Functions on L∞

Gregor Svindland

We study continuity properties of law-invariant (quasi-)convex functions f : L1(Ω,F, P) to ( ∞,∞] over a non-atomic probability space (Ω,F, P) .This is a supplementary note to [12] ...

Springer Verlag2010

Stochastic Three-Composite Convex Minimization

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Approximate dynamic programming via sum of squares programming

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

To Convexify or Not? Regression with Clustering Penalties on Graphs

Regularized Bundle Methods for Convex and Non-Convex Risks

Contactless deflection sensing of concave and convex shapes assisted by soft mirrors

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Robust Ultrasound Travel-time Tomography Using the Bent Ray Model

Continuity Properties of Law-Invariant (Quasi-)Convex Risk Functions on L∞

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

Approximate dynamic programming via sum of squares programming

To Convexify or Not? Regression with Clustering Penalties on Graphs

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Regularized Bundle Methods for Convex and Non-Convex Risks

Robust Ultrasound Travel-time Tomography Using the Bent Ray Model

Contactless deflection sensing of concave and convex shapes assisted by soft mirrors

Continuity Properties of Law-Invariant (Quasi-)Convex Risk Functions on L∞

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