Publications related to Adaptive Gradient Descent without Descent

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

Time-optimal path following considers the problem of moving along a predetermined geometric path in minimum time. In the case of a robotic manipulator with simplified constraints, a convex reformulation of this optimal control problem has been derived prev ...

Institute of Electrical and Electronics Engineers2013

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

Use of Convex Model Approximations for Real-Time Optimization via Modifier Adaptation

Dominique Bonvin, Grégory François

Real-Time Optimization (RTO) via modifier adaptation is a class of methods for which measurements are used to iteratively adapt the model via input-affine additive terms. The modifier terms correspond to the deviations between the measured and predicted co ...

American Chemical Society2013

Joint image registration and reconstruction from compressed multi-view measurements

Pierre Vandergheynst, Gilles Puy

We present a method for joint reconstruction of a set of images representing a given scene from few multi-view measurements obtained by compressed sensing. We model the correlation between measurements using global geometric transformations represented by ...

Spie-Int Soc Optical Engineering2013

Joint image registration and reconstruction from compressed multi-view measurements

Pierre Vandergheynst, Gilles Puy

We present a method for joint reconstruction of a set of images representing a given scene from few multi-view measurements obtained by compressed sensing. We model the correlation between measurements using global geometric transformations represented by ...

Spie-Int Soc Optical Engineering2013

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

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

From Discrete Measurements to Bounded Gradient Estimates: A Look at Some Regularizing Structures

Dominique Bonvin, Grégory François, Gene Bunin

Obtaining a reliable gradient estimate for an unknown function when given only its discrete measurements is a common problem in many engineering disciplines. While there are many approaches to obtaining an estimate of a gradient, obtaining lower and upper ...

American Chemical Society2013

Accelerated And Inexact Forward-Backward Algorithms

Luca Baldassarre

We propose a convergence analysis of accelerated forward-backward splitting methods for composite function minimization, when the proximity operator is not available in closed form, and can only be computed up to a certain precision. We prove that the 1/k( ...

Siam Publications2013

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

Adaptive Gradient Descent without Descent

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Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

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

Use of Convex Model Approximations for Real-Time Optimization via Modifier Adaptation

Joint image registration and reconstruction from compressed multi-view measurements

Joint image registration and reconstruction from compressed multi-view measurements

To Convexify or Not? Regression with Clustering Penalties on Graphs

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

From Discrete Measurements to Bounded Gradient Estimates: A Look at Some Regularizing Structures

Accelerated And Inexact Forward-Backward Algorithms

Regularized Bundle Methods for Convex and Non-Convex Risks

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

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

Joint image registration and reconstruction from compressed multi-view measurements

Use of Convex Model Approximations for Real-Time Optimization via Modifier Adaptation

To Convexify or Not? Regression with Clustering Penalties on Graphs

Joint image registration and reconstruction from compressed multi-view measurements

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

From Discrete Measurements to Bounded Gradient Estimates: A Look at Some Regularizing Structures

Accelerated And Inexact Forward-Backward Algorithms

Regularized Bundle Methods for Convex and Non-Convex Risks