Publications associées à Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

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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

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

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Learning with Structured Sparsity: From Discrete to Convex and Back.

A Data-Driven Approach to Power Converter Control via Convex Optimization

Technical Report: Control of Nonlinear Systems with Explicit-MPC-like Controllers

A single-phase, proximal path-following framework

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Separation with restricted families of sets

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Convex Optimization for Big Data

An Augmented Lagrangian Coordination-Decomposition Algorithm for Solving Distributed Non-Convex Programs

A Data-Driven Approach to Power Converter Control via Convex Optimization

Learning with Structured Sparsity: From Discrete to Convex and Back.

Technical Report: Control of Nonlinear Systems with Explicit-MPC-like Controllers

A single-phase, proximal path-following framework

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Separation with restricted families of sets

Composite convex minimization involving self-concordant-like cost functions

Composite convex minimization involving self-concordant-like cost functions

Convex Optimization for Big Data

An Augmented Lagrangian Coordination-Decomposition Algorithm for Solving Distributed Non-Convex Programs