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Cluster: Sparse optimization & compressed sensing


10:30 - 12:00, room: H 1028

Chair: Benjamin Recht
New models and algorithms in sparse optimization

Boumal Riemannian algorithms and estimation bounds for synchronization of rotations [...]
Davenport A simple framework for analog compressive sensing [...]
Recht Atomic norm denoising with applications to spectrum estimation and system identification [...]


13:15 - 14:45, room: H 1028

Chair: Gitta Kutyniok
Sparse optimization and generalized sparsity models

Saab Recovering compressively sampled signals using partial support information [...]
Candes PhaseLift: Exact phase retrieval via convex programming [...]
Kutyniok Clustered sparsity [...]


15:15 - 16:45, room: H 1028

Chair: Michel Baes
Global rate guarantees in sparse optimization

Yin Augmented L1 and nuclear-norm minimization with a globally linearly convergent algorithm [...]
Xiao A proximal-gradient homotopy method for the sparse least-squares problem [...]
Baes First-order methods for eigenvalue optimization [...]




10:30 - 12:00, room: H 1028

Chair: Mark Schmidt
Machine learning algorithms and implementations

Schmidt Linearly-convergent stochastic gradient methods [...]
Oh Statistical analysis of ranking from pairwise comparisons [...]


13:15 - 14:45, room: H 1028

Chair: Peter Richtarik
Coordinate descent methods for huge-scale optimization

Richtarik Parallel block coordinate descent methods for huge-scale partially separable problems [...]
Takac Distributed block coordinate descent method: Iteration complexity and efficient hybrid implementation [...]
Tappenden Block coordinate descent method for block-structured problems [...]


15:15 - 16:45, room: H 1028

Chair: Andreas Michael Tillmann
Algorithms for sparse optimization I

Tillmann Heuristic optimality check and computational solver comparison for basis pursuit [...]
Zikrin Sparse optimization techniques for solving multilinear least-squares problems with application to design of filter networks [...]
Demenkov Real-time linear inverse problem and control allocation in technical systems [...]




10:30 - 12:00, room: H 1028

Chair: Kimon Fountoulakis
Algorithms for sparse optimization II

Fountoulakis Matrix-free interior point method for compressed sensing problems [...]
Wang Linearized alternating direction methods for Dantzig selector [...]
Voronin Iteratively reweighted least squares methods for structured sparse regularization [...]


13:15 - 14:45, room: H 1028

Chair: Shiqian Ma
Efficient first-order methods for sparse optimization and its applications

Ma An alternating direction method for latent variable Gaussian graphical model selection [...]
Lu Sparse approximation via penalty decomposition methods [...]
Goldfarb An accelerated linearized Bregman method [...]


15:15 - 16:45, room: H 1028

Chair: John C. Duchi
Structured models in sparse optimization

Jenatton Proximal methods for hierarchical sparse coding and structured sparsity [...]
Pham Alternating linearization for structured regularization problems [...]
Duchi Adaptive subgradient methods for stochastic optimization and online learning [...]




10:30 - 12:00, room: H 1028

Chair: Anatoli Juditsky
Computable bounds for sparse recovery

d'Aspremont High-dimensional geometry, sparse statistics and optimization [...]
Kilinc Karzan Verifiable sufficient conditions for l1-recovery of sparse signals [...]
Juditsky Accuracy guaranties and optimal l1-recovery of sparse signals [...]


13:15 - 14:45, room: H 1028

Chair: Wotao Yin
Nonconvex sparse optimization

Wen Alternating direction augmented Lagrangian methods for a few nonconvex problems [...]
Solombrino Linearly constrained nonsmooth and nonconvex minimization [...]
Lai On the Schatten p-quasi-norm minimization for low rank matrix recovery [...]


15:15 - 16:45, room: H 1028

Chair: Junfeng Yang
Variational signal processing -- algorithms and applications

Zhang On variational image decomposition model for blurred images with missing pixel values [...]
Yang Convergence of a class of stationary iterative methods for saddle point problems [...]




10:30 - 12:00, room: H 1028

Chair: Prateek Jain
Greedy algorithms for sparse optimization

Ravikumar Nearest neighbor based greedy coordinate descent [...]
Jain Orthogonal matching pursuit with replacement [...]


13:15 - 14:45, room: H 1028

Chair: Inderjit Dhillon
Structured matrix optimization

van den Berg Phase-retrieval using explicit low-rank matrix factorization [...]
Harchaoui Lifted coordinate descent for learning with Gauge regularization [...]
Dhillon Sparse inverse covariance matrix estimation using quadratic approximation [...]



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