We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse [wavelet-type] representation in time domain. This leads to a large scale convex optimization problem, which involves l1 norm minimization. The optimization is carried by the Truncated Newton method, using preconditioned Conjugate Gradients in inn...

We study stochastic optimization problems when the data is sparse, which is in a sensedual to the current understanding of high-dimensional statistical learning and optimization.We highlight both the difficulties—in terms of increased sample complexity that sparse datanecessitates—and the potential benefits, in terms of allowing parallelism and asynchrony in thedesign of alg...

In this presentation, we introduce sparsity methods for networked control systems and show the effectiveness of sparse control. In networked control, efficient data transmission is important since transmission delay and error can critically deteriorate the stability and performance. We will show that this problem is solved by sparse control designed by recent sparse optimization methods.

In this paper, we propose a proximal gradient algorithm for solving a general nonconvex and nonsmooth optimization model of minimizing the summation of a C1,1 function and a grouped separable lsc function. This model includes the group sparse optimization via lp,q regularization as a special case. Our algorithmic scheme presents a unified framework for several well-known iterative thresholding ...

We provide stronger and more general primal-dual convergence results for FrankWolfe-type algorithms (a.k.a. conditional gradient) for constrained convex optimization, enabled by a simple framework of duality gap certificates. Our analysis also holds if the linear subproblems are only solved approximately (as well as if the gradients are inexact), and is proven to be worst-case optimal in the sp...

In this manuscript, we analyze the sparse signal recovery (compressive sensing) problem from the perspective of convex optimization by stochastic proximal gradient descent. This view allows us to significantly simplify the recovery analysis of compressive sensing. More importantly, it leads to an efficient optimization algorithm for solving the regularized optimization problem related to the sp...

In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse representation is formulated by cardinality-constrained 2 optimization of the control performance, which is...

Sparse learning is an important topic in many areas such as machine learning, statistical estimation, signal processing, etc. Recently, there emerges a growing interest on structured sparse learning. In this paper we focus on the lq-analysis optimization problem for structured sparse learning (0 < q ≤ 1). Compared to previous work, we establish weaker conditions for exact recovery in noiseless ...

Based on the convergent sequence of SDP relaxations for a multivariate polynomial optimization problem (POP) by Lasserre, Waki et al. constructed a sequence of sparse SDP relaxations to solve sparse POPs efficiently. Nevertheless, the size of the sparse SDP relaxation is the major obstacle in order to solve POPs of higher degree. This paper proposes an approach to transform general POPs to quad...