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Structured and Simultaneous Lyapunov Functions for System Stability Problems
Structured Simultaneous Lyapunov Functions System Stability Problems
2015/7/13
It is shown that many system stability and robustness problems can be reduced to the question of when there is a quadratic Lyapunov function of a certain structure which establishes stability of dx/dt...
Computing Bounds for the Structured Singular Value via an Interior Point Algorithm
Computing Bounds Structured Singular Value Interior Point Algorithm
2015/7/13
We describe an interior point algorithm for computing the upper bound for the structured singular value described in the paper by Fan, Tits and Doyle, IEEE Trans AC, Jan. 1991. We demonstrate the perf...
On Maximizing a Robustness Measure for Structured Nonlinear Perturbations
Maximizing Robustness Measure Structured Nonlinear Perturbations
2015/7/13
In this paper, we propose a robustness measure for LTI systems with causal, nonlinear diagonal perturbations with finite L_2-gain. We propose an algorithm to reliably compute this quantity. We show ho...
Interaction Value Analysis: When Structured Communication Benefits Organizations
Interaction Value Analysis When Structured Communication Benefits Organizations
2015/7/6
We present a mathematical model that predicts and explains the circumstances under which a management-defined communications tructure can add value to an organization. This model provides a game-theor...
Corrupted Sensing: Novel Guarantees for Separating Structured Signals
Corrupted sensing compressed sensing deconvolution error correction structured signal sparsity block sparsity low rank atomic norms ℓ 1 minimization
2013/6/14
We study the problem of corrupted sensing, a generalization of compressed sensing in which one aims to recover a signal from a collection of corrupted or unreliable measurements. While an arbitrary si...
Recovering Graph-Structured Activations using Adaptive Compressive Measurements
Recovering Graph-Structured Activations Adaptive Compressive Measurements
2013/6/13
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activate...
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Convex Tensor Decomposition Structured Schatten Norm Regularization
2013/4/28
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tens...
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Sparse approximation Regularization Fused-LASSO Split Bregman Multidimensional signals
2013/5/2
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization prob...
Object Oriented Data Analysis of Cell-Well Structured Data
data objects cell con uence bright
2013/4/28
Object oriented data analysis (OODA) aims at statistically analyzing populations of complicated objects. This paper is motivated by a study of cell images in cell culture biology, which highlights a c...
Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity
Algorithm Large Multi-task Regression Massive Structured Sparsity
2012/9/17
We develop a highly scalable optimization method called “hierarchical group-thresholding”for solving a multi-task regression model with complex structured sparsity constraints on both input and output...
Kramers-type effective Reactive Flow in Structured-noise Environments
Kramers-type Reactive Flow Structured-noise Environments
2012/9/17
The non-Markovian features of three typical anomalous diffusing systems are studied by analyti-cally solving the generalized Langevin equation directly driven by three kind of internal structured-nois...
Structured prediction tasks pose a fundamental trade-o between the need for model com-plexity to increase predictive power and the limited computational resources for inference in the exponentially-s...
Gaussian Oracle Inequalities for Structured Selection in Non-Parametric Cox Model
Gaussian Oracle Inequalities Structured Selection Non-Parametric Cox Model
2012/9/19
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard乫s model. Due to high impact of sequencing data, carrying genet...
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals
Tight Measurement Bounds Exact Recovery Structured Sparse Signals
2011/7/6
Standard compressive sensing results state that to exactly recover an s sparse signal in R^p, one requires O(s\cdotlog p) measurements. While this bound is extremely useful in practice, often real wor...