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Computing bounds for the structured singular value via an interior point algorithm
Calculation structure singular value the interior point algorithm
2015/8/12
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...
Closed-loop convex formulation of classical and singular value loop shaping
Singular value circulation formation control dynamic systems digital technology application control system design
2015/8/12
We show that control system design via classical loop shaping and singular value loop shaping can be formulated as a closed-loop convex problem. Consequently, loop shaping problems can be solved by ef...
A SINGULAR VALUE THRESHOLDING ALGORITHM FOR MATRIX COMPLETION
Nuclear norm minimization matrix completion singular value thresholding Lagrange dual function Uzawa’s algorithm and linearized Bregman iteration
2015/6/17
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxat...
Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators
Singular value thresholding Stein’s unbiased risk estimate (SURE) differentiability of eigenvalues and eigenvectors magnetic resonance cardiac imaging
2015/6/17
In an increasing number of applications, it is of interest to recover an approximately low-rank data matrix from noisy observations. This paper develops an unbiased risk estimate—holding in a Gaussian...
Minimizing Communication for Eigenproblems and the Singular Value Decomposition
Minimizing Communication Eigenproblems the Singular Value Decomposition
2010/11/19
Algorithms have two costs: arithmetic and communication. The latter represents the cost of moving data, either between levels of a memory hierarchy, or between processors over a network. Communicatio...
Dynamic interactions in terms of senders, hubs, and receivers (SHR) using the singular value decomposition of time series: Theory and brain connectivity applications
Dynamic interactions senders hubs receivers singular value decomposition of time series: Theory and brain connectivity applications
2010/12/15
Understanding of normal and pathological brain function requires the identification and localization of functional connections between specialized regions. The availability of high time resolution sig...
Utilizing the properties of the smallest singular value of a matrix,we propose a new, efficient and reliable algorithm for solvingnonsymmetric matrix inverse eigenvalue problems, and compare it with a...