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PMU Placement for Line Outage Identification via Multinomial Logistic Regression
IEEE Transactions on Smart Grid
Cited by: Papers (44)
We consider the problem of identifying a single line outage in a power grid by using data from phasor measurement units (PMUs). When a line outage occurs, the voltage phasor of each bus node changes in response to the change in network topology. Each individual line outage has a distinctive signature, and a multinomial logistic regression classifier can be trained to distinguish between these sign...Show More
Analyzing Vulnerability of Power Systems with Continuous Optimization Formulations
Potential vulnerabilities in a power grid can be exposed by identifying those transmission lines on which attacks (in the form of interference with their transmission capabilities) causes maximum disruption to the grid. In this study, we model the grid by (nonlinear) AC power flow equations, and assume that attacks take the form of increased impedance along transmission lines. We quantify disrupti...Show More
Forward–Backward Greedy Algorithms for Atomic Norm Regularization
In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as “atoms” allow us to define “atomic norms” that can be used to formulate convex regularizations for the reconstruction problem. Efficient algorithms are available to solve these formulations in certain specia...Show More
Forward — Backward greedy algorithms for signal demixing
2014 48th Asilomar Conference on Signals, Systems and Computers
Year: 2014 | Conference Paper |
Cited by: Papers (2)
Signal demixing arises in many applications. Common among these are the separation of sparse and low rank components in image and video processing, sparse and group sparse models in multitask learning and spikes and sinusoids in source separation problems. For specific problems of interest, many methods exist to perform recovery, but an approach that generalizes to arbitrary notions of simplicity ...Show More
Online algorithms for factorization-based structure from motion
Ryan Kennedy;Laura Balzano;Stephen J. Wright;Camillo J. Taylor
IEEE Winter Conference on Applications of Computer Vision
Year: 2014 | Conference Paper |
Cited by: Papers (3)
We present a family of online algorithms for real-time factorization-based structure from motion, leveraging a relationship between the incremental singular value decomposition and recent work in online matrix completion. Our methods are orders of magnitude faster than previous state of the art, can handle missing data and a variable number of feature points, and are robust to noise and sparse out...Show More
On GROUSE and incremental SVD
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Year: 2013 | Conference Paper |
Cited by: Papers (31)
GROUSE (Grassmannian Rank-One Update Subspace Estimation) [1] is an incremental algorithm for identifying a subspace of ℝn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis [2] has shown that GROUSE converges locally at an expected linear rate, under certain assumptions. GROUSE has a similar flavor to the in...Show More
A greedy forward-backward algorithm for atomic norm constrained minimization
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2013 | Conference Paper |
Cited by: Papers (14)
In many applications in signal and image processing, communications, and system identification, one aims to recover a signal that has a simple representation in a given basis or frame. Key devices for obtaining such representations are objects called atoms, and functions called atomic norms. These concepts unify the idea of simple representations across several known applications, and motivate ext...Show More
Optimization Algorithms and Applications for Speech and Language Processing
Stephen J. Wright;Dimitri Kanevsky;Li Deng;Xiaodong He;Georg Heigold;Haizhou Li
Optimization techniques have been used for many years in the formulation and solution of computational problems arising in speech and language processing. Such techniques are found in the Baum-Welch, extended Baum-Welch (EBW), Rprop, and GIS algorithms, for example. Additionally, the use of regularization terms has been seen in other applications of sparse optimization. This paper outlines a range...Show More
Overview of large scale optimization for discriminative training in speech recognition
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2012 | Conference Paper |
Cited by: Papers (1)
Over the past few decades, a variety of specialized approaches have been proposed to solve large problems in speech recognition. Conventional optimization techniques have not been widely applied, because the problems do not readily admit an objective for evaluating a given set of parameters and because of the large number of parameters. This situation is changing, due to recent developments in alg...Show More
Inherently robust suboptimal nonlinear MPC: Theory and application
2011 50th IEEE Conference on Decision and Control and European Control Conference
Year: 2011 | Conference Paper |
Cited by: Papers (13)
We discuss inherent robust stability properties of discrete-time nonlinear systems controlled by Model Predictive Control (MPC) algorithms that do not necessarily attain the global minimum of the optimization problem solved at each sample time. For these implementable suboptimal MPC algorithms, we prove nominal exponential stability of the origin of the closed-loop system. The stability property i...Show More
Convex approaches to model wavelet sparsity patterns
Nikhil S. Rao;Robert D. Nowak;Stephen J. Wright;Nick G. Kingsbury
2011 18th IEEE International Conference on Image Processing
Year: 2011 | Conference Paper |
Cited by: Papers (33)
Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-co...Show More
Hierarchical cooperative distributed model predictive control
Proceedings of the 2010 American Control Conference
Year: 2010 | Conference Paper |
Cooperative distributed model predictive control has recently been shown to provide stabilizing feedback for plants composed of any finite number of dynamically coupled subsystems. No assumption is made about the strength of the coupling between the subsystems. This control scheme provides optimal feedback in the limit of infinite optimization iterates, can be terminated before convergence of the ...Show More
The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees av...Show More
Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to m...Show More
Sparse reconstruction by separable approximation
2008 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2008 | Conference Paper |
Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to m...Show More
Power Awareness in Network Design and Routing
J. Chabarek;J. Sommers;P. Barford;C. Estan;D. Tsiang;S. Wright
IEEE INFOCOM 2008 - The 27th Conference on Computer Communications
Year: 2008 | Conference Paper |
Exponential bandwidth scaling has been a fundamental driver of the growth and popularity of the Internet. However, increases in bandwidth have been accompanied by increases in power consumption, and despite sustained system design efforts to address power demand, significant technological challenges remain that threaten to slow future bandwidth growth. In this paper we describe the power and assoc...Show More
Distributed MPC Strategies With Application to Power System Automatic Generation Control
Aswin N. Venkat;Ian A. Hiskens;James B. Rawlings;Stephen J. Wright
A distributed model predictive control (MPC) framework, suitable for controlling large-scale networked systems such as power systems, is presented. The overall system is decomposed into subsystems, each with its own MPC controller. These subsystem-based MPCs work iteratively and cooperatively towards satisfying systemwide control objectives. If available computational time allows convergence, the ...Show More
Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ) error term combined with a sparseness-inducing regularization term. Basis pursuit, the least absolute shrinkage and selection operato...Show More
Toeplitz-Structured Compressed Sensing Matrices
Waheed U. Bajwa;Jarvis D. Haupt;Gil M. Raz;Stephen J. Wright;Robert D. Nowak
2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Year: 2007 | Conference Paper |
The problem of recovering a sparse signal x Rn from a relatively small number of its observations of the form y = Ax Rk, where A is a known matrix and k « n, has recently received a lot of attention under the rubric of compressed sensing (CS) and has applications in many areas of signal processing such as data cmpression, image processing, dimensionality reduction, etc. Recent work has established...Show More
Distributed Output Feedback MPC for Power System Control
Aswin N. Venkat;Ian A. Hiskens;James B. Rawlings;Stephen J. Wright
Proceedings of the 45th IEEE Conference on Decision and Control
Year: 2006 | Conference Paper |
In this paper, a distributed output feedback model predictive control (MPC) framework with guaranteed nominal stability and performance properties is described. Distributed state estimation strategies are developed for supporting distributed output feedback MPC of large-scale systems, such as power systems. It is shown that under certain (easily verifiable) conditions, local measurements are suffi...Show More
This article describes an implementable distributed MPC framework with guaranteed nominal stability and performance properties. The proposed distributed MPC framework consists of three main components (i) distributed estimator (ii) centralized/distributed target calculation (iii) distributed regulator. State estimation for distributed MPC is addressed using the well established Kalman filtering fr...Show More
Approximating StreamingWindow Joins Under CPU Limitations
22nd International Conference on Data Engineering (ICDE'06)
Year: 2006 | Conference Paper |
Cited by: Papers (8)
Data streaming systems face the possibility of having to shed load in the case of CPU or memory resource limitations. We study the CPU limited scenario in detail. First, we propose a new model for the CPU cost. Then we formally state the problem of shedding load for the goal of obtaining the maximum possible subset of the complete answer, and propose an online strategy for semantic load shedding. ...Show More
Stability and optimality of distributed model predictive control
Proceedings of the 44th IEEE Conference on Decision and Control
Year: 2005 | Conference Paper |
This article extends existing concepts in linear model predictive control (MPC) to a unified, theoretical framework for distributed MPC with guaranteed nominal stability and performance properties. Centralized MPC is largely viewed as impractical, inflexible and unsuitable for control of large, networked systems. Incorporation of the proposed distributed regulator provides a means of achieving opt...Show More
Minimizing delivery cost in scalable streaming content distribution systems
IEEE Transactions on Multimedia
Cited by: Papers (48)
Recent scalable multicast streaming protocols for on-demand delivery of media content offer the promise of greatly reduced server and network bandwidth. However, a key unresolved issue is how to design scalable content distribution systems that place replica servers closer to various client populations and route client requests and response streams so as to minimize the total server and network de...Show More
An approach to optimizing adaptive parabolic PDE solvers for the Grid
V. Adve;J. Browne;B. Ensink;J. Rice;P. Teller;M. Vernon;S. Wright
Proceedings International Parallel and Distributed Processing Symposium
Year: 2003 | Conference Paper |
The method of lines is a widely used algorithm for solving parabolic partial differential equations that could benefit greatly from implementation on Grid computing environments. This paper outlines the issues involved in executing method-of-lines codes on a Grid and in developing model-driven adaptive control strategies for these codes. We have developed a parameterizable benchmark called MOL tha...Show More
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