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Mixed State Estimation for a Linear Gaussian Markov Model
Mixed State Estimation Linear Gaussian Markov Model
2015/7/9
We consider a discrete-time dynamical system with Boolean and continuous states, with the continuous state propagating linearly in the continuous and Boolean state variables, and an additive Gaussian ...
Optimal Estimation of Deterioration from Diagnostic Image Sequence
Damage interior-point methods optimal estimation regularization
2015/7/9
Estimation of mechanical structure damage can greatly benefit from the knowledge that the damage accumulates irreversibly over time. This paper formulates a problem of estimation of a pixel-wise monot...
Convex Piecewise-Linear Fitting
Convex optimization Piecewise-linear approximation Data fi tting
2015/7/9
We consider the problem of fitting a convex piecewise-linear function, with some specified form, to given multi-dimensional data. Except for a few special cases, this problem is hard to solve exactly,...
Relaxed Maximum a Posteriori Fault Identification
Fault detection Statistical estimation Convex relaxation Interior-point methods
2015/7/9
We consider the problem of estimating a pattern of faults, represented as a binary vector, from a set of measurements. The measurements can be noise corrupted real values, or quantized versions of noi...
Robust Design of Slow-Light Tapers in Periodic Waveguides
robust optimization PDE-constrained optimization shape optimization coupling taper
2015/7/9
This paper concerns the design of tapers for coupling power between uniform and slow-light periodic waveguides. New optimization methods are described for designing robust tapers, which not only perfo...
l_1 Trend Filtering
detrending regularization Hodrick–Prescott fi ltering piecewise linear fi tting
2015/7/9
The problem of estimating underlying trends in time series data arises in a variety of disciplines. In this paper we propose a variation on Hodrick-Prescott (H-P) filtering, a widely used method for t...
Subspaces that Minimize the Condition Number of a Matrix
Subspaces Minimize Condition Number Matrix
2015/7/9
We define the condition number of a nonsingular matrix on a subspace, and consider the problem of finding a subspace of given dimension that minimizes the condition number of a given matrix. We give a...
Estimation of Faults in DC Electrical Power System
Estimation Faults DC Electrical Power System
2015/7/9
This paper demonstrates a novel optimization-based approach to estimating fault states in a DC power system. The model includes faults changing the circuit topology along with sensor faults. Our appro...
An Efficient Method for Large-scale Slack Allocation
timing graph, slack allocation delay budgeting convex optimization
2015/7/9
We consider a timing or project graph, with given delays on the edges and given arrival times at the source and sink nodes. We are to find the arrival times at the other nodes; these determine the tim...
We consider the problem of estimating a sparse signal from a set of quantized, Gaussian noise corrupted measurements, where each measurement corresponds to an interval of values. We give two methods f...
Mixed Linear System Estimation and Identification
Statistical estimation Convex relaxation Interior-point methods
2015/7/9
We consider a mixed linear system model, with both continuous and discrete inputs and outputs, described by a coefficient matrix and a set of noise variances. When the discrete inputs and outputs are ...
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Distributed Optimization Statistical Learning via Alternating Direction Method Multipliers
2015/7/9
Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasi...
This paper considers the problem of fitting regression models to historical fleet data with mixed effects, which arises in the context of statistical monitoring of data from a fleet (population) of si...
We consider an optimizing process (or parametric optimization problem), i.e., an optimization problem that depends on some parameters. We present a method for imputing or estimating the objective func...
In this paper, we describe the embedded conic solver (ECOS), an interior-point solver for second-order cone programming (SOCP) designed specifically for embedded applications. ECOS is written in low f...