Minority Challenge of Majority Actions in a Close Corporation in Italy and the United States

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to estimate the change times and the mean values within the segments. The second case is when the mean value is constant, but the variance can change. The assumption is that the variance is piecewise constant in time, and we want to estimate change times and the variance values within the segments. To find solutions to these problems, we will study an l_1 regularized maximum likelihood method, related to the fused lasso method and l_1 trend filtering, where the parameters to be estimated are free to vary at each sample. To penalize variations in the estimated parameters, the $l_1$-norm of the time difference of the parameters is used as a regularization term. This idea is closely related to total variation denoising. The main contribution is that a convex formulation of this variance estimation problem, where the parametrization is based on the inverse of the variance, can be formulated as a certain $l_1$ mean estimation problem. This implies that results and methods for mean estimation can be applied to the challenging problem of variance segmentation/estimationQC 20140908</p

An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems

We present an alternating augmented Lagrangian method for convex optimization problems where the cost function is the sum of two terms, one that is separable in the variable blocks, and a second that is separable in the difference between consecutive variable blocks. Examples of such problems include Fused Lasso estimation, total variation denoising, and multi-period portfolio optimization with transaction costs. In each iteration of our method, the first step involves separately optimizing over each variable block, which can be carried out in parallel. The second step is not separable in the variables, but can be carried out very efficiently. We apply the algorithm to segmentation of data based on changes inmean (l_1 mean filtering) or changes in variance (l_1 variance filtering). In a numerical example, we show that our implementation is around 10000 times faster compared with the generic optimization solver SDPT3

A distributed primal-dual interior-point method for loosely coupled problems using ADMM

In this paper we propose an efficient distributed algorithm for solving loosely coupled convex optimization problems. The algorithm is based on a primal-dual interior-point method in which we use the alternating direction method of multipliers (ADMM) to compute the primal-dual directions at each iteration of the method. This enables us to join the exceptional convergence properties of primal-dual interior-point methods with the remarkable parallelizability of ADMM. The resulting algorithm has superior computational properties with respect to ADMM directly applied to our problem. The amount of computations that needs to be conducted by each computing agent is far less. In particular, the updates for all variables can be expressed in closed form, irrespective of the type of optimization problem. The most expensive computational burden of the algorithm occur in the updates of the primal variables and can be precomputed in each iteration of the interior-point method. We verify and compare our method to ADMM in numerical experiments.Comment: extended version, 50 pages, 9 figure

Optimal Input Signal Design and MPC of Nonlinear Dynamical Systems

The main topics of this master’s project are control theory, system identification and convex optimization. The objective is to develop, implement and test methods for optimal input signal design and for control of a nonlinear dynamical system using MPC. The thesis begins with a theoretical part, in which some known results in these fields are summarized. In the applied part of the thesis, methods are developed and exemplified in MATLAB. Optimal input signal design is performed on specific FIR, ARX and DCmotor systems which are all controlled by an MPC. The implementation works very well for the FIR and ARX systems. The estimates of the true parameters fulfill the pre-specified requirements when using the optimal input signal constructed. An unexpected behavior is obtained of the estimates for the DC-motor system. Some additional approximations which were made in the design of the optimal input signal are thought to be the cause. Although, the source of the odd behavior were never confirmed. To be able to have a user-friendly environment for optimal input signal design, further work is necessary to overcome numerical problems in the implementation. A general method of implementing MPC of nonlinear dynamical systems is constructed. It is problematic to use MPC to control a nonlinear system. The reason for this is that a nonlinear system in general corresponds to a non-convex optimization problem in the MPC algorithm. Our method is based on making the problem convex through a linearization of the nonlinear system dynamics. The method is tested on simulations of a reaction wheel pendulum and a two link robot arm. It works very well and the systems fulfill the control objectives. Each optimization problem takes about 0.3-1 second to solve when using cvx. This is in some situations too slow to be able to control a system in reality. Further work is recommended on implementing our method with another solver so that it can be tested on an actual system which requires new updates every milli- or microsecond.Design av optimal insignal och MPC för ickelinjära dynamiska system Detta examensarbete berör områden som reglerteknik, systemidentifiering och konvex optimering. Syftet är att utveckla metoder för design av en optimal insignal och för att använda MPC på ickelinjära dynamiska system. Rapporten börjar med en teoretisk del som sammanfattar kända resultat inom dessa områden. Därefter följer beskrivningar av de metoder som har utvecklats och de exemplifieras genom simulering i MATLAB. En metod för att designa en optimal insignal för specifika FIR-, ARX och DC-system har utvecklats. Systemen styrs med hjälp av en MPC regulator. Metoden fungerar utmärkt för FIR- och ARX-system. Ett oväntat resultat erhålls för fallet med DC-motorn. Vi tror att det beror på de approximationer som har gjorts särskilt för detta system men det har inte kunnat bekräftas. För att skapa en användarvänlig miljö för design av en optimal insignal givet ett system och en regulator så krävs ytterligare arbete. Det bör fokusera på att eliminera de numeriska problem som uppstår när metoden implementeras i MATLAB. En allmän metod för att använda MPC på ickelinjära dynamiska system har implementerats. Ett ickelinjärt system ger generellt upphov till ett ickekonvext optimeringsproblem i MPC algoritmen. Det är därför problematiskt att använda MPC för att reglera ickelinjära system. Vår metod bygger på att göra problemet konvext genom att linjärisera det icke linjära systemet. Metoden har testats på simuleringar av en pendel och en två-länkad robotarm. Den presterar väldigt bra och systemen uppnår önskat beteende. Optimeringsproblemet tar cirka 0,3-1 sekund att lösa när vi använder cvx. Detta är i vissa fall för långsamt för att kunna reglera ett verkligt system. Ytterligare arbete där metoden implementeras med en annan lösare rekommenderas. Detta skulle möjliggöra att man kan testa den på ett verkligt system som kräver uppdateringar varje milli- eller mikrosekund

ADMM for l1 Regularized Optimization Problems and Applications Oriented Input Design for MPC

This licentiate thesis is divided into two main parts. The first part considers alternating direction method of multipliers (ADMM) for ℓ1 regularized optimization problems and the second part considers applications oriented input design for model predictive control (MPC). Many important problems in science and engineering can be formulated as convex optimization problems. As such, they have a unique solution and there exist very efficient algorithms for finding the solution. We are interested in methods that can handle big, in terms of the number of variables, optimization problems in an efficient way. Large optimization problems are common in many fields of research, for example, the problem of feature selection from huge medical data sets. ADMM is a method capable of handling such problems. We derive a scalable and efficient algorithm based on ADMM for two ℓ1 regularized optimization problems: ℓ1 mean and covariance filtering that occur in signal processing, and ℓ1 regularized MPC that is a specific type of model based control. System identification provides tools for estimating models of dynamical systems from experimental data. The application of such models can be divided into three main categories: prediction, simulation and control. We focus on identifying models used for control, with special attention to MPC. The objective is to minimize a cost related to the identification experiment while guaranteeing, with high probability, that the obtained model gives an acceptable control performance. We use applications oriented input design to find such a model. We present a general procedure of implementing applications oriented input design to unknown, and possibly nonlinear, systems controlled using MPC. In addition, we show that the input design problem obtained for output-error systems has the same simple structure as for finite impulse response systems.QC 20121017</p

Application-Oriented Input Design and Optimization Methods Involving ADMM

This thesis is divided into two main parts. The first part considers application-oriented input design, specifically for model predictive control (MPC). The second part considers alternating direction method of multipliers (ADMM) for ℓ1 regularized optimization problems and primal-dual interior-point methods. The theory of system identification provides methods for estimating models of dynamical systems from experimental data. This thesis is focused on identifying models used for control, with special attention to MPC. The objective is to minimize the cost of the identification experiment while guaranteeing, with high probability, that the obtained model gives an acceptable control performance. We use application-oriented input design to find such a model. We present a general procedure of implementing application-oriented input design to unknown, possibly nonlinear, systems controlled using MPC. The practical aspects of application-oriented input design are addressed and the method is tested in an experimental study. In addition, a MATLAB-based toolbox for solving application-oriented input design problems is presented. The purpose of the toolbox is threefold: it is used in research; it facilitates communication of research results; it helps an engineer to use application-oriented input design. Several important problems in science can be formulated as convex optimization problems. As such, there exist very efficient algorithms for finding the solutions. We are interested in methods that can handle optimization problems with a very large number of variables. ADMM is a method capable of handling such problems. We derive a scalable and efficient algorithm based on ADMM for two ℓ1 regularized optimization problems: ℓ1 mean and covariance filtering, and ℓ1 regularized MPC. The former occurs in signal processing and the latter is a specific type of model based control. We are also interested in optimization problems with certain structural limitations. These limitations inhibit the use of a central computational unit to solve the problems. We derive a distributed method for solving them instead. The method is a primal-dual interior-point method that uses ADMM to distribute all the calculations necessary to solve the optimization problem at hand.QC 20160602</p

MOOSE2—A toolbox for least-costly application-oriented input design

MOOSE2 is a MATLAB®-based toolbox for solving least-costly application-oriented input design problems in system identification. MOOSE2 provides the spectrum of the input signal to be used in the identification experiment made to estimate a linear parametric model of the system. The objective is to find a spectrum that minimizes experiment cost while fulfilling constraints imposed in the experiment and on the obtained model. The constraints considered by MOOSE2 are: frequency or power constraints on the signal spectra in the experiment, and application or quality specifications on the obtained model. Keywords: Input design, System identification, MATLAB