identification of systems containing linear dynamic and static nonlinear elements identification of nonlinear systems which can be represented by combinations of linear dynamic and static nonlinear elements are considered previous results by the authors based on correlation analysis are combined to provide a unified treatment for this class of systems it is shown that systems composed of cascade feedforward feedback and multiplicative connections of linear dynamic and zero memory nonlinear elements can be identified in terms of the individual component subsystems from measurements of the system input and output only

on global identifiability for arbitrary model parametrizations it is a fundamental problem of identification to be able even before the data have been analyzed to decide if all the free parameters of a model structure can be uniquely recovered from data this is the issue of global identifiability in this contribution we show how global identifiability for an arbitrary model structure basically with analytic non linearities can be analyzed using concepts and algorithms from differential algebra it is shown how the question of global structural identifiability is reduced to the question of whether the given model structure can be rearranged as a linear regression an explicit algorithm to test this is also given furthermore the question of persistent excitation for the input can also be tested explicitly is a similar fashion the algorithms involved are very well suited for implementation in computer algebra one such implementation is also described

existence characterizations of temporal safety supervisors this note shows that beyond the expected basic results on supervisor existence and synthesis the temporal logic systems analysis for the invariance control of an arbitrary past formula p yields new insights into supremal control of temporal safety these insights come in the form of equivalent temporal characterizations one characterization allows a natural language interpretation which provides a very good intuitive feel of the supremal controllability concept another provides a structurally elegant representation revealing that the past formula p must never be stronger than some fixed logic constant importantly from the control synthesis viewpoint these temporal characterizations provide the opportunity to use several industrial strength commercially available theorem provers to accomplish the synthesis task in a transparent fashion finally a controllable canonical form unifying the supremal exact and infimal existences of temporal safety control is exhibited and its theoretical significance is discussed

syntax based synthesis for temporal safety supervision this paper presents a supremal control generalization of a temporal version of supervisory control for temporal safety in discrete event systems the generalization is an outcome of the development of a temporal logic equations approach formulated using unary transformer concepts borrowed mainly from the predicate and predicate transform theory of dijkstra and scholten the proposed syntax based approach has not hitherto been formally augmented to the temporal logic framework and is shown to parallel and to generalize the boolean equations approach proposed in the existing predicate control theory of kumar garg and marcus a detailed example which cannot be handled by the existing predicate control theory demonstrates how the natural language basis of temporal logic for requirement specification and syntax based control synthesis can be unified in a common logic framework

supervisory control of a class of discrete event processes this paper studies the control of a class of discrete event processes i e processes that are discrete asynchronous and possibly nondeter ministic the controlled process is described as the generator of a formal language while the controller or supervisor is constructed from the grammar of a specified target language that incorporates the desired closed loop system behavior the existence problem for a supervisor is reduced to finding the largest controllable language contained in a given legal language two examples are provided

modular feedback logic for discrete event systems we examine a modular approach to the synthesis of state feedback controls for the problem of maintaining a predicate on the state set of a discrete dynamic system invariant dynamical systems are modeled by automata together with a mechanism for enabling and disabling a subset of state transitions the basic problem of interest is to ensure by appropriate control action that a given predicate on the state set of the process remains invariantly true whenever it is initially satisfied assuming the predicate can be decomposed into the conjunction or disjunction of component predicates we determine conditions under which it is possible to synthesize the appropriate control in a modular fashion

kalman filtering for a generalized class of nonlinear systems and a new gaussian quadrature technique the class of nonlinear systems treated in this technical note consists of the discrete time nonlinear systems that are formed by the interconnection of linear systems through static nonlinearities with few inputs this special structure is exploited to reduce the dimension of the integrals involved in the propagation of mean values and covariances thus permitting accurate calculations furthermore a new quadrature scheme suitable for nonlinear kalman filtering is introduced the proposed techniques are applied to a seven dimensional numerical example the results show that they can increase the performance significantly

nonlinear black box modeling in system identification a unified overview a nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics there has been considerable recent interest in this area with structures based on neural networks radial basis networks wavelet networks and hinging hyperplanes as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules this paper describes all these approaches in a common framework from a user s perspective it focuses on what are the common features in the different approaches the choices that have to be made and what considerations are relevant for a successful system identification application of these techniques it is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space these mappings are discussed separately the latter mapping is usually formed as a basis function expansion the basis functions are typically formed from one simple scalar function which is modified in terms of scale and location the expansion from the scalar argument to the regressor space is achieved by a radial or a ridge type approach basic techniques for estimating the parameters in the structures are criterion minimization as well as two step procedures where first the relevant basis functions are determined using data and then a linear least squares step to determine the coordinates of the function approximation a particular problem is to deal with the large number of potentially necessary parameters this is handled by making the number of used parameters considerably less than the number of offered parameters by regularization shrinking pruning or regressor selection

identification of discrete hammerstein systems using kernel regression estimates in this note a discrete time hammerstein system is identified the weighting function of the dynamical subsystem is recovered by the correlation method the main results concern estimation of the nonlinear memoryless subsystem no conditions concerning functional form of the transform characteristic of the subsystem are made and an algorithm for estimation of the characteristic is presented the algorithm is a nonparametric kernel estimate of regression functions calculated from dependent data it is shown that the algorithm converges to the characteristic as the number of observations tend to infinity for sufficiently smooth characteristics the rate of convergence is o n 2 5 in probability

predicates and predicate transformers for supervisory control of discrete event dynamical systems discrete event systems are studied treating the state space as the fundamental modeling concept the control of discrete event systems using predicates and predicate transformers is treated predicates have the advantage that they can concisely characterize an infinite state space the notion of controllability of a predicate is defined and the supervisory predicate control problem is solved a closed form expression for the weakest controllable predicate is obtained the problem of controlling discrete event systems under incomplete state observation is also considered and observability of predicates is defined techniques for finding extremal solutions of boolean equations are used to derive minimally restrictive supervisors

parametric identification of structured nonlinear systems in this paper identification of structured nonlinear systems is considered using linear fractional transformations lft the a priori information regarding the structural interconnection is systematically exploited a parametric approach to the identification problem is investigated where it is assumed that the linear part of the interconnection is given and the input to the nonlinear part is measurable an algorithm for the identification of the nonlinear part is proposed the uniqueness properties of the estimate provided by the algorithm are examined it is shown that the estimate converges asymptotically to its true value under a certain persistence of excitation condition two simulated examples and a real data example are presented to show the effectiveness of the proposed algorithm

modified kolmogorov s neural network in the identification of hammerstein and wiener systems this brief deals with the possibilities of using the modified kolmogorov s neural network for the identification of non linear dynamic systems among them the wiener and hammerstein systems the algorithm of training the network is simple well convergent and with a small error of approximation the modified neural network is characterized by a simple computer algorithm it also omits complicated techniques of back propagation the simulation results are shown to illustrate the modified kolmogorov theorem

nonlinear black box models in system identification mathematical foundations we discuss several aspects of the mathematical foundations of the nonlinear black box identification problem we shall see that the quality of the identification procedure is always a result of a certain trade off between the expressive power of the model we try to identify the larger the number of parameters used to describe the model the more flexible is the approximation and the stochastic error which is proportional to the number of parameters a consequence of this trade off is the simple fact that a good approximation technique can be the basis of a good identification algorithm from this point of view we consider different approximation methods and pay special attention to spatially adaptive approximants we introduce wavelet and neuron approximations and show that they are spatially adaptive then we apply the acquired approximation experience to estimation problems finally we consider some implications of these theoretical developments for the practically implemented versions of the spatially adaptive algorithms

two unconstrained optimization approaches for the euclidean κ centrum location problem abstract consider the single facility euclidean centrum location problem in r n this problem is a generalization of the classical euclidean 1 median problem and 1 center problem in this paper we develop two efficient algorithms that are particularly suitable for problems where n is large by using unconstrained optimization techniques the first algorithm is based on the neural networks smooth approximation for the plus function and reduces the problem to an unconstrained smooth convex minimization problem the second algorithm is based on the fischer burmeister merit function for the second order cone complementarity problem and transforms the kkt system of the second order cone programming reformulation for the problem into an unconstrained smooth minimization problem our computational experiments indicate that both methods are extremely efficient for large problems and the first algorithm is able to solve problems of dimension n up to 10 000 efficiently

supervisory control of discrete event systems with ctl temporal logic specifications the supervisory control problem of discrete event systems with temporal logic specifications is studied the full branching time logic of ctl is used for expressing specifications of discrete event systems the control problem of ctl is reduced to the decision problem of ctl a small model theorem for the control of ctl is obtained it is shown that the control problem of ctl resp ctl is complete for deterministic double resp single exponential time a sound and complete supervisor synthesis algorithm for the control of ctl is provided special cases of the control of computation tree logic ctl and linear time temporal logic are also studied

a fractional approach to identify wiener hammerstein systems block oriented nonlinear models are appealing due to their simplicity and parsimony existing methods to identify the wiener hammerstein model suffer from one or several drawbacks this paper shows that it is possible to generate initial estimates in an alternative way a fractional model parameterization is the key to the success of this approach advantages are that no more than two iterative optimizations are needed and that large model orders can be handled as illustrated through a simulation example and experimental benchmark data it gives superior initial estimates and comparable optimized results

some tractable supervisory control problems for discrete event systems modeled by buchi automata discrete event systems des are modeled by buchi automata together with a means of online control in this setting the concept of a controllable language is extended to infinite strings and conditions for the existence of a supervisor controller to implement a prescribed closed loop behavior are derived the focus is on a class of des called product systems these are des composed of a finite set of asynchronous components a control problem for such a system typically requires the synthesis of an online controller so as to achieve some prescribed coordinated behavior of the component subsystems one of the principal difficulties in this task is that the size of the state space increases exponentially with the number of components it is shown that despite this fact several interesting control synthesis problems for such systems are computationally feasible and algorithms are developed for solution

identification of non linear systems using empirical data and prior knowledge an optimization approach abstract the choice of a parametric model structure in empirical and semi empirical non linear modeling is usually viewed as an important and critical step however it is known that by augmenting the least squares identification criterion with a term that imposes a penalty on the non smoothness of the model an optimal non parametric model can be found explicitly the optimal non parametric model will depend on the particular form of the penalty which can be looked upon as a priori knowledge or the desired properties of the model in this paper these results are extended in several directions i we show how useful types of prior knowledge other than smoothness can be included as a term in the criterion or as a constraint and how this influences the optimal model ii dynamic models and a general prediction error penalty are considered iii we present a practical numerical procedure for the identification of a close to optimal semi parametric model the numerical approach is motivated by the difficulty of deriving the optimal non parametric model if there are complicated constraints or penalty terms in the criterion and finally iv we discuss determination of the appropriate model complexity through selecting weights on the different penalties on the basis of empirical data since the optimal non parametric model is identified using an augmented optimization criterion and all prior knowledge and desired model properties may be specified through the augmented optimization criterion it is the choice of this criterion that is critical in this approach this elevates the empirical and semi empirical modeling problems to a more transparent and engineering friendly level than is achieved by directly specifying a parametric model structure hence our results shed some more light on modern non linear empirical modeling approaches like radial basis functions splines and neural networks in addition to providing a fundamental insight into the role of different kinds of prior knowledge this high level formulation is also attractive from a practical point of view as it lies closer to engineering thinking and less guesswork is required the flexibility and power of this approach are illustrated with a semi realistic simulation example

controllability and control invariance in discrete event systems recently for discrete event systems modelled by automata ramadge and wonham 1987 a b have proposed two control techniques called supervisory control and state feedback logic if control specifications are given in terms of predicates on the set of states both techniques are applicable this paper discusses the relationship between these techniques it is shown that the language accepted by the discrete event system with maximally permissive feedback is equal to the supremal controllable language if the predicate is control invariant moreover it is shown that the predicate is control invariant if there exists a controllable language with a certain condition on the set of reachable states but the converse does not hold in general

an efficient primal dual interior point method for minimizing a sum of euclidean norms the problem of minimizing a sum of euclidean norms dates from the 17th century and may be the earliest example of duality in the mathematical programming literature this nonsmooth optimization problem arises in many different kinds of modern scientific applications we derive a primal dual interior point algorithm for the problem by applying newton s method directly to a system of nonlinear equations characterizing primal and dual feasibility and a perturbed complementarity condition the main work at each step consists of solving a system of linear equations the schur complement equations this schur complement matrix is not symmetric unlike in linear programming we incorporate a mehrotra type predictor corrector scheme and present some experimental results comparing several variations of the algorithm including as one option explicit symmetrization of the schur complement with a skew corrector term we also present results obtained from a code implemented to solve large sparse problems using a symmetrized schur complement this has been applied to problems arising in plastic collapse analysis with hundreds of thousands of variables and millions of nonzeros in the constraint matrix the algorithm typically finds accurate solutions in less than 50 iterations and determines physically meaningful solutions previously unobtainable

test for local structural identifiability of high order non linearly parametrized state space models in this paper the issue of local structural identifiability of high order state space models with non linear parametrizations is addressed two methods are presented that provide analytical expressions for information matrices of which the rank determines identifiability the first method requires solving a lyapunov equation of high dimension and is applicable only to stable models the second method applies also to unstable models and is based on a gradient computation algorithm by dynamic programming that normally is used in an identification framework

applications of second order cone programming in a second order cone program socp a linear function is minimized over the intersection of an affine set and the product of second order quadratic cones socps are nonlinear convex problems that include linear and convex quadratic programs as special cases but are less general than semidefinite programs sdps several efficient primaldual interior point methods for socp have been developed in the last few years after reviewing the basic theory of socps we describe general families of problems that tan be recast as socps these include robust linear programming and robust leastsquares problems problems involving sums or maxima of norms or with convex hyperbolic constraints we discuss a variety of engineering applications such as filter design antenna array weight design truss design and grasping forte optimization in robotics we describe an efficient primaldual interior point method for solving socps which shares many of the features of primaldual interior point methods for linear program

k eccentricity and absolute k centrum of a probabilistic tree abstract the k eccentricity evaluated at a point x of a graph g is the sum of the weighted distances from x to the k vertices farthest from it the k centrum is the set of vertices for which the k eccentricity is a minimum the concept of k centrum includes as a particular case that of center and that of centroid or median of a graph the absolute k centrum is the set of points not necessarily vertices for which the k eccentricity is a minimum in this paper it will be proven that for a weighted tree both deterministic and probabilistic the k eccentricity is a convex function and that the absolute k centrum is a connected set and is contained in an elementary path hints will be given for the construction of an algorithm to find the k centrum and the absolute k centrum

failure diagnosis of discrete event systems with linear time temporal logic specifications the paper studies failure diagnosis of discrete event systems dess with linear time temporal logic ltl specifications the ltl formulas are used for specifying failures in the system the ltl based specifications make the specification specifying process easier and more user friendly than the formal language automata based specifications and they can capture the failures representing the violation of both liveness and safety properties whereas the prior formal language automaton based specifications can capture the failures representing the violation of only the safety properties such as the occurrence of a faulty event or the arrival at a failed state prediagnosability and diagnosability of dess in the temporal logic setting are defined the problem of testing prediagnosability and diagnosability is reduced to the problem of model checking an algorithm for the test of prediagnosability and diagnosability and the synthesis of a diagnoser is obtained the complexity of the algorithm is exponential in the length of each specification ltl formula and polynomial in the number of system states and the number of specifications the requirement of nonexistence of unobservable cycles in the system which is needed for the diagnosis algorithms in prior methods to work is relaxed finally a simple example is given for illustration

control of vector discrete event systems i the base model a vector discrete event system vdes is a discrete event system model in which the system state is represented by a vector with integer components and state transitions by integer vector addition the authors investigate such structures from the viewpoint of control theory the objective is to specialize to vdes the general automation based control theory of p j ramadge and w m wonham 1987 and w m wonham and p j ramadge 1988 it is shown that within the vdes framework a useful class of control systems can be compactly modeled and some of the associated control problems of ramadge and wonham can be more efficiently solved

identification of structured nonlinear systems this paper is concerned with the identification of static nonlinear components in a complex interconnected system these nonlinear components are treated nonparametrically in the sense that no natural parameterization is assumed to be available

smoothing functions for second order cone complementarity problems smoothing functions have been much studied in the solution of optimization and complementarity problems with nonnegativity constraints in this paper we extend smoothing functions to problems in which the nonnegative orthant is replaced by the direct product of second order cones these smoothing functions include the chen mangasarian class and the smoothed fischer burmeister function we study the lipschitzian and differential properties of these functions and in particular we derive computable formulas for these functions and their jacobians these properties and formulas can then be used to develop and analyze noninterior continuation methods for solving the corresponding optimization and complementarity problems in particular we establish the existence and uniqueness of the newton direction when the underlying mapping is monotone

second order cone programming second order cone programming socp problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the cartesian product of second order lorentz cones linear programs convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as socp problems as can many other problems that do not fall into these three categories these latter problems model applications from a broad range of fields from engineering control and finance to robust optimization and combinatorial optimization on the other hand semidefinite programming sdp that is the optimization problem over the intersection of an affine set and the cone of positive semidefinite matrices includes socp as a special case therefore socp falls between linear lp and quadratic qp programming and sdp like lp qp and sdp problems socp problems can be solved in polynomial time by interior point methods the computational effort per iteration required by these methods to solve socp problems is greater than that required to solve lp and qp problems but less than that required to solve sdp s of similar size and structure because the set of feasible solutions for an socp problem is not polyhedral as it is for lp and qp problems it is not readily apparent how to develop a simplex or simplex like method for socp while socp problems can be solved as sdp problems doing so is not advisable both on numerical grounds and computational complexity concerns for instance many of the problems presented in the survey paper of vandenberghe and boyd vb96 as examples of sdps can in fact be formulated as socps and should be solved as such in 2 3 below we give socp formulations for four of these examples the convex quadratically constrained quadratic programming qcqp problem problems involving fractional quadratic functions rutcor rutgers university e mail alizadeh rutcor rutgers edu research supported in part by the u s national science foundation grant ccr 9901991 ieor columbia university e mail gold ieor columbia edu research supported in part by the department of energy grant de fg02 92er25126 national science foundation grants dms 94 14438 cda 97 26385 and dms 01 04282

self tuning control of non linear armax models a control weighted self tuning minimum variance controller with a non linear difference equation structure is described an extended recursive least squares estimation algorithm is employed to provide the adaptiveness performance analysis of the controller is discussed in terms of a cumulative loss function and high order correlation functions of the system input output and residual sequences simulation results from an experiment using a model identified from a real system are also provided

instrumental variable methods for identification of hammerstein systems abstract consistency conditions for instrumental variable methods applied to hammerstein models are derived and analysed it is necessary to have a model that is not over parametrized and an input that is strongly persistently exciting i e the inputs and their powers are jointly persistently exciting some specific choices of the instruments are proved to give consistency those instruments are formed as filtered inputs and powers of the input consistency can be guaranteed if either the input is white noise or if a certain transfer function is positive real the vector of instruments is in general of larger dimension than the parameter vector it is shown then that a non singular linear transformation of the instruments gives a new and still consistent estimate some simulations using the iv variants proposed in this paper are also included

input design for structured nonlinear system identification this paper is concerned with the input design problem for a class of structured nonlinear models this class contains models described by an interconnection of known linear dynamic systems and unknown static nonlinearities many widely used model structures are included in this class the model class considered naturally accommodates a priori knowledge in terms of signal interconnections under certain structural conditions the identification problem for this model class reduces to standard least squares we treat the input design problem in this situation an expression for the expected estimate variance is derived a method for synthesizing an informative input sequence that minimizes an upper bound on this variance is developed this reduces to a convex optimization problem features of the solution include parameterization of the expected estimate variance by the input distribution and a graph based method for input generation

uniqueness of circuits and systems containing one nonlinearity we study systems containing one memoryless nonlinearity we show that two such systems have the same i o operator only when they are related by simple scaling delay and loop transformations the theory is applied to one port networks containing one nonlinear element

on the convergence of an iterative algorithm used for hammerstein system identification an analytical counterexample to the general convergence of an interative algorithm used for identification of hammerstein systems is given numerical illustration is also included

an efficient algorithm for minimizing a sum of euclidean norms with applications in recent years rich theories on polynomial time interior point algorithms have been developed these theories and algorithms can be applied to many nonlinear optimization problems to yield better complexity results for various applications in this paper the problem of minimizing a sum of euclidean norms is studied this problem is convex but not everywhere differentiable by transforming the problem into a standard convex programming problem in conic form we show that an epsilon optimal solution can be computed efficiently using interior point algorithms as applications to this problem polynomial time algorithms are derived for the euclidean single facility location problem the euclidean multifacility location problem and the shortest network under a given tree topology in particular by solving the newton equation in linear time using em gaussian elimination on leaves of a tree we present an algorithm which computes an epsilon optimal solution to the shortest network under a given full steiner topology interconnecting n regular points in o n sqrt n log bar c epsilon log n arithmetic operations where bar c is the largest pairwise distance among the given points the previous best known result on this problem is a graphical algorithm which requires o n2 arithmetic operations under certain conditions

on the series expansion approach to the identification of hammerstein systems a polynomial identification algorithm for recovering a nonlinearity in the hammerstein system is proposed the estimate employs the legendre orthogonal system with adaptively selected number of terms the global consistency along with rates of convergence are established no assumptions concerning continuity of the nonlinearity or its functional form are made a data driven method using the cross validation technique for selecting the number of terms in the estimate is presented

the complex structured singular value a tutorial introduction to the complex structured singular value is presented with an emphasis on the mathematical aspects of the based methods discussed here have been useful for analysing the performance and robustness properties of linear feedback systems several tests for robust stability and performance with computable bounds for transfer functions and their state space realizations are compared and a simple synthesis problem is studied uncertain systems are represented using linear fractional transformations lfts which naturally unify the frequency domain and state space methods

identification of nonlinear systems in the presence of unknown but bounded disturbances this paper deals with recursive parameter estimation of nonlinear systems in the presence of unknown but bounded disturbances the proposed technique is performed by the use of a modified extended least squares algorithm so that the estimated parameters are consistent with the measurements and the noise constraints while local asymptotic convergence under very general conditions is assured one of the main features is to show from the stability analysis connection between some arbitrary factors and basin of attraction high performances from the design and implementation point of view will be shown through a numerical example under severe conditions

identification of block oriented nonlinear systems starting from linear approximations a survey abstract block oriented nonlinear models are popular in nonlinear system identification because of their advantages of being simple to understand and easy to use many different identification approaches were developed over the years to estimate the parameters of a wide range of block oriented nonlinear models one class of these approaches uses linear approximations to initialize the identification algorithm the best linear approximation framework and the approximation framework or equivalent frameworks allow the user to extract important information about the system guide the user in selecting good candidate model structures and orders and prove to be a good starting point for nonlinear system identification algorithms this paper gives an overview of the different block oriented nonlinear models that can be identified using linear approximations and of the identification algorithms that have been developed in the past a non exhaustive overview of the most important other block oriented nonlinear system identification approaches is also provided throughout this paper

neural network approach for identification of hammerstein systems in this paper four different on line gradient based learning algorithms for training neural network hammerstein models are presented in a unified framework these algorithms namely the backpropagation for series parallel models the backpropagation sensitivity method and truncated backpropagation through time algorithm bptt for parallel models are derived analysed and compared for the truncated bptt it is shown that determination of the number of unfolding time steps necessary to calculate the gradient with an assumed degree of accuracy can be made on the basis of impulse response functions of sensitivity models the algorithms are shown to differ in their computational complexity gradient approximation accuracy and convergence rates numerical examples are also included to compare the performance of the algorithms

on solving the planar k centrum problem with euclidean distances this paper presents a solution procedure based on a gradient descent method for the k centrum problem in the plane the particular framework of this problem for the euclidean norm leads to bisector lines whose analytical expressions are easy to handle this allows us to develop different solution procedures which are tested on different problems and compared with existing procedures in the literature of location analysis the computational analysis reports that our procedures provide better results than the existing ones for the k centrum problem

a smoothing newton method for minimizing a sum of euclidean norms we consider the problem of minimizing a sum of euclidean norms f x sum i 1 m b i a i tx this problem is a nonsmooth problem because f is not differentiable at a point x when one of the norms is zero in this paper we present a smoothing newton method for this problem by applying the smoothing newton method proposed by qi sun and zhou math programming 87 2000 pp 1 35 directly to a system of strongly semismooth equations derived from primal and dual feasibility and a complementarity condition this method is globally and quadratically convergent as applications to this problem smoothing newton methods are presented for the euclidean facilities location problem and the steiner minimal tree problem under a given topology preliminary numerical results indicate that this method is extremely promising

a primal dual algorithm for minimizing a sum of euclidean norms we study the problem of minimizing a sum of euclidean norms this nonsmooth optimization problem arises in many different kinds of modern scientific applications in this paper we first transform this problem and its dual problem into a system of strongly semismooth equations and give some uniqueness theorems for this problem we then present a primal dual algorithm for this problem by solving this system of strongly semismooth equations preliminary numerical results are reported which show that this primal dual algorithm is very promising

a temporal logic approach to discrete event control for the safety canonical class this paper presents a temporal logic formulation of discrete event control which forms a new theoretical basis for control analysis and synthesis of a class of discrete event systems des based on the formulation a basic supervisory control theory is developed for a control objective specified by an invariance formula belonging to the safety canonical class of manna and pneuli using the safety canonical class as a basis the refinement and generalization of the existing basic predicate framework are demonstrated a simple example illustrates the formal axiomatic means to perform control theoretic analysis and synthesis under the new formulation