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This paper deals with the development of a nonlinear programming methodology for evaluating preventive actions to improve the dynamic security of power systems when poor voltage transients are detected. For the proposed power system modeling, voltage-degraded trajectories arise from both angle and voltage instability on the transient time scale. The rescheduling of the generation improves system security by ensuring an acceptable voltage transient behavior and constraining the system to be stable. The algorithm is implemented and tested on an actual power system. Keywords: On-line Dynamic Security Assessment, Preventive Control, Transient stability, Voltage Stability.

We summarize the state of the classification of almost perfect nonlinear (APN) power functions [x.sub.d] on GF([2.sup.n]) and contribute two new cases. To prove these cases we derive new permutation polynomials. The first case supports a well-known conjecture of Welch stating that for odd n = 2m + 1, the power function [x.sup.[2.sup.m] + 3] is even maximally nonlinear or, in other terms, that the crosscorrelation function between a binary maximum-length linear shift register sequences of degree n and a decimation of that sequence by [2.sup.m] + 3 takes on precisely the three values -1, -1 [+ or -] [2.sup.m+1]. Index Terms - Almost perfect nonlinear mappings, crosscorrelation, maximally nonlinear mappings, permutation polynomials, power functions, Walsh transform, Welch conjecture.

Byline: Jussi Keppo (1), Samu Peura (2) Keywords: optimal portfolio hedging; nonlinear programming; Monte Carlo simulation Abstract: We consider the problem of dynamically hedging a fixed portfolio of assets in the presence of non-linear instruments and transaction costs, as well as constraints on feasible hedging positions. We assume an investor maximizing the expected utility of his terminal wealth over a finite holding period, and analyse the dynamic portfolio optimization problem when the trading interval is fixed. An approximate solution is obtained from a two-stage numerical procedure. The problem is first transformed into a nonlinear programming problem which utilizes simulated coefficient matrices. The nonlinear programming problem is then solved numerically using standard constrained optimization techniques. Author Affiliation: (1) Systems Analysis Laboratory, Helsinki University of Technology, Otakaari 1, 02150, Espoo, Finland (2) Risk Management, Postipankki Ltd., Unioninkatu 22, FIN-00007, Helsinki, Finland Article History: Registration Date: 16/10/2004

Byline: Ralf Ostermark (1) Keywords: optimization; econometric functions; nonlinear programming; evolutionary programming Abstract: In the present paper we apply a new Genetic Hybrid Algorithm (GHA) to globally minimize a representative set of ill-conditioned econometric/mathematical functions. The genetic algorithm was specifically designed for nonconvex mixed integer nonlinear programming problems and it can be successfully applied to both global and constrained optimization. In previous studies, we have demonstrated the efficiency of the GHA in solving complicated NLP, INLP and MINLP problems. The present study is a continuation of this research, now focusing on a set of highly irregular optimization problems. In this paper we discuss the genetic hybrid algorithm, the nonlinear problems to be solved and present the results of the empirical tests. Author Affiliation: (1) Department of Business Administration, Abo Akademi University, Henriksgatan 7, FIN-20500, ABO, Finland e-mail Article History: Registration Date: 16/10/2004

This paper deals with the identification of a linear parameter-varying (LPV) system whose parameter dependence can be written as a linear-fractional transformation (LFT). We formulate an output-error identification problem and present a parameter estimation scheme in which a prediction error-based cost function is minimized using nonlinear programming; its gradients and (approximate) Hessians can be computed using LPV filters and inner products, and identifiable model sets (i.e., local canonical forms) are obtained efficiently using a natural geometrical approach. Some computational issues and experiences are discussed, and a simple numerical example is provided for illustration.

In this paper, we address a neural-network-based control design for a discrete-time nonlinear system. Our design approach is to approximate the nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin. A linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state-feedback control law for the nonlinear system based on the certainty equivalence principle. The control design equations are shown to he a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal. Further, the stability of the closed-loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed-loop system converges. Finally, a simple example is presented so as to illustrate our control design procedure. Index Term -Linear matrix inequality, multilayer perceptron, nonlinear control, stability analysis.

The use of polar nonlinear processing to reject a constant carrier envelope (and possibly wideband) jammer of a DS/SS spread spectrum link is explored, with the additional objective of ensuring constant false alarm rate (CFAR) in the reception of the associated DS/SS synchronization preamble. It is shown that the constant carrier envelope jammer can be rejected very effectively, with CFAR, whether it is transmitted continuously or pulsed at some maximally damaging duty cycle.

This paper presents a Simulated Annealing Algorithm (SAA) to solve the Unit Commitment Problem (UCP). New rules for randomly generating feasible solutions are introduced. The problem has two subproblems: a combinatorial optimization problem and a nonlinear programming problem. The former is solved using the SAA while the latter problem is solved via a quadratic programming routine. Numerical results showed an improvement in the solutions costs compared to previously obtained results.

The pivot nonlinearity of a disk drive actuator is a complex phenomenon. Various models and compensation schemes were designed to cope with the nonlinearity. In this paper, a fuzzy logic based actuator pivot nonlinearity model is proposed. The fuzzy model can learn the dynamics of the actuator pivot nonlinearity by training with measured data. After the fuzzy model is trained, it can be augmented into a servo controller design as a disturbance observer to cancel the effects of the pivot nonlinearity. The fuzzy model, its modeling performance and a laboratory implementation to demonstrate its effectiveness in pivot nonlinearity compensation are presented.

The utility of numerical codes is greatly enhanced if they can be used in design, a situation that typically involves iterative optimization algorithms. An attractive way is to use gradient-based algorithms developed for solving nonlinear programming (NLP) problems. In this letter, we examine the performance of a general sequential quadratic programming (SQP) optimization algorithm for designing patch antennas in conjunction with a finite-element boundary-integral code. Index Terms - Microstrip antennas.

The concept of a general nonlinear parity-check equation was first proposed by Pietrobon et al. who used them to construct rotationally invariant trellis codes for two dimensional signal sets. This paper applies this technique to trellis-coded modulation (TCM) codes with multidimensional MPSK and MQAM constellations to construct nonlinear phase rotationally invariant multi-D codes. Based on this construction technique, a series of the best fully phase rotationally invariant multi-D trellis codes for a variety of signal constellations is obtained through a systematic code search program. The code search results show that most codes achieve the same asymptotic coding gain as the best linear, but nonphase rotationally invariant codes found by Pietrobon et al. Index Terms - Code search, multi-D signal sets, nonlinear codes, rotationally invariant codes, TCM codes.

Two distinct pairs of second order minimax dual programs following symmetric dual programs introduced by Balas is used. A symmetric duality theorem under seperatibility and bonvexity/boncavity of the kernel function is also established. Self-duality theorems for Mond-Weir pairs are obtained by assuming that the function K is skew symmetric. The second order minimax integer duality program is in the order or Wolfe pairs.

This paper develops a global optimum solution to the Economic Lot Scheduling Problem (ELSP) using the restrictions of the basic period approach as originally formulated by Bomberger. The ELSP is a classic problem in production which involves scheduling multiple products on a single facility, with the restriction that no two products may be produced at the same time. Ideally, a manufacturer could generate a schedule which would minimize the sum of setup and holding costs, but the complexities involved have so far precluded the attainment of such an ideal. This article presents a real time solution method for practical sized problems using the basic period version of the ELSP. The authors believe this is the first global optimal algorithm for the basic period ELSP to be presented in the literature. it is anticipated that future research will extend the idea presented in this article to cover more general approaches to the ELSP. Key words - batch production, inventory control, nonlinear programming, optimization, scheduling

A new nonlinear transform has been developed to solve the problem of multiscale image segmentation. All of its parameters are automatically selected, therefore eliminating the need for user-specified parameters. The automatic selection of both homogeneity and spatial scales also eliminates the need to make restrictive assumptions about the geometric of homogeneity properties of the structure.

In this study, a nonlinear programming approach using the successive quadratic programming optimization technique is developed for the optimal design of a pipeline network for water supply systems. The proposed method eliminates the equality constraints describing the hydraulics by a suitable choice of dependent and independent variables. The dependent variables are chosen based on graph theoretic decomposition of the network structure. This makes it possible to compute analytically the reduced constraints, objective function gradients, and reduced Hessian in a very efficient manner. This method of decomposition ensures that the nodal and loop balances are exactly satisfied and is robust for any initial starting point, able to handle incorrect initial as well as expansion of existing water distribution networks.

A review of the stochastic optimization techniques utilized for asset/liability management (ALM) by international firms offering multiperiod financial planning services is described. Specifically, solution algorithms for solving the problem as characterized by dynamic stochastic control models and a split variable version of the stochastic asset allocation problem are discussed. These have been implemented in the ALM of international benefits consultant Towers Perrin and Frank Russell's Russell-Yasuda-Kasai system for insurance companies.

The radius of curvature of a cam profile is an important factor that affects the working life of cam mechanisms. Based on flexible motion curves using parametric polynomials developed earlier, a method for maximizing the minimum radius of cam curvature using a nonlinear programming technique is presented. The relation between the radius of curvature of a cam and the kinematic features of the corresponding motion curves is discussed. Examples are presented to illustrate the method.

A method for determining how good the chosen wind velocity represents the wind data and a process for choosing a representative set of wind directions to enhance the modeling of wind data are shown. A mathematical program is developed to choose those directions which will minimize approximation error. The problem is a non-convex program and may have local minimum points. Comparison between the dominant wind direction method and the traditional method indicates that a smooth probability distribution results from applying the former.

A non-linear programming model is recommended for the funding of polytechnics in England which anticipates the effects of government policies. The unique feature of the model is the absence of an objective function. It is argued that this approach of non-linear programming can be extended to the social sciences.

The rigorous estimation of a dissociation constant ([K.sub.b]) for antagonists in functional assays has been sought by pharmacologists using a variety of techniques ever since the regression method of Arunlakshana and Schild in 1959. Here, Michael Lew and James Angus describe a simplified global regression method with improved accuracy compared to Schild analysis. The method is suitable for personal computers with standard graphing and statistical software. The accuracy of the predicted p[K.sub.b] values and confidence intervals has been tested by comparing examples of published data, and by mathematical (bootstrap) simulations.

Development of a nonlinear programming algorithm, for a mathematical optimization problem in the area of hospital management, is discussed. The problem is for decision management to get the most reimbursement in the hospital's rate structure. A maximizing charge structure must be selected. The algorithm is developed using Kuhn-Tucker theory of nonlinear programming. It can use elementary sorting techniques.

A new algorithm using the primal-dual interior point method with the predictor-corrector for solving nonlinear optimal power flow (OPF) problems is presented. The formulation and the solution technique are new. Both equalities and inequalities in the OPF are considered and simultaneously solved in a nonlinear manner based on the Karush-Kuhn-Tucker (KKT) conditions. The major computational effort of the algorithm is solving a symmetrical system of equations, whose sparsity structure is fixed. Therefore only one optimal ordering and one symbolic factorization are involved. Numerical results of several test systems ranging in size from 9 to 2423 buses are presented and comparisons are made with the pure primal-dual interior point algorithm. The results show that the predictor-corrector primal-dual interior point algorithm for OPF is computationally more attractive than the pure primal-dual interior point algorithm in terms of speed and iteration count.

A concept involving finite element analysis and nonlinear programming techniques provides an automated design for microelectronic packaging that satisfies the requirements of thermal and mechanical issues. One such design enables the sizing and layout of a power amplifier package component while another helps design the pad array carrier digital package.

On-line production control problems are solved by the conjunction of off-line adaptive solutions with the expected value of perfect information (EVPI) principle. Such problems are optimizations of the expected net profit where the state variables are conditioned on future decisions. By using extensive simulations, the EVPI algorithm and the adaptive algorithm AD/T are found to be the most efficient and accurate.

The synthesis of actively controlled composite wings is reviewed by a new multidisciplinary optimization capability. The design optimization combined with techniques for the various disciplines is used for the nonlinear programming or approximation of the composite wing of a remotely piloted vehicle.

We consider the problem of placing a set of rectangles of different sizes, in a non-overlapping fashion, within a large rectangle of minimum area. This general problem has several practical applications in location of departments in a minimum area and in two dimensional stock-cutting with the objective of keeping trim-loss at a minimum. We formulate the problem as a mixed-integer non-linear programming problem. We present computation results for two special cases of the general problem that reduce to linear integer programs., The problem of placing a set of different-size rectangles in a non-overlapping configuration within a specific rectangular area is investigated. A non-linear mixed-integer programming formulation is suggested as a solution for the two-dimensional assortment problem. The results show that the model is accurate.

Several techniques of optimizing machining conditions for a single pass machining task are investigated using sensitivity and parametric bound analysis. The results show that the binding constraints are the maximum ciutting temperature constraint and the bending constraint. There was a linear relation between optimal value function and cutting force and the maximum cutting temperature and right-hand side parameters.

Multiobjective optimization methods are employed to solve a constrained reliability and redundancy assignment problem involving series systems with time-dependent reliability. At issue is the balance required to achieve optimal component reliability with the minimum amount of components at every stage of the system. A gas turbine overspeed protection system is used as an example to derive a solution using fuzzy sets to model fuzzy objective and constraint functions.

For customers with a cogeneration system (CGS), a mixed-integer programming and nonlinear programming model is used to examine effects of time-of-use (TOU) rates on the optimal operation of the constituent equipment as well as the optimal sizing. Three customers are selected for case studies: a hotel, a hospital, and an office building. Effects of an on-peak/off-peak energy charge ratio on purchased power share in the total electricity demand are saturated at the ratio of 5. As the energy charge ratio is raised, cheaper off-peak power substitutes for city gas and the operating cost decreases. The responses of the hotel and the hospital are similar. The simulation results indicate that properly designed TOU rates can provide commercial customers an incentive to operate a CGS with an appropriate mix of the self-generation and commercial power.

Optimal joint production decision, joint product selling prices and joint cost allocations for a monopoly firm can be determined simultaneously through a net realizable value. The model eliminates the interdependency problem between product cost and prices using the nonlinear programming approach. It is applicable for imperfectly competitive firms with which the ordinary cost-pricing policies cannot apply.

New methods used in the multidisciplinary design of airplane wings are given. Structural, aerodynamic and control system models are used to simulate a wing's behavior with computers. This optimizes the design with a precision that is good for a prototype. Nonlinear programming and approximation techniques are also used in optimizing the design. The analysis of the control augmented, aeroservoelastic, structural synthesis problems using the new methods are given with the results.

Foreign fishing activity has increased near the US Coast and a nonlinear programming model was developed to determine the most efficient coast guard aircraft patrolling scheme to maximize interdiction efforts. The results showed that an estimate can be made regarding the maximization of efficiency of aircraft patrols and their deterrent effect on intrusions made on the US coastline. The results were cited in budget deliberations and enactment of territorial definition statutes.

This note presents an aircraft turbofan engine model identification in the time domain. The identification makes use of a multivariable linear model of turbofan engine dynamics having uncertain eigenvalues. The uncertainty describes the difference between nonlinear and linear models. The bounds for real and imaginary parts of uncertain matrix eigenvalues are determined using nonlinear programming. The results of identification application using a detailed nonlinear model of an aircraft turbofan engine are presented. Index Terms--Aircraft turbofan engine, identification, linear model, nonlinear programming, uncertain eigenvalues.