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Supply chains with returned products are receiving increasing attention in the operations management community. The present paper studies a capacitated facility location model with bidirectional flows and a marginal value [...]

We introduce nondegeneracy and the C-index for C-stationary points of a QPCC, that is, for a mathematical program with a quadratic objective function and linear complementarity constraints. The C-index characterizes [...]

The ranking of MBA programmes by newspapers and magazines is common and usually controversial. This paper discusses the use of the most popular method of making these rankings via a multicriteria model which uses the weighted sum of a number of performance measures to give an overall score on which selection or ranking may be based. The weights are a quantitative model of the preferences of those making the evaluation. Many methods are available to obtain weights from preference statements so that for any set of preferences a number of different weight sets can be found depending on the method used. Cognitive limits lead to inconsistency in preference judgements so that weights may be subject both to uncertainty and to bias. It is proposed that choosing weights to minimize discrimination between alternatives (not weights) guards against unjustified discrimination between alternatives. Applying the method to data collected by the Financial Times shows the effect of varying the level of discrimination between weights and also the effect of using a reduced data set made necessary by the partial publication of information. Journal of the Operational Research Society (2010) 61, 632-639. doi:10.1057/jors.2008.193 Published online 18 March 2009 Keywords: multicriteria; quadratic programming; performance; MBA

In this article we introduce a novel method for transferring expert statements about an uncertain bounded quantity into a probability distribution. This is a challenging problem, as these statements are usually sparse and imprecise. We will approach both issues by penalization, such that the final distribution will contain the stated information, but subject to that distributes its probability mass as uniformly as possible. As a functional basis for the modeled distribution we use linear combinations of B-splines. B-splines are highly flexible, have excellent shape-preserving properties, and are also well suited for evaluation, differentiation, and integration. An R-package implementing the methods described in this article is available as online supplemental material. KEY WORDS: Brier entropy; Density estimation; Expert information: Quadratic programming.

This paper presents a new approach based on the linear approximation method and chance-constrained programming model for the optimal planning of single-tuned harmonic passive filters in an industrial power system. In the problem, the effects of the probabilistic characteristics of the nonlinear load currents and harmonic impedances of the system and linear loads are considered in the filter planning. The objective is to minimize the total filter installation cost, while the harmonic currents limits and filter component constraints are satisfied with predetermined confidence levels. The formulated probability-constrained problem is then transformed into a deterministic nonlinear programming problem and is solved by a sequential quadratic programming (SQP) solver. The proposed solution procedure is tested with an actual industrial power system and is verified by the conventional deterministic approach and by the Monte Carlo simulation. Numerical experiences show that the proposed method yields favorable results for the filter planning. Index Terms--Chance-constrained programming model, linear approximation, passive harmonic filter, probabilistic characteristics.

Usage of quadratic programming for estimation of network flows, while solving minimum cost flow problem by applying Gaussian Most Probable Flow Estimation techniques, is described.

Quadratic programming and Kuhn-Tucker theorem are used for determining extremal graphs.

Solving the troubles of disaggregated simplicial decomposition algorithm implementation in traffic assignment problem, using quadratic programming, is analyzed.

Decomposition methods are well-known techniques for solving quadratic programming (QP) problems arising in support vector machines (SVMs). In each iteration of a decomposition method, a small number of variables are selected and a QP problem with only the selected variables is solved. Since large matrix computations are not required, decomposition methods are applicable to large QP problems. In this paper, we will make a rigorous analysis of the global convergence of general decomposition methods for SVMs. We first introduce a relaxed version of the optimality condition for the QP problems and then prove that a decomposition method reaches a solution satisfying this relaxed optimality condition within a finite number of iterations under a very mild condition on how to select variables. Index Terms--Decomposition method, global convergence, quadratic programming (QP), support vector machines (SVMs), termination.

A computer-aided design technique for coupled resonator filters is presented. The technique is formulated as a bound constrained quadratic programming (QP) problem and predicated upon coupling matrix identification. The identification relies on the derivation of a rational model of the filter response and is formulated as a bound constrained QP problem. The definition of the optimization procedure in the form of a QP problem with an assumption of linear dependence of the coupling coefficients on filter geometrical dimensions leads to a significant reduction of the number of the full-wave electromagnetic simulations required to satisfy specifications. The proposed procedure is verified by designing and fabricating three microstrip filters. Index Terms--Coupling matrix, microwave filters.

A study evaluates the run-time distributions in an iterated local search to solve problems related to quadratic assignment problem.

Kernel methods, such as the support vector machine (SVM), are often formulated as quadratic programming (QP) problems. However, given m training patterns, a naive implementation of the QP solver takes O([m.sup.3]) training time and at least O([m.sup.2]) space. Hence, scaling up these QPs is a major stumbling block in applying kernel methods on very large data sets, and a replacement of the naive method for finding the QP solutions is highly desirable. Recently, by using approximation algorithms for the minimum enclosing ball (MEB) problem, we proposed the core vector machine (CVM) algorithm that is much faster and can handle much larger data sets than existing SVM implementations. However, the CVM can only be used with certain kernel functions and kernel methods. For example, the very popular support vector regression (SVR) cannot be used with the CVM. In this paper, we introduce the center-constrained MEB problem and subsequently extend the CVM algorithm. The generalized CVM algorithm can now be used with any linear/nonlinear kernel and can also be applied to kernel methods such as SVR and the ranking SVM. Moreover, like the original CVM, its asymptotic time complexity is again linear in m and its space complexity is independent of m. Experiments show that the generalized CVM has comparable performance with state-of-the-art SVM and SVR implementations, but is faster and produces fewer support vectors on very large data sets. Index Terms--Approximation algorithms, core vector machines (CVMs), kernel methods, minimum enclosing ball (MEB), quadratic programming, support vector machines (SVMs).

The maximum-length sequences, also called m-sequences, have received a lot of attention since the late 1960s. In terms of linear-feedback shift register (LFSR) synthesis they are usually generated by certain power polynomials over a finite field and in addition are characterized by a low cross correlation and high nonlinearity. We say that such a sequence is generated by a semi-bent function. Some new families of such function, represented by f(x) = [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] [C.sub.i]Tr([x.sup.2i] +1) , n odd and [C.sub.i] [member of] [F.sub.2], have recently (2002) been introduced by Khoo et al. We first generalize their results to even n. We further investigate the conditions on the choice of [c.sub.i] for explicit definitions of new infinite families having three and four trace terms. Also, a class of nonpermutation polynomials whose composition with a quadratic function yields again a quadratic semi-bent function is specified. The treatment of semi-bent functions is then presented in a much wider framework. We show how bent and semi-bent functions are interlinked, that is, the concatenation of two suitably chosen semi-bent functions will yield a bent function and vice versa. Finally, this approach is generalized so that the construction of both bent and semi-bent functions of any degree in certain range for any n [greater than or equal to] 7 is presented, n being the number of input variables. Index Terms--Bent function, Boolean function, linear permutation, m-sequence, nonlinearity, quadratic mapping, semi-bent function.

The recurrence formulas formulated in Gomez and Pediera were used to devise a null-space method for computing the search direction when a general inertia-controlling strategy. It is shown that at each iteration, only one triangular system has to be solved for determining the search direction. This method takes advantage of previously developed recurrence formulas for updating the search direction when the working set changes.

This paper presents a formulation of the Optimal Power Flow problem with an explicit modeling of Static Var Compensator (SVC) and Unified Power Flow Controller (UPFC) devices. The optimization problem is solved by using Sequential Quadratic Programming, where two convergence criteria and four different methods are studied to solve the quadratic subproblems. The proposed model is integrated in an object-oriented based decision support platform for competitive power markets. Validation of the method and practical applications to real longitudinal systems are discussed, where FACTS location and a UPFC-based interconnection are described. Results show the impact of SVC and UPFC FACTS technologies in the physical and economic behavior of a real system. Index Terms--FACTS, optimal power flow (OPF), quadratic programming (QP), Static Var Compensator (SVC), Unified Power Flow Controller (UPFC).

The application of a simulated annealing approach to the solution of a complex portfolio selection model is described. This model is a quadratic programming problem which arises when Markowitz' classical mean-variance model is enriched with the additional realistic constraints.

Byline: Markus Leippold (1), Liuren Wu (2) Keywords: quadratic model; term structure; positive interest rates; humps; expectation hypothesis; GMM Abstract: We consider the design and estimation of quadratic term structuremodels. We start with a list of stylized facts on interest ratesand interest rate derivatives, classified into three layers: (1)general statistical properties, (2) forecasting relations, and (3)conditional dynamics. We then investigate the implications of eachlayer of property on model design and strive to establish amapping between evidence and model structures. We calibrate atwo-factor model that approximates these three layers ofproperties well, and show that a flexible specification for themarket price of risk is important in capturing the stylizedevidence in forecasting relations while factor interactions areindispensable in generating the hump-shaped dynamics of bondyields. Author Affiliation: (1) Swiss Banking Institute, University of Zurich, Switzerland (2) Graduate School of Business, Fordham University, Switzerland Article History: Registration Date: 19/10/2004

The 'one-pass' heuristics for solving very large-scale unconstrained binary quadratic problems are presented. These can be used as stand-alone solution methods or as procedures for quickly generating high quality starting points for other, more sophisticated methods.

In July 2000, a new automatic gauge control (AGC) system was successfully installed at the Kakogawa Works, Kobe Steel, Ltd., Hyogo, Japan. The new AGC system consists of two functions: multivariable AGC based on mass flow thickness and roll eccentricity control. The multivariable AGC is designed using inverse linear quadratic theory, a type of optimal control theory, and controls both thickness and tension. Repetitive control theory is applied to roll eccentricity control as roll eccentricity is a periodic disturbance related to the rotational angle of the mils. The new AGC system has contributed to improved thickness accuracy and stable rolling. In this paper, details of the new AGC system are described and the application results are shown. Index Terms--Automatic gauge control, inverse linear quadratic theory, mass flow thickness, repetitive control theory, roll eccentricity, tandem cold mill, tension.

Sequential Quadratic Programming and the Finite Element Method were used to solve a non-linear optimization problem in a superconducting magnetic energy storage magnet.

The paper describes two applications of quadratic programming in finance, one from the early years (Markowitz's efficient portfolios with minimum risk) and the other a more recent innovation (Sharpe's style analysis which estimates an implied asset allocation for an investment fund). We show how, in the presence of inequality constraints, Excel's Solver can be used to find the optimal weights in both quadratic programming applications. We also implement a direct analytic solution for generating the efficient frontier when there are no inequality constraints using the matrix functions in Excel. Both applications use only a small number of asset classes and require repeated use of the minimisation task. We show how Visual Basic for Applications (Microsoft's macro language for Excel) can be used to program such tasks, confirming that techniques that were the preserve of dedicated software only a few years ago can now be easily replicated using Excel to solve real problems.

This paper deals with higher order approximation for discrete-time systems. It is shown that approximated feedback linearization at the second order can always be achieved under feedback compensation based on an approximated observer. An example is given in order to illustrate the control design and the efficiency of the proposed method. Index Terms - Dynamic state feedback, nonlinear discrete-time systems, nonlinear observer, quadratic approximation.

The present paper proposes a combined method for the global optimization of realistic engineering problems using radial basis functions and a deterministic approach. A die mold model for orientation of magnetic powder is optimized to show the effectiveness of the proposed method. Index terms - Global optimization, radial basis function, sequential quadratic programming method

This paper presents a reactive power optimization model that is based on successive quadratic programming (SQP) methods. Mathematical formulation and unified algorithm suppose different objective functions (OF) of reactive power optimization, depending on type and purposes of current reactive power control or planning problem. A bicriterion reactive power optimization model, that represents compromise between economical and security objective functions, is proposed. An efficient algorithm for approximation of initial problem by quadratic programming problem is described. Quadratic programming problem (QP) is solved on base of Newton type quadratic programming method. Modified successive quadratic programming method was developed, that provide reliable convergence of SQP method.

Real fretting fatigue cycles often involve complex loading where the fretting force may vary with a different phase or frequency to the other loads on the components. Analysis of the contact tractions in such cases can be difficult. Here we present a quadratic programming technique which enables the shear tractions to be determined in a relatively straightforward manner. A number of sample load histories are investigated and it is shown that the shear traction cycle rapidly shakes down to a steady state. Further, the results are relatively insensitive to the manner in which loading is commenced.

Quadratic and integer programming methods, dual ascent and branch-and-bound, were used to solve portfolio selection problems with multiple constraints and linked proposals. The methodology successfully integrates all the constraints such as risk, return, contingency conditions, integer restrictions and multiple constraints, into a single procedure. Computational results reveal that the methodology can be applied to portfolio selection problems with independent investment returns and diagonal variance-covariance matrices.

The presence of multiple constraints due to network line flow limits and emission allowances in economic dispatch of modern power systems makes the conventional Lambda-Delta iterative approach no longer effective. This paper proposes a practical strategy based on Quadratic Programming (QP) techniques to solve the real-time economic dispatch problem. It formulates the problem with a quadratic objective function based on the unit's cost curves in quadratic or piecewise-quadratic forms. The operation constraints are modeled as linear equality/inequality equations, resulting in a typical QP problem. Goal Programming techniques are also incorporated in the formulation which guarantees the best available solution even under infeasible conditions. In addition, the proposed strategy formulates the problem in the second phase dispatch in real time by including a set of emergency control variables to provide effective control strategies for properly relieving constraint violations if they exist. The effectiveness of the proposed strategy is demonstrated by an example power dispatch problem. Keywords: Economic Dispatch, Line Flow Constraints, Emission Constraints, Quadratic Programming, Goal Programming.

A new approach to the least-squares design of stable infinite impulse response (IIR) digital filters is presented. It uses an iterative method whereby the denominator polynomial is part of the weighting function. A standard quadratic programming problem is used to carry out each iteration, and a stable and truly least-squares solution is obtained when the iteration converges.

Experimental verification of minimum time, straight-line tracking using a two-link planar flexible robot is presented. Previously reported minimum-time angle histories are precompensated to account for joint servo-actuator dynamics. Using the precompensated joint commands, the optimal joint angles are tracked with such fidelity that the tip tracking error is less than 1.8 percent of the tip travel distance.