a krylov subspace method for large scale second order cone linear complementarity problem in this paper we first show that the second order cone linear complementarity problem soclcp can be solved by finding a positive zero s in mathbb r of a particular rational function h s and we then propose a krylov subspace method to reduce h s to h ell s as in the model reduction the zero s of h s can be accurately approximated by that of h ell s 0 which itself can be cast as a small eigenvalue problem the new method is made possible by a complete characterization of the curve of h s and it has several advantages over the bisection newton sf bn iteration recently proposed by l h zhang and w h yang math comp 83 2013 pp 1701 1720 and shown to be very efficient for small to medium size problems the method is tested and compared against the sf bn iteration and two other state of the art packages sf sdpt3 and sf sedumi our numerical results show that the method is very efficient for both small to medium dense problems and large scale ones

eigensolution of nonclassically damped structures by complex subspace iteration a nonclassically damped structure includes the consistent damping matrix all structural matrices are symmetric the stiffness matrix has to be positive definite this leads to a quadratic eigenproblem with complex eigenvalues and eigenvectors the common solution technique is the transformation into a linear equation system in state space form which doubles the order of the system and to solve it e g by the lanczos method the presented approach solves the quadratic eigenproblem directly i e the order is not increased and the sparseness or bandedness of the system matrices is retained the solution of the eigenproblem is obtained by the principle of subspace iteration in comparison to undamped modes the complex subspace iteration operates on a modal subspace of double size the iteration sequence is based on a filter principle this enables to utilise pre factorised system equations and starting information of eigenvectors from a previous solution even when the structural matrices are updated shift techniques can be applied which makes the algorithm useful for numerically tough problems including a large number of overdamped eigensolutions

quadratic weighted median filters for edge enhancement of noisy images quadratic volterra filters are effective in image sharpening applications the linear combination of polynomial terms however yields poor performance in noisy environments weighted median wm filters in contrast are well known for their outlier suppression and detail preservation properties the wm sample selection methodology is naturally extended to the quadratic sample case yielding a filter structure referred to as quadratic weighted median qwm that exploits the higher order statistics of the observed samples while simultaneously being robust to outliers arising in the higher order statistics of environment noise through statistical analysis of higher order samples it is shown that although the parent gaussian distribution is light tailed the higher order terms exhibit heavy tailed distributions the optimal combination of terms contributing to a quadratic system i e cross and square is approached from a maximum likelihood perspective which yields the wm processing of these terms the proposed qwm filter structure is analyzed through determination of the output variance and breakdown probability the studies show that the qwm exhibits lower variance and breakdown probability indicating the robustness of the proposed structure the performance of the qwm filter is tested on constant regions edges and real images and compared to its weighted sum dual the quadratic volterra filter the simulation results show that the proposed method simultaneously suppresses the noise and enhances image details compared with the quadratic volterra sharpener the qwm filter exhibits superior qualitative and quantitative performance in noisy image sharpening

cut generation through binarization for a mixed integer linear program where all integer variables are bounded we study a reformulation introduced by roy that maps general integer variables to a collection of binary variables we study theoretical properties and empirical strength of rank 2 simple split cuts of the reformulation we show that for a pure integer problem with two integer variables these cuts are sufficient to obtain the integer hull of the problem but that this does not generalize to problems in higher dimensions we also give an algorithm to compute an approximation of the rank 2 simple split cut closure we report empirical results on 22 benchmark instances showing that the bounds obtained compare favorably with those obtained with other approximate methods to compute the split closure or lattice free cut closure it gives a better bound than the split closure on 6 instances while it is weaker on 9 instances for an average gap closed 3 8 smaller than the one for the split closure

binary extended formulations of polyhedral mixed integer sets we analyze different ways of constructing binary extended formulations of polyhedral mixed integer sets with bounded integer variables and compare their relative strength with respect to split cuts we show that among all binary extended formulations where each bounded integer variable is represented by a distinct collection of binary variables what we call unimodular extended formulations are the strongest we also compare the strength of some binary extended formulations from the literature finally we study the behavior of branch and bound on such extended formulations and show that branching on the new binary variables leads to significantly smaller enumeration trees in some cases

on the partial realization problem abstract in this paper we take a unified approach to the partial realization problem in which we seek to incorporate ideas from numerical linear algebra most of which were originally developed in other contexts we approach the partial realization problem from several different angles and explore the connections to such topics as factoriza tion of hankel matrices block tridiagonalization generalizations of the lanczos process for biorthogonalization the euclidean algorithm and the principal part con tinued fractions of arne magnus the pade table and the berlekamp masseyalgo rithm in this way we are able to clarify some previous results by rissanen kalman and others and place them in a broader context this leads to several results and concepts which we think are new our analysis is restricted to the scalar case but some definitions and formulations have been rigged to facilitate an extension to the matrix case

on the value of binary expansions for general mixed integer linear programs we study the use of binary variables in reformulating general mixed integer linear programs we show that binary reformulations result in problems for which almost all the binary variables replacing a general integer variable need to be explored during branching we also give computational results on the performance of such reformulations in solving the mixed integer programs which support our theoretical results

iterative solution of linear systems in the 20th century this paper sketches the main research developments in the area of iterative methods for solving linear systems during the 20th century although iterative methods for solving linear systems nd their origin in the early 19th century work by gauss the eld has seen an explosion of activity spurred by demand due to extraordinary technological advances in engineering and sciences the past ve decades have been particularly rich in new developments ending with the availability of large toolbox of specialized algorithms for solving the very large problems which arise in scientic and industrial computational models as in any other scientic area research in iterative methods has been a journey characterized by a chain of contributions building on each other it is the aim of this paper not only to sketch the most signicant of these contributions during the past century but also to relate them to one another c 2000 elsevier science b v all rights reserved

reduced order modeling for time dependent optimization problems with initial value controls this paper presents a new reduced order model rom hessian approximation for linear quadratic optimal control problems where the optimal control is the initial value such problems arise in parameter identification and data assimilation where the parameters to be identified appear in the initial data and also as subproblems in multiple shooting formulations of more general optimal control problems the new rom hessians can provide a substantially better approximation than the underlying basic rom approximation and thus can substantially reduce the computing time needed to solve these optimal control problems the computation of a hessian vector product requires the solution of the linearized state equation with initial value given by the vector to which the hessian is applied followed by the solution of the second order adjoint equation projection based roms of these two linear differential equations are used to generate the hessian approximation while the objective function and gradient are compute

complex shift and invert strategies for real matrices abstract when using an iterative method for solving a generalized nonsymmetric eigenvalue problem of the form fu mu where f and m are real banded matrices it is often desirable to work with the shifted and inverted operator b f m 1 m in order to enhance the eigenvalue separation and improve efficiency unfortunately the shift is generally complex and so is the matrix b the question then is whether it is possible to avoid complex arithmetic while preserving any advantages of bandedness of the pair f m for the classical problem where m i and f is banded complex arithmetic can be avoided by using double shifts i e by working with the real matrix bb whose bandwidth is double that of f this satisfactory solution extends to the case where m is diagonal as well in the generalized case the answer to the above question is negative in the sense that complex arithmetic can be avoided only at the expense of losing the advantage of bandedness one solution is to factor the shifted matrix f m in complex arithmetic but employ real arithmetic subsequently in the iterative procedure this paper examines several approaches and discusses their respective merits under various circumstances

stack filters the median and other rank order operators possess two properties called the threshold decomposition and the stacking properties the first is a limited superposition property which leads to a new architecture for these filters the second is an ordering property which allows an efficient vlsi implementation of the threshold decomposition architecture motivated by the success of rank order filters in a wide variety of applications and by the ease with which they can now be implemented we consider in this paper a new class of filters called stack filters they share the threshold decomposition and stacking properties of rank order filters but are otherwise unconstrained they are shown to form a very large class of easily implemented nonlinear filters which includes the rank order operators as well as all compositions of morphological operators the convergence properties of these filters are investigated using techniques similar to those used to determine root signal behavior of median filters the results obtained include necessary conditions for a stack filter to preserve monotone regions or edges in signals the output distribution for these filters is also found all the stack filters of window width 3 are determined along with their convergence properties among these filters are found two which we have named asymmetric median filters they share all the properties of median filters except that they remove impulses of one sign only that is one removes only positive going edges the other removes only negative going edges while the median filter removes impulses of both signs this investigation of the properties of stack filters thus produces several new useful and easily implemented filters

a generalized gaussian image model for edge preserving map estimation the authors present a markov random field model which allows realistic edge modeling while providing stable maximum a posterior map solutions the model referred to as a generalized gaussian markov random field ggmrf is named for its similarity to the generalized gaussian distribution used in robust detection and estimation the model satisfies several desirable analytical and computational properties for map estimation including continuous dependence of the estimate on the data invariance of the character of solutions to scaling of data and a solution which lies at the unique global minimum of the a posteriori log likelihood function the ggmrf is demonstrated to be useful for image reconstruction in low dosage transmission tomography

a study of symbolic processing and computational aspects in helicopter dynamics even research models of helicopter dynamics often lead to a large number of equations of motion with periodic coefficients and floquet theory is a widely used mathematical tool for dynamic analysis presently three approaches are used in generating the equations of motion these are 1 general purpose symbolic processors such as reduce and macsyma 2 a special purpose symbolic processor dehim dynamic equations for helicopter interpretive models and 3 completely numerical approaches in this paper comparative aspects of the first two purely algebraic approaches are studied by applying reduce and dehim to the same set of problems these problems range from a linear model with one degree of freedom to a mildly non linear multi bladed rotor model with several degrees of freedom further computational issues in applying floquet theory are also studied which refer to 1 the equilibrium solution for periodic forced response together with the transition matrix for perturbations about that response and 2 a small number of eigenvalues and eigenvectors of the unsymmetric transition matrix the study showed the following 1 compared to reduce dehim is far more portable and economical but it is also less user friendly particularly during learning phases 2 the problems of finding the periodic response and eigenvalues are well conditioned

chebyshev acceleration techniques for solving nonsymmetric eigenvalue problems abstract the present paper deals with the problem of computing a few of the eigenvalues with largest or smallest real parts of a large sparse nonsymmetric matrix we present a general acceleration technique based on chebyshev polynomials and discuss its practical application to arnoldi s method and the subspace iteration method the resulting algorithms are compared with the classical ones in a few experiments which exhibit a sharp superiority of the arnoldi chebyshev approach

large scale complex eigenvalue problems abstract the nonsymmetric eigenvalue problem ax bx is discussed with special emphasis on linear algebra theory on algorithms implemented for solving large scale systems and on interpreting complex spectra obtained in applications from physics and engineering present day supercomputers make the required matrix computations feasible especially in view of the increase in memory

finite disjunctive programming characterizations for general mixed integer linear programs in this paper we give a finite disjunctive programming procedure to obtain the convex hull of general mixed integer linear programs milp with bounded integer variables we propose a finitely convergent convex hull tree algorithm that constructs a linear program that has the same optimal solution as the associated milp in addition we combine the standard notion of sequential cutting planes with ideas underlying the convex hull tree algorithm to help guide the choice of disjunctions to use within a cutting plane method this algorithm which we refer to as the cutting plane tree algorithm is shown to converge to an integral optimal solution in finitely many iterations finally we illustrate the proposed algorithm on three well known examples in the literature that require an infinite number of elementary or split disjunctions in a rudimentary cutting plane algorithm

a new implementation of the cmrh method for solving dense linear systems the cmrh method h sadok methodes de projections pour les systemes lineaires et non lineaires habilitation thesis university of lille1 lille france 1994 h sadok cmrh a new method for solving nonsymmetric linear systems based on the hessenberg reduction algorithm numer algorithms 20 1999 303 321 is an algorithm for solving nonsymmetric linear systems in which the arnoldi component of gmres is replaced by the hessenberg process which generates krylov basis vectors which are orthogonal to standard unit basis vectors rather than mutually orthogonal the iterate is formed from these vectors by solving a small least squares problem involving a hessenberg matrix like gmres this method requires one matrix vector product per iteration however it can be implemented to require half as much arithmetic work and less storage moreover numerical experiments show that this method performs accurately and reduces the residual about as fast as gmres with this new implementation we show that the cmrh method is the only method with long term recurrence which requires not storing at the same time the entire krylov vectors basis and the original matrix as in the gmres algorithm a comparison with gaussian elimination is provided

weighted median filters admitting complex valued weights and their optimization this paper introduces the concept of complex weighted median wm filtering admitting complex weighting unlike previous approaches in the literature that only allowed positive real valued weights the new wm structures exhibit improved performance as they exploit the richness of unrestricted complex weighting to this end the newly defined complex wm structures synthesize filtering operations whereas the prior structures could only attain smoothing properties due to the inherent constraints imposed on the weights in order to overcome the two dimensional 2 d search burden associated with the computation of the complex wm two fast robust and very efficient approximations are introduced adaptive optimization algorithms for their design are developed leading to simple lms type weight updates several simulations are shown illustrating the performance of the new complex wm filter structures

a lift and project cutting plane algorithm for mixed 0 1 programs we propose a cutting plane algorithm for mixed 0 1 programs based on a family of polyhedra which strengthen the usual lp relaxation we show how to generate a facet of a polyhedron in this family which is most violated by the current fractional point this cut is found through the solution of a linear program that has about twice the size of the usual lp relaxation a lifting step is used to reduce the size of the lp s needed to generate the cuts an additional strengthening step suggested by balas and jeroslow is then applied we report our computational experience with a preliminary version of the algorithm this approach is related to the work of balas on disjunctive programming the matrix cone relaxations of lovasz and schrijver and the hierarchy of relaxations of sherali and adams

matched median filter for detecting qam signals in this paper the complex matched median filter mmf is developed for qam signal detection it is shown that the mmf is robust against impulsive type noise by combining the mmf and the linear matched filter lmf an extended class of matched filters is introduced these filters combine the desirable properties of mmf and lmf and behave well in varying noise environments computer simulations demonstrate that the proposed detectors give a much smaller symbol error probability than the lmf when the noise has an impulsive component and produces only a slight performance degradation in the case of pure gaussian noise

rational interpolation via orthogonal polynomials abstract relations between rational interpolants and hankel matrices are investigated a modification of a jacobi like algorithm for rational interpolation proposed in 1 is developed the proposed algorithm eliminates all the auxiliary assumptions required to implement the algorithm of 1 in this way it can be used to construct all existing rational interpolants with the cost of o n 2 arithmetic operations where n 1 is the number of data points

qmr smoothing for lanczos type product methods based on three term rrecurrences for the solution of large sparse non hermitian linear systems lanczos type product methods that are based on the lanczos three term recurrence are derived in a systematic way these methods either square the lanczos process or combine it with a local minimization of the residual for them a quasi minimal residual qmr smoothing is proposed that can also be implemented by short term recurrences the practical performance of these methods and the qmr smoothing is demonstrated in a number of numerical examples

ml n bicgstabt a ml n bicgstab variant with a transpose the 1980 idr method wesseling and sonneveld 1980 12 plays an important role in the history of krylov subspace methods it started the research of transpose free krylov subspace methods the ml n bicgstab method yeung 2012 is one of such methods in this paper we present a new ml n bicgstab variant that involves a transpose in its implementation comparison of this new algorithm with the existing ml n bicgstab algorithms and some other krylov subspace algorithms will be presented

lanczos algorithms and field of value rotations for symmetric matrix pencils abstract iterative algorithms for the eigensolution of symmetric pencils of matrices are considered it is shown that the symmetric lanczos algorithm the nonsymmetric lanczos algorithm and the arnoldi algorithm are closely related in this case the applicability of this class of algorithms to indefinite pencils is discussed a new field of values concept is used to describe the symmetric pencil problem then spectral transformation corresponds to a rotation in the complex plane and the inertia count gives the number of eigenvalues corresponding to points in one of two half planes

an implementation of the qmr method based on coupled two term recurrences recently we proposed a new krylov subspace iteration the quasi minimal residual algorithm qmr 1 for solving general nonsingular non hermitian systems of linear equations ax b 1

methods for overcoming breakdown problems in the unsymmetric lanczos reduction method the unsymmetric lanczos reduction ulr method is developed to solve the finite element based solution to the contaminant transport problem the method sometimes suffers from breakdown when at some step division by a pivot which is zero or near zero causes numerical instability in this paper the maximum pivot new start vector method is developed to overcome such breakdowns by constructing a new starting vector with the possible maximum pivot some cases of instability cannot be remedied by this approach pathological breakdowns and the switch method is developed to complete the solution by changing the algorithm to an arnoldi reduction approach investigation of some two dimensional examples and field problems illustrates the efficiency of the methods and substantial time savings over other existing solution methods 1998 john wiley sons ltd

a breakdown free variation of the nonsymmetric lanczos algorithms the nonsymmetric lanczos tridiagonalization algorithm is essentially the gram schmidt biorthogonalization method for generating biorthogonal bases of a pair of krylov subspaces it suffers from breakdown and instability when a pivot at some step is zero or nearly zero which is often the result of mismatch of the two krylov subspaces in this paper we propose to modify one of the two krylov subspaces by introducing a new start vector when a pivot is small this new start vector generates another krylov subspace which we add to the old one in an appropriate way so that the gram schmidt method for the modified subspaces yields a recurrence similar to the lanczos algorithm our method enforces the pivots to be above a certain threshold and can handle both exact breakdown and near breakdown in particular we recover lookahead lanczos algorithms and arnoldi s algorithm as two special cases we also discuss theoretical and practical issues concerning the new start procedure and present a convergence analysis as well as some numerical examples

computational enhancement of an unsymmetric block lanczos algorithm an unsymmetric block lanczos algorithm has been employed for the dynamic analysis of a large system which has arbitrary damping and or repeated or closely spaced eigenvalues in the algorithm development the right and left lanczos vectors are all theoretically biorthogonal to each other however these vectors may lose the biorthogonality owing to cancellation and roundoff errors for the unsymmetric case there can be a breakdown even without numerical errors this paper describes computational techniques which have led to a robust unsymmetric block lanczos algorithm

a rayleigh chebyshev procedure for finding the smallest eigenvalues and associated eigenvectors of large sparse hermitian matrices a procedure is presented for finding a number of the smallest eigenvalues and their associated eigenvectors of large sparse hermitian matrices the procedure a modification of an inverse subspace iteration procedure uses adaptively determined chebyshev polynomials to approximate the required application of the inverse operator on the subspace the method is robust converges with acceptable rapidity and can easily handle operators with eigenvalues of multiplicity greater than one numerical results are shown that demonstrate the utility of the procedure

new look ahead lanczos type algorithms for linear systems a breakdown due to a division by zero can arise in the algorithms for implementing lanczos method because of the non existence of some formal orthogonal polynomials or because the recurrence relationship used is not appropriate such a breakdown can be avoided by jumping over the polynomials involved this strategy was already used in some algorithms such as the mrz and its variants in this paper we propose new implementations of the recurrence relations of these algorithms which only need the storage of a fixed number of vectors independent of the length of the jump these new algorithms are based on horner s rule and on a different way for computing the coefficients of the recurrence relationships moreover these new algorithms seem to be more stable than the old ones and they provide better numerical results numerical examples and comparisons with other algorithms will be given

computational methods for complex eigenproblems in finite element analysis of structural systems with viscoelastic damping treatments in this paper efficient numerical methods to approximate the complex eigenvalues and eigenvectors in non proportional and non viscous systems are presented these methods are specially conceived for practical engineering applications making use of the finite element analysis to determinate the effect that potential damping treatments have on the natural frequencies and mode shapes of structural systems considering the solution of the undamped problem the complex eigenpair is estimated by finite increments using the eigenvector derivatives for non proportional viscous systems with low and medium damping a simple single step technique is presented whose rapidity and accuracy is verified by means of numerical applications for higher damped systems an incremental approach is proposed that keeps the accuracy without significantly increasing the computational time for non viscously damped systems a fast iterative modality is suggested which allows to approximate in an efficient and simple way the complex eigenpair as numerical applications the study of a metallic beam with free layer damping treatment is completed using finite element procedures where the damping material is modelized by an exponential model whose parameters are obtained from curve fitting to experimental data

detection of weak signals in non gaussian noise a locally optimum detector structure is derived for the detection of weak signals in non gaussian environments optimum performance is obtained by employing a zero memory nonlinearity prior to the matched filter that would be optimum for detecting the signal were the noise gaussian the asymptotic detection performance of the locally optimum detector under non gaussian conditions is derived and compared with that for the corresponding detector optimized for operations in gaussian noise numerical results for the asymptotic detection performance are shown for signal detection in noise environments of practical interest

meridian filtering for robust signal processing a broad range of statistical processes is characterized by the generalized gaussian statistics for instance the gaussian and laplacian probability density functions are special cases of generalized gaussian statistics moreover the linear and median filtering structures are statistically related to the maximum likelihood estimates of location under gaussian and laplacian statistics respectively in this paper we investigate the well established statistical relationship between gaussian and cauchy distributions showing that the random variable formed as the ratio of two independent gaussian distributed random variables is cauchy distributed we also note that the cauchy distribution is a member of the generalized cauchy distribution family recently proposed myriad filtering is based on the maximum likelihood estimate of location under cauchy statistics an analogous relationship is formed here for the laplacian statistics as the ratio of laplacian statistics yields the distribution referred here to as the meridian interestingly the meridian distribution is also a member of the generalized cauchy family the maximum likelihood estimate under the obtained statistics is analyzed motivated by the maximum likelihood estimate under meridian statistics meridian filtering is proposed the analysis presented here indicates that the proposed filtering structure exhibits characteristics more robust than that of median and myriad filtering structures the statistical and deterministic properties essential to signal processing applications of the meridian filter are given the meridian filtering structure is extended to admit real valued weights utilizing the sign coupling approach finally simulations are performed to evaluate and compare the proposed meridian filtering structure performance to those of linear median and myriad filtering

a review of formal orthogonality in lanczos based methods krylov subspace methods and their variants are presently the favorite iterative methods for solving a system of linear equations although it is a purely linear algebra problem it can be tackled by the theory of formal orthogonal polynomials this theory helps to understand the origin of the algorithms for the implementation of krylov subspace methods and moreover the use of formal orthogonal polynomials brings a major simplification in the treatment of some numerical problems related to these algorithms this paper reviews this approach in the case of lanczos method and its variants the novelty being the introduction of a preconditioner

adaptive frequency windowing for multifrequency solutions in structural acoustics based on the matrix pade via lanczos algorithm for many problems in structural acoustics it is desired to obtain solutions at many frequencies over a large range in the frequency domain a reduced order multifrequency algorithm based on matrix pade approximation using the matrix pade via lanczos mpvl connection has been previously used to solve both exterior and interior acoustic problems however the method is not guaranteed to give the correct solution across the entire frequency region of interest but only locally around a reference frequency an adaptive frequency windowing scheme is introduced to address this shortcoming for practical application of this method the application of this algorithm to tightly coupled problems in interior structural acoustics is presented

computation of matrix valued formally orthogonal polynomials and applications we present a computational procedure for generating formally orthogonal polynomials associated with a given bilinear hankel form with rectangular matrix valued moments our approach covers the most general case of moments of any size and is not restricted to square moments moreover our algorithm has a built in deflation procedure to handle linearly dependent or almost linearly dependent columns and rows of the block hankel matrix associated with the bilinear form possible singular or close to singular leading principal submatrices of the deflated block hankel matrix are avoided by means of look ahead techniques applications of the computational procedure to eigenvalue computations reduced order modeling the solution of multiple linear systems and the fast solution of block hankel systems are also briefly described

matched median filtering a class of median type filters called matched median filters is defined for the estimation of the information which is carried by the amplitude of a noisy signal these filters form the natural counterpart of linear matched filters in the class of median type filters and are maximum likelihood estimators if the noise is biexponential matched median filters are defined both for baseband and passband systems their statistical properties are analyzed and simulation results presented

galerkin eigenvector approximations how close are galerkin eigenvectors to the best approximation available out of the trial subspace under a variety of conditions the galerkin method gives an approximate eigenvector that approaches asymptotically the projection of the exact eigenvector onto the trial subspace and this occurs more rapidly than the underlying rate of convergence of the approximate eigenvectors both orthogonal galerkin and petrov galerkin methods are considered here with a special emphasis on nonselfadjoint problems thus extending earlier studies by chatelin babuska and osborn and knyazev consequences for the numerical treatment of elliptic pdes discretized either with finite element methods or with spectral methods are discussed new lower bounds to the sep of a pair of operators are developed as well

on the facets of mixed integer programs with two integer variables and two constraints in this paper we consider an infinite relaxation of the mixed integer linear program with two integer variables nonnegative continuous variables and two equality constraints and we give a complete characterization of its facets we also derive an analogous characterization of the facets of the underlying finite integer program

fir median hybrid filters this paper introduces a new generalized class of median type filters which we call fir median hybrid fmh filters the input signal x n is filtered with m linear phase fir filters and the output of the fmh filter is the median of the outputs of the fir filters the statistical properties of the fmh filters are analyzed for input signals with gaussian double exponential and uniform density functions it is shown that fmh filters have essentially the same statistical properties and same type of root signals as the standard median sm filters fmh filters preserve corner points and ramps and attenuate impulsive type noise components effectively an interesting subclass of the fmh filters requiring only one scaling multiplier two additions and three compare swap operations irrespective of the filter length is introduced in the examples speed improvement by a factor of 450 is obtained over an otherwise approximately equivalent sm filter

a look ahead lanczos algorithm for unsymmetric matrices abstract the two sided lanczos algorithm is known to suffer from serious breakdowns these occur when the associated moment matrix does not permit triangular factorization we modify the algorithm slightly so that it corresponds to using a 2 x 2 pivot in triangular factorization whenever a 1 x 1 pivot would be dangerous the incidence of breakdown is greatly reduced the price paid is that the tridiagonal matrix produced by the algorithm now has bumps whenever a 2 x 2 pivot is used author

chva tal closures for mixed integer programming problems chvatal introduced the idea of viewing cutting planes as a system for proving that every integral solution of a given set of linear inequalities satisfies another given linear inequality this viewpoint has proven to be very useful in many studies of combinatorial and integer programming problems the basic ingredient in these cutting plane proofs is that for a polyhedronp and integral vectorw if max wx x p wx integer t thenwx t is valid for all integral vectors inp we consider the variant of this step where the requirement thatwx be integer may be replaced by the requirement that bar wx be integer for some other integral vector bar w the cutting plane proofs thus obtained may be seen either as an abstraction of gomory s mixed integer cutting plane technique or as a proof version of a simple class of the disjunctive cutting planes studied by balas and jeroslow our main result is that for a given polyhedronp the set of vectors that satisfy every cutting plane forp with respect to a specified subset of integer variables is again a polyhedron this allows us to obtain a finite recursive procedure for generating the mixed integer hull of a polyhedron analogous to the process of repeatedly taking chvatal closures in the integer programming case these results are illustrated with a number of examples from combinatorial optimization our work can be seen as a continuation of that of nemhauser and wolsey on mixed integer cutting planes

the lanczos biorthogonalization algorithm and other oblique projection methods for solving large unsymmetric systems many powerful methods for solving systems of equations can be regarded as projection methods most of the projection methods known for solving linear systems are orthogonal projection methods but little attention has been given to the class of nonorthogonal or oblique projection methods which is particularly attractive for large nonsymmetric systems the purpose of the paper is to present some methods in the general setting of oblique projection methods and to give some theoretical results some experiments comparing the various algorithms are reported

mir closures of polyhedral sets we study the mixed integer rounding mir closures of polyhedral sets the mir closure of a polyhedral set is equal to its split closure and the associated separation problem is np hard we describe a mixed integer programming mip model with linear constraints and a non linear objective for separating an arbitrary point from the mir closure of a given mixed integer set we linearize the objective using additional variables to produce a linear mip model that solves the separation problem exactly using a subset of these additional variables yields an mip model which solves the separation problem approximately with an accuracy that depends on the number of additional variables used our analysis yields an alternative proof of the result of cook et al 1990 that the split closure of a polyhedral set is again a polyhedron we also discuss a heuristic to obtain mir cuts based on our approximate separation model and present some computational results

numerical solutions for large sparse quadratic eigenvalue problems abstract we study the quadratic eigenvalue problem a b 2c x 0 where a b and c are symmetric real n n matrices and a c are positive definite we propose an efficient numerical algorithm to compute a few of the smallest positive eigenvalues of the problem and their associated eigenvectors the new algorithm includes two parts the first part gives iterative methods which can be used to compute the smallest positive eigenvalue we develop a globally linearly convergent basic iteration and two locally quadratically convergent iterations the second part uses the nonequivalence deflation technique this technique allows us to transform the original problem to a new problem with different a b and c the new problem has the same eigenvalues as the old problem except that the smallest positive eigenvalue of the old problem is replaced by zero therefore the second smallest positive eigenvalue of the old problem becomes the smallest positive one for the new problem then the above proposed iterative methods can be applied again to find the second smallest positive eigenvalue proceeding in this way we can find out the 3rd 4th smallest positive eigenvalues our algorithm utilizes the symmetry and positivity of the given matrices and avoids computing the undesired complex conjugate eigenvalues under some mild conditions is efficient and reliable the above process can also be used to find a few of the largest negative eigenvalues

a transpose free quasi minimal residual algorithm for non hermitian linear systems the biconjugate gradient method bcg for solving general non hermitian linear systems ax b and its transpose free variant the conjugate gradients squared algorithm cgs both typically exhib

morphological filters part ii their relations to median order statistic and stack filters this paper extends the theory of median order statistic os and stack filters by using mathematical morphology to analyze them and by relating them to those morphological erosions dilations openings closings and open closings that commute with thresholding the max min representation of os filters is introduced by showing that any median or other os filter is equal to a maximum of erosions moving local minima and also to a minimum of dilations moving local maxima thus os filters can be computed by a closed formula that involves a max min on prespecified sets of numbers and no sorting stack filters are established as the class of filters that are composed exactly of a finite number of max min operations the kernels of median os and stack filters are collections of input signals that uniquely represent these filters due to their translation invariance the max min functional definitions of these nonlinear iliters is shown to be equivalent to a maximum of erosions by minimal with respect to a signal ordering kernel elements and also to a minimum of dilations by minimal kernel elements of dual filters the representation of stack filters based on their minimal kernel elements is proven to be equivalent to their representation based on irreducible sum of products expressions of boolean functions it is also shown that median filtering and its iterations of any signal by convex 1 d windows is bounded below by openings and above by closings a signal is a root fixed point of the median iff it is a root of both an opening and a closing the open closing and clos opening yield median roots in one pass suppress impulse noise similarly to the median can discriminate between positive and negative noise impulses and are computationally less complex than the median some similar results are obtained for 2 d median filtering

analysis of the properties of median and weighted median filters using threshold logic and stack filter representation the deterministic properties of weighted median wm filters are analyzed threshold decomposition and the stacking property together establish a unique relationship between integer and binary domain filtering the authors present a method to find the weighted median filter which is equivalent to a stack filter defined by a positive boolean function because the cascade of wm filters can always be expressed as a single stack filter this allows expression of the cascade of wm filters as a single wm filter a direct application is the computation of the output distribution of a cascade of wm filters the same method is used to find a nonrecursive expansion of a recursive wm filter as applications of theoretical results several interesting deterministic and statistical properties of wm filters are derived

computing eigenvalues eigenvectors and frequency responses of structures with non proportional damping abstract this paper presents algorithms to compute eigenvalues eigenvectors and frequency response matrix of a linear structure with non proportional damping the method is based on derivatives of repeated or unrepeated complex eigenvalues and complex eigenvectors since eigenvectors are not unique for a repeated eigenvalue a special algorithm is developed to select the correct eigenvector for a given non proportional damping the advantage of the proposed method is that it only requires the knowledge of natural frequencies and mode shapes of the undamped structure the validity of this method is established by comparing numerical results from this algorithm to exact answers

a transpose free quasi minimal residual variant of the cors method for solving non hermitian linear systems recently the conjugate a orthogonal residual squared cors method is often competitive and superior to other krylov subspace methods for solving complex non hermitian linear systems from many realistic problems however like the conjugate gradient squared cgs method the cors method often exhibits irregular convergence behavior with wild oscillations in the residual norm to try and remedy this problem we develop a transpose free quasi minimal residual tfqmr variant of the cors method which leads to smooth convergence curves with a convergence rate similar to the cors method we also give convergence results for this new method finally numerical examples are reported to illustrate the effectiveness of the proposed method

an explicit formula for lanczos polynomials abstract the lanczos algorithm for tridiagonalizing a given matrix a generates a sequence of approximating matrices a p that can naturally be obtained as restrictions to subspaces the eigenvalues of these approximating matrices are well known to be good approximations to the extreme eigenvalues of a in this paper we produce explicit formulas for the characteristics polynomials of the a p in terms of the eigenvalues of a these formulas can be used to explain heuristically why these approximations are often quite good at present we have no concrete analytic argument that explains the quality of the approximation the main result draws on the formal relationship between the lanczos algorithm and pade approximations to the moment generating function of a this result is one of the few analytic results available for the unsymmetric lanczos algorithm

optimizing over the split closure the polyhedron defined by all the split cuts obtainable directly i e without iterated cut generation from the lp relaxation p of a mixed integer program mip is termed the elementary or rank 1 split closure of p this paper deals with the problem of optimizing over the elementary split closure this is accomplished by repeatedly solving the following separation problem given a fractional point say x find a rank 1 split cut violated by x or show that none exists following caprara and letchford 17 we formulate this separation problem as a nonlinear mixed integer program that can be treated as a parametric mixed integer linear program pmilp with a single parameter in the objective function and the right hand side we develop an algorithmic framework to deal with the resulting pmilp by creating and maintaining a dynamically updated grid of parameter values and use the corresponding mixed integer programs to generate rank 1 split cuts our approach was implemented in the coin or framework using cplex 9 0 as a general purpose mip solver we report our computational results on well known benchmark instances from miplib 3 0 and several classes of structured integer and mixed integer problems our computational results show that rank 1 split cuts close more than 98 of the duality gap on 15 out of 41 mixed integer instances from miplib 3 0 more than 75 of the duality gap can be closed on an additional 10 instances the average gap closed over all 41 instances is 72 78 in the pure integer case rank 1 split cuts close more than 75 of the duality gap on 13 out of 24 instances from miplib 3 0 on average rank 1 split cuts close about 72 of the duality gap on these 24 instances we also report results on several classes of structured problems capacitated versions of warehouse location single source facility location p median fixed charge network flow multi commodity network design with splittable and unsplittable flows and lot sizing the fraction of the integrality gap closed varies for these problem classes between 100 and 67 we also gathered statistics on the average coefficient size absolute value of the disjunctions generated they turn out to be surprisingly small