a rigorous computational comparison of alternative solution methods for the generalized assignment problem statistical experimental design and analysis is a cornerstone for scientific inquiry that is rarely applied in reporting computational testing this approach is employed to study the relative performance characteristics of the four leading algorithmic and heuristic alternatives to solve the linear cost generalized assignment problem lcgap against a newly developed heuristic variable depth search heuristic vdsh in assessing the relative effectiveness of the prominent solution methodologies and vdsh under the effects of various problem characteristics we devise a carefully designed experimentation of state of the art implementations through a rigorous statistical analysis we identify the most efficient method s for commonly studied lcgaps and determine the effect on solution time and quality of problem class and size

exponential error bounds for random codes in the arbitrarily varying channel random codes for the arbitrarily varying channel are investigated the code ensemble is restricted to increase only exponentially with codeword length a quantity called key rate is used as a measure of the rate of increase the reliability function for such codes is related to the reliability function for codes with unlimited key rate explicit results are obtained in some examples of practical interest

a branch and bound method for the fixed charge transportation problem in this paper we develop a new conditional penalty for the fixed charge transportation problem this penalty is stronger than both the driebeek penalties and the lagrangean penalties of cabot and erenguc computational testing shows that the use of these penalties leads to significant reductions in enumeration and solution times for difficult problems in the size range tested we also study the effect of problem parameters on the difficulty of the problem the ratio of fixed charges to variable costs the shape of the problem arc density in the underlying network and fixed charge arc density are shown to have a significant effect on problem difficulty for problems involving up to 40 origins and 40 destinations

a fast algorithm for apes and capon spectral estimation in this paper we derive a novel implementation of some very computationally demanding matched filter bank based spectral estimators namely the amplitude and phase estimator apes the amplitude spectrum capon asc estimator and the power spectrum capon psc estimator filter bank based spectral estimation methods that adopt data dependent filter banks can provide spectra characterized by a significantly improved resolution compared to classical approaches however the computational complexity of the currently available implementation algorithms is extremely high a novel technique is introduced that provides efficient algorithms for the computation of the apes asc and psc spectra the proposed method is based on suitable displacement representations of all pertinent data matrices that are subsequently utilized for the computation of the associated complex valued polynomials the computational complexity of the proposed algorithms is lower than all relevant existing methods

exponential parameter estimation in the presence of known components and noise in the determination of the natural modes of an electromagnetic scatterer the measured time series will contain desired information noise and quite often known transient components introduced by the excitation source or measuring equipment this paper describes a linearly constrained total least squares lctls prony method for extracting the exponential model parameters from observed transient data for such problems the tls criterion yields better parameter estimates than ls moreover the incorporation of known signal information via constraints leads to even greater improvements in performance mathematical connections between lctls prony and a tls variation of time series deflation tsd are used to derive constraints for higher order excitation poles also we use tsd concepts to derive numerically superior data transformations for lctls simulation studies involving idealized test data and synthetic scattering response data of a perfectly conducting sphere demonstrate the advantages of the method

linear prediction approach to direction estimation of cyclostationary signals in multipath environment we investigate the estimation of the directions of arrival doa of closely spaced narrowband cyclostationary signals in the presence of multipath propagation by exploiting the spatial and temporal properties of most communication signals we propose a new cyclic forward backward linear prediction fblp approach for coherent signals impinging on a uniform linear array ula in the proposed algorithm the evaluation of the cyclic array covariance matrix is avoided and the difficulty of choosing the optimal time lag parameter is alleviated as a result the proposed approach has two advantages 1 the computational load is relatively reduced and 2 the robustness of estimation is significantly improved the performance of the proposed approach is confirmed through numerical examples and it is shown that this approach is superior in resolving the closely spaced coherent signals with a small length of array data and at relatively low signal to noise ratio snr

new efficient ls and svd based techniques for high resolution frequency estimation new least squares and singular value decomposition based methods for the estimation of the frequencies of complex sinusoids in white noise are presented the methods are based on a new symmetric prediction problem that has some very useful properties leading to algorithms that have considerably reduced complexity the new symmetric predictor is superior in performance as compared to the well known symmetric smoother and has a performance comparable to other well known methods finally a new ls based method which combines the new prediction technique with the fblp method is proposed this method performs slightly better than the fblp offering at the same time a significant computational saving as a by product in the derivation of the new methods is the establishment of some useful properties concerning the eigenstructure of hermitian and persymmetric matrices

coal a new heuristic approach for solving the fixed charge problem computational results abstract computational results are presented for a new heuristic algorithm for solving large general fixed charge problems based on lagrangian relaxation and cost allocation heuristics these results are contrasted with results using adjacent extreme point heuristics and branch and bound mixed integer programming general facility location and capacitated warehouse location problems as well as production planning problems with fixed and variable work force considerations are used the many test problems evaluated include problems with a large number of fixed charge and continuous variables the new algorithm demonstrates superiority over alternative methods for solving these problems

arbitrarily varying channels with constrained inputs and states random coding theorems are proved for discrete memoryless arbitrarily varying channels avcs with constraints on the transmitted codewords and channel state sequences two types of constraints are considered peak i e required for each n length sequence almost surely and average over the message set or over an ensemble for peak constraints on the codewords and on the channel state sequences the avc is shown to have a strong random coding capacity if the codewords and or the channel state sequences are constrained in the average sense the avcs do not possess strong capacities only epsilon capacities are shown to exist

techniques for the efficient evaluation of two dimensional autocorrelation functions the evaluation of the autocorrelation of multidimensional sequences using the fft is investigated the bidimensional case is treated in detail not only because it is of special interest for the applications but also because the reasoning used for this case can be applied to higher dimension signals the analysis confirms that the use of the proposed frequency domain techniques leads to significant computation time savings

a tabu search heuristic procedure for the fixed charge transportation problem abstract we develop a tabu search approach for the fixed charge transportation fct problem using recency based and frequency based memories together with two strategies for each of the intermediate and long term memory processes making use of a network based implementation of the simplex method as the local search method our approach is evaluated computationally on randomly generated problems of different sizes and of different ranges of magnitude of fixed costs relative to variable costs comparisons are made with two leading methods previously proposed one in the category of exact methods and one in the category of heuristic methods our tabu search procedure obtains optimal or near optimal solutions more than a thousand times faster than the exact solution algorithm for simple problems and thoroughly dominates the exact algorithm on all dimensions for more complex problems compared to the heuristic approach on very small and easy test problems the tabu search procedure required about the same amount of solution time and found solutions at least as good however for larger problems and for problems with higher fixed relative to variable costs the tabu search procedure was 3 4 times faster than the competing heuristic and found significantly better solutions in all cases

analysis of modified smi method for adaptive array weight control the authors characterize the performance of the diagonally loaded sample matrix inverse smi algorithm versus the number k of snapshots used in the covariance matrix estimate by providing o 1 k statistics bias and variance of the array weights output powers and output power ratios such as signal to interference noise ratio sinr and inr the approach accommodates wideband signals monte carlo simulations verify the theoretical analysis

nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance the least squares ls can be used for nonlinear autoregressive nar and nonlinear autoregressive moving average narma parameter estimation however for nonlinear cases the ls results in biased parameter estimation due to its assumption that the independent variables are noise free the total least squares tls is another method that can used for nonlinear parameter estimation to increase the accuracy of the ls because it specifically accounts for the fact that the independent variables are noise corrupted tls has its own limitations however mainly because it is difficult for the method to isolate noise from the signal components we present a new method that is based on minimization of hypersurface distance for accurate parameter estimation for nar and narma models computer simulation examples show that the new method results in far more accurate nar and narma model parameter estimates than do either the ls and tls with noise that is either white or colored and retains its high accuracy even when the signal to noise ratio snr is as low as 10 db

lms like ar modeling in the case of missing observations this paper presents a new recursive algorithm for the time domain reconstruction and spectral estimation of uniformly sampled signals with missing observations an autoregressive ar modeling approach is adopted the ar parameters are estimated by optimizing a mean square error criterion the optimum is reached by means of a gradient method adapted to the nonperiodic sampling the time domain reconstruction is based on the signal prediction using the estimated model the power spectral density is obtained using the estimated ar parameters the development of the different steps of the algorithm is discussed in detail and several examples are presented to demonstrate the practical results that can be obtained the spectral estimates are compared with those obtained by known ar estimators applied to the same signals sampled periodically we note that this algorithm can also be used in the case of nonstationary signals

sinusoidal frequency estimation by signal subspace approximation an efficient fourier transform based method that avoids eigenvector computation is proposed for approximating the signal subspace the resulting signal subspace estimate can be used directly to define a music type frequency estimator or as a very good initial guess in context with adaptive or iterative eigenvector computation schemes at low signal to noise ratios the approximation yields better results than exact music it is also more robust than music against overestimating the number of sinusoids some variations of the basic method are briefly discussed

improved penalties for fixed cost linear programs using lagrangean relaxation the most commonly used penalty in branch and bound approaches to integer programming is the driebeek tomlin penalty it has been used successfully in solving fixed cost linear programs by kennington and unger and by barr glover and klingman it is well known that the driebeek tomlin penalty can be derived from a lagrangean relaxation of the integer programming problem we show however that the lagrangean relaxation for fixed cost problems not only yields the driebeek tomlin penalty but two penalties which sometimes dominate it we show the strength of the new penalties by solving a series of text problems and comparing the number of nodes generated on the branch and bound tree and the total computer time needed to solve each problem

weighted least squares design and characterization of complex fir filters the paper presents two novel weighted least squares methods for the design of complex coefficient finite impulse response fir filters to attain specified arbitrary multiband magnitude and linear or arbitrary phase responses these methods are computationally efficient requiring only the solution of a toeplitz system of n linear equations for an n length filter that can be obtained in o n sup 2 operations illustrative filter design examples are presented

efficient algorithms for the solution of block linear systems with toeplitz entries abstract this paper is concerned with the development of fast solvers for block linear systems with toeplitz entries two algorithms are described the first utilizes block permutations to unravel the nesting structure of the associated matrix the second has a block schur type format and features highly parallel potential

surface characterization using frequency diverse scattering measurements and regularity models accurate classification and characterization of image regions is often an important goal for the users of coherent imaging systems such as synthetic aperture radar sar many surfaces measured by remote sensing systems can be stochastically described by a regularity model this parametric point process model describes a 1 d surface in terms of the mean and variance of the interscatterer distances variations of these parameters can describe scatterer distributions ranging from totally random to nearly periodic under certain conditions a closed form approximation to the mean power spectrum of finite length data intervals exists the estimation of model parameters from measured spectra can then be cast as an optimization problem in which the total squared error between the approximation and the simple periodogram is minimized we examine the general performance limitations of such an optimization procedure determining how approximation error signal to noise ratio and frequency sampling rate affect the feasibility and accuracy of parameter estimation we determine under what conditions the approximation may be used we find that parameter estimation is feasible at a frequency sampling rate that is well below that suggested by the power spectral density psd this suggests that it is possible to obtain parameter estimates by comparing sparse narrow band frequency measurements to the psd of the point process and thereby obtain information about the surface on subresolution scales

capacity and decoding rules for classes of arbitrarily varying channels the capacity of an arbitrarily varying channel avc is considered for deterministic codes with the average probability of error criterion and typically subject to at state constraint first sufficient conditions are provided that enable relatively simple decoding rules such as typicality maximum mutual information and minimum distance to attain capacity then the possibly noisy or channels and group adder channels are studied in detail for the former the capacity is explicitly determined and shown to be attainable by minimum distance decoding next for a large class of addictive avcs in addition to providing an intuitively suggestive simplification of the general avc capacity formula it is proven that capacity can be attained by a universal decoding rule finally the effect of random state selections on capacity is studied the merits and limitations of a previous mutual information game approach are also discussed

state constraints for the multiple access arbitrarily varying channel a discrete multiple access arbitrarily varying channel with two transmitters and one receiver is considered it has recently been shown that if such a channel is symmetrizable then the capacity region has an empty interior it is shown that if the channel is subject to a suitable state constraint then the capacity region can contain certain open rectangles and thereby possess a nonempty interior even if the channel is symmetrizable a new weak converse under a state constraint is also proved these results are then used to establish the capacity region of the two user adder channel under state constraint 1 2

computationally efficient capon and apes based coherence spectrum estimation the coherence spectrum is of notable interest as a bivariate spectral measure in a variety of application and the topic has lately attracted interest with the recent formulation of several high resolution data adaptive estimators in this work we further this development with the presentation of computationally efficient implementations of the capon and apes based msc estimators the presented implementations furthers the recent development of exploiting the estimators inherently low displacement rank of the necessary products of toeplitz like matrices to include also the required cross correlation covariance matrices for the mentioned coherence algorithms numerical simulations together with theoretical complexity measures illustrate the performance of the proposed implementations

computationally efficient two dimensional capon spectrum analysis we present a computationally efficient algorithm for computing the 2 d capon 1969 spectral estimator the implementation is based on the fact that the 2 d data covariance matrix will have a toeplitz block toeplitz structure with the result that the inverse covariance matrix can be expressed in closed form by using a special case of the gohberg heinig 1974 formula that is a function of strictly the forward 2 d prediction matrix polynomials furthermore we present a novel method based on a 2 d lattice algorithm to compute the needed forward prediction matrix polynomials and discuss the difference in the so obtained 2 d spectral estimate as compared with the one obtained by using the prediction matrix polynomials given by the whittle wiggins robinson 1963 1965 algorithm numerical simulations illustrate the improved resolution as well as the clear computational gain in comparison to both the well known classical implementation and the method published by liu et al see ieee trans aerosp electron syst vol 34 p 1314 19 1998

application of higher order spectral analysis to cubically nonlinear system identification in this study digital higher order spectral analysis and frequency domain volterra system models are utilized to yield a practical methodology for the identification of weakly nonlinear time invariant systems up to third order the primary focus is on consideration of random excitation of nonlinear systems and thus the approach makes extensive use of higher order spectral analysis to determine the frequency domain volterra kernels which correspond to linear quadratic and cubic transfer functions although the volterra model is nonlinear in terms of its input it is linear in terms of its unknown transfer functions thus a least squares approach is used to determine the optimal in a least squares sense set of linear quadratic and cubic transfer functions of particular practical note is the fact that the approach of this paper is valid for non gaussian as well as gaussian random excitation it may also be utilized for multitone inputs the complexity of the problem addressed in this paper arises from two principal causes 1 the necessity to work in a 3d frequency space to describe cubically nonlinear systems and 2 the necessity to characterize the non gaussian random excitation by computing higher order spectral moments up to sixth order a detailed description of the approach used to determine the nonlinear transfer functions including considerations necessary for digital implementation is presented

a path relinking approach with ejection chains for the generalized assignment problem the generalized assignment problem is a classical combinatorial optimization problem known to be np hard it can model a variety of real world applications in location allocation machine assignment and supply chains the problem has been studied since the late 1960s and computer codes for practical applications emerged in the early 1970s we propose a new algorithm for this problem that proves to be more effective than previously existing methods the algorithm features a path relinking approach which is a mechanism for generating new solutions by combining two or more reference solutions it also features an ejection chain approach which is embedded in a neighborhood construction to create more complex and powerful moves computational comparisons on benchmark instances show that the method is not only effective in general but is especially effective for types d and e instances which are known to be very difficult

a genetic algorithm for the two stage supply chain distribution problem associated with a fixed charge this paper considers a two stage distribution problem of a supply chain that is associated with a fixed charge two kinds of cost are involved in this problem a continuous cost that linearly increases with the amount transported between a source and a destination and secondly a fixed charge that incurs whenever there exists a transportation of a non zero quantity between a source and a destination the objective criterion is the minimisation of the total cost of distribution a genetic algorithm ga that belongs to evolutionary search heuristics is proposed and illustrated the proposed methodology is evaluated for its solution quality by comparing it with the approximate and lower bound solutions thus the comparison reveals that the ga generates better solution than the approximation method and is capable of providing solution either equal or closer to the lower bound solution of the problem

hybridising tabu search with optimisation techniques for irregular stock cutting sequential meta heuristic implementations for the irregular stock cutting problem have highlighted a number of common problems the literature suggests a consensus that it is more efficient to allow configurations with overlapping pieces in the solution space and to penalise these in the evaluation function however depending on the severity of the penalty this relaxation results in a tendency to converge toward infeasible solutions or to seek out feasible solutions at the expense of overall quality a further problem is encountered in defining a neighbourhood search strategy that can deal with the infinite solution space inherent in the irregular stock cutting problem the implementation in this paper adopts a hybrid tabu search approach that incorporates two very different optimisation routines that utilise alternative neighbourhoods to address the described problems

solving zero one mixed integer programming problems using tabu search we describe a tabu search ts approach for solving general zero one mixed integer programming mip problems that exploits the extreme point property of zero one solutions specialized choice rules and aspiration criteria are identified for the problems expressed as functions of integer infeasibility measures and objective function values the first level ts mechanisms are then extended with advanced level strategies and learning we also look at probabilistic measures in this framework and examine how the learning tool target analysis ta can be applied to identify better control structures and decision rules computational results are reported on a portfolio of multiconstraint knapsack problems our approach is designed to solve thoroughly general 0 1 mip problems and thus contains no problem domain specific knowledge yet it obtains solutions for the multiconstraint knapsack problem whose quality rivals and in some cases surpasses the best solutions obtained by special purpose methods that have been created to exploit the special structure of these problems

superfast approximative implementation of the iaa spectral estimate in this correspondence we develop superfast approximative one dimensional algorithms for the computationally efficient implementation of the recent iterative adaptive approach iaa spectral estimate the proposed methods are based on rewriting the iaa algorithm with suitable gohberg semencul representations solving the resulting linear systems of equations using the preconditioned conjugate gradient method where a novel preconditioning is applied using an incomplete factorization of the toeplitz matrix numerical simulations illustrate the efficiency of both the proposed preconditioning as well as the overall algorithm offering a computational reduction of up to two orders of magnitude as compared to our recently proposed efficient and exact iaa implementation

characterizing three dimensional wavefields with high resolution spectra three dimensional spectra frequency x wavenumber y wavenumber or f kx ky spectra can be used to determine the frequency content velocity and direction of waves entering an array of receivers this information is important in detecting aliasing problems understanding coherent noise designing arrays and determining parameters for coherent noise filters because of the limited spatial dimensions of most arrays the discrete fourier transform produces an estimate of the three dimensional 3 d spectrum with severe wavenumber distortion we extend a 2 d hybrid spectral estimation method to three dimensions by combining a temporal fourier transform with a spatial 2 d maximum entropy spectral estimation technique the method produces f kx ky spectra with higher wavenumber resolution and less spectral distortion than corresponding 3 d fourier spectra the 2 d maximum entropy spectral estimation algorithm uses a sequence of fourier transforms to extrapolate the estimated autocorrelation function of the data

significance of group delay functions in spectrum estimation a method of spectrum estimation using group delay functions is proposed this method exploits the additive property of the fourier transform ft phase to extract spectral information of the signal in the presence of noise the phase is generally featureless due to random polarity and wrappings but the group delay function can be processed to derive significant information such as peaks in the spectral envelope in the resulting spectral estimates obtained the resolution properties of the periodogram estimate are preserved while the variance is reduced variance caused by the sidelobe leakage due to windows and additive noise are significantly reduced even in the spectral estimate obtained using a single realization of the observation peak resolution is primarily dictated by the size of the data window the method works even for high noise levels the results of this procedure are demonstrated through two illustrative examples estimation of sinusoids in noise and estimation of the narrowband autoregressive process in noise

a new frequency estimation method for equally and unequally spaced data spectral estimation is an important classical problem that has received considerable attention in the signal processing literature in this contribution we propose a novel method for estimating the parameters of sums of complex exponentials embedded in additive noise from regularly or irregularly spaced samples the method relies on kronecker s theorem for hankel operators which enables us to formulate the nonlinear least squares problem associated with the spectral estimation problem in terms of a rank constraint on an appropriate hankel matrix this matrix is generated by sequences approximating the underlying sum of complex exponentials unequally spaced sampling is accounted for through a proper choice of interpolation matrices the resulting optimization problem is then cast in a form that is suitable for using the alternating direction method of multipliers admm the method can easily include either a nuclear norm or a finite rank constraint for limiting the number of complex exponentials the usage of a finite rank constraint makes in contrast to the nuclear norm constraint the method heuristic in the sense that the problem is non convex and convergence to a global minimum can not be guaranteed however we provide a large set of numerical experiments that indicate that usage of the finite rank constraint nevertheless makes the method converge to minima close to the global minimum for reasonably high signal to noise ratios hence essentially yielding maximum likelihood parameter estimates moreover the method does not seem to be particularly sensitive to initialization and performs substantially better than standard subspace based methods

tabu search for the multilevel generalized assignment problem abstract the multilevel generalized assignment problem mgap differs from the classical gap in that agents can perform tasks at more than one efficiency level important manufacturing problems such as lot sizing can be formulated as mgaps however the large number of variables in the related 0 1 integer program makes the use of commercial optimization packages impractical in this paper we present a heuristic approach to the solution of the mgap which consists of a novel application of tabu search ts our ts method employs neighborhoods defined by ejection chains that produce moves of greater power without significantly increasing the computational effort

the capacity of the arbitrarily varying channel revisited positivity constraints a well known result of r ahlswede 1970 asserts that the deterministic code capacity of an arbitrarily varying channel avc under the average error probability criterion either equals its random code capacity or else is zero a necessary and sufficient condition is identified for deciding between these alternative namely the capacity is zero if and only if the avc is symmetrizable the capacity of the avc is determined with constraints on the transmitted codewords as well as on the channel state sequences and it is demonstrated that it may be positive but less than the corresponding random code capacity a special case of the results resolves a weakened version of a fundamental problem of coding theory

correlations in the bond future market we analyze the time series of overnight returns for the bund and btp futures exchanged at liffe london the overnight returns of both assets are mapped onto a one dimensional symbolic dynamics random walk the bond walk during the considered period october 1991 january 1994 the bund future market opened earlier than the btp future one the crosscorrelations between the two bond walks as well as estimates of the conditional probability show that they are not independent however each walk can be modeled by means of a trinomial probability distribution monte carlo simulations confirm that it is necessary to take into account the bivariate dependence in order to properly reproduce the statistical properties of the real world data various investment strategies have been devised to exploit the prior information obtained by the aforementioned analysis

a hybrid heuristic for the generalized assignment problem abstract the objective of this study is to develop a hybrid heuristic hh for solving the generalized assignment problem gap and conduct a computational comparison with the leading alternative heuristic approaches hh is designed around the two best known heuristics heuristic gap hgap and variable depth search heuristic vdsh previous performance characteristic studies have shown that hgap dominates vdsh in terms of solution cpu time while vdsh obtains solutions of 13 to 300 better quality within a reasonable time the main idea in this paper is to hybridize the two approaches such that the inherent values of both heuristics are realized

two dimensional multivariate parametric models for radar applications part ii maximum entropy extensions for hermitian block matrices in a series of two papers a new class of parametric models for two dimensional multivariate matrix valued space time adaptive processing is introduced this class is based on the maximum entropy extension and or completion of partially specified matrix valued hermitian covariance matrices in both the space and time dimensions this first paper considers the more restricted class of toeplitz hermitian covariance matrices that model stationary clutter if the clutter is stationary only in time then we deal with a toeplitz block matrix whereas clutter that is stationary in time and space is described by a toeplitz block toeplitz matrix we first derive exact expressions for this new class of 2 d models that act as approximations for the unknown true covariance matrix second we propose suboptimal but computationally simpler relaxed 2 d time varying autoregressive models ldquorelaxationsrdquo that directly use the non toeplitz hermitian sample covariance matrix the high efficiency of these parametric models is illustrated by simulation results using true ground clutter covariance matrices provided by the darpa kassper dataset 1 which is a trusted phenomenological airborne radar model and a complementary afrl dataset

a hybrid tabu search branch bound approach to solving the generalized assignment problem a new approach for solving the generalized assignment problem gap is proposed that combines the exact branch bound approach with the heuristic strategy of tabu search ts to produce a hybrid algorithm for solving gap the algorithm described uses commercial software to solve sub problems generated by the ts guiding strategy the ts approach makes use of the concept of referent domain optimisation and introduces novel add drop strategies in addition the linear programming relaxation of gap that forms part of the branch bound approach is itself helpful in suggesting which variables might take binary values computational results on benchmark test instances are presented and compared with results obtained by the standard branch bound approach and also several other heuristic approaches from the literature the results show the new algorithm performs competitively against the alternatives and is able to find some new best solutions for several benchmark instances

a tabu search heuristic for the generalized assignment problem abstract this paper considers the generalized assignment problem gap it is a well known np hard combinatorial optimization problem that is interesting in itself and also appears as a subproblem in other problems of practical importance a tabu search heuristic for the gap is proposed the algorithm uses recent and medium term memory to dynamically adjust the weight of the penalty incurred for violating feasibility the most distinctive features of the proposed algorithm are its simplicity and its flexibility these two characteristics result in an algorithm that compared to other well known heuristic procedures provides good quality solutions in competitive computational times computational experiments have been performed in order to evaluate the behavior of the proposed algorithm

directed enumeration method in image recognition the article is devoted to the problem of image recognition in real time applications with a large database containing hundreds of classes the directed enumeration method as an alternative to exhaustive search is examined this method has two advantages first it could be applied with measures of similarity which do not satisfy metric properties chi square distance kullback leibler information discrimination etc second the directed enumeration method increases recognition speed even in the most difficult cases which seem to be very important in practical terms in these cases many neighbors are located at very similar distances in this paper we present the results of an experimental study of the directed enumeration method with comparison of color and gradient orientation histograms in solving the problem of face recognition with well known datasets essex feret it is shown that the proposed method is characterized by increased computing efficiency of automatic image recognition 3 12 times in comparison with a conventional nearest neighbor classifier

high spectral resolution time frequency distribution kernels a new class of time frequency distribution tfd kernels is introduced members in this class satisfy the desirable tfd properties and simultaneously provide local autocorrelation functions laf that are amenable to high frequency resolution modeling techniques it is shown that members of the proposed class are product kernels fast implementation multiplication free kernels recursive kernels and optimum kernels with respect to autoterm localization

use of penalties in a branch and bound procedure for the fixed charge transportation problem abstract the fixed charge transportation problem fctp is an extension of the classic transportation problem in which a fixed cost is incurred for every supply point that is used in the solution a branch and bound procedure which requires the use of two subproblems a classic transportation problem and a knapsack problem can be used to solve the fctp unlike fixed change problems with the fctp there is a many to one correspondence between the continuous variables of the transportation problem and the fixed charge variables of the knapsack problem this paper incorporates penalties into the branch and bound procedure in order to improve the solution times also an adjacent extreme point heuristic is presented that finds a near optimal solution quickly

on the family of ml spectral estimates for mixed spectrum identification a recently developed point spectrum identification procedure based on a family of ar and ml spectral estimates is exploited to arrive at a mixed spectrum identification procedure to this end a variety of properties of the ar and ml estimates as a function of model order are described these properties relate to amplitude convergence resolution and a characterization of the ar spectral artifact which is used to arrive at improved continuous spectral estimates a variety of examples are presented

on the capacity region of the discrete additive multiple access arbitrarily varying channel the discrete additive multiple access arbitrarily varying channel avc with two senders and one receiver is considered necessary and sufficient conditions are given for its deterministic code average probability of error capacity region under a state constraint to have a nonempty interior in the case that no state constraint is present the capacity region is characterized exactly in the case of the noiseless mod 2 adder avc using state constraint function l s s and subject to a state constraint l less than or equal to 0 13616917 the capacity region is shown to be a 45 degree triangle whose legs have length 1 h l where h denotes the binary entropy function

bounds for estimation of complex exponentials in unknown colored noise we consider the problem of estimating the parameters of complex exponentials in the presence of complex additive gaussian noise with unknown covariance bounds are derived for the accuracy of jointly estimating the parameters of the exponentials and the noise we first present an exact cramer rao bound crb for this problem and specialize it for the cases of circular gaussian processes and autoregressive processes we also derive an approximate expression for the crb which is related to the conditional likelihood function numerical evaluation of these bounds provides some insights on the effect of various signal and noise parameters on the achievable estimation accuracy

a weighted linear prediction method for near field source localization this paper proposes a low computational cost method for the near field narrowband source localization problem which does not require multidimensional search or high order statistics the proposed method is based on the second order statistics sos of the outputs of a uniform linear array ula more precisely the range and angle parameters are estimated through a weighted linear prediction lp algorithm applied to a properly chosen array output correlation sequence detailed performance analysis and derivation of the optimal weightings are provided simulation results are finally presented to validate the theoretical analysis results and to assess the performance of the proposed method

genetic algorithms for a supply management problem mip recombination vs greedy decoder two variants of genetic algorithm ga for solving the supply management problem with lower bounded demands smpld are proposed and experimentally tested the smpld problem consists in planning the shipments from a set of suppliers to a set of customers minimizing the total cost given lower and upper bounds on shipment sizes lower bounded consumption and linear costs for opened deliveries the first variant of ga uses the standard binary representation of solutions and a new recombination operator based on the mixed integer programming mip techniques the second ga is based on the permutation representation and a greedy decoder our experiments indicate that the ga with mip recombination compares favorably to the other ga and to the mip solver cplex 9 0 in terms of cost of obtained solutions the ga based on greedy decoder is shown to be the most robust in finding feasible solutions

reduction methods of type 2 uncertain variables and their applications to solid transportation problem uncertainty theory is a branch of mathematics for dealing with realistic uncertainties arising out of complexity changeability and non decidability of practical environments an uncertain variable is defined as a function from the uncertainty space to the set of real numbers and is characterized by an uncertainty distribution this paper proposes the definition of type 2 uncertain variables within the framework of uncertainty theory through introduction of generalized uncertain measures and focuses on more complex twofold uncertainties some uncertainty reduction methods associated with type 2 uncertain variables are also proposed for convenience of applicability including reduction of optimistic value pessimistic value and expected value moreover four classes of type 2 uncertain variables are reduced to type 1 uncertain variables with specific uncertainty distributions type 2 uncertain optimization methods are applied to solving the fixed charge solid transportation problem with the type 2 uncertain parameters where the solution methods are also provided for the proposed models finally numerical experiments are implemented to demonstrate application and sensitivity analysis of the proposed approaches

scaling in the market of futures the price time series of the italian government bonds btp futures is studied by means of scaling concepts originally developed for random walks in statistical physics the series of overnight price differences is mapped onto a one dimensional random walk the bond walk the analysis of the root mean square fluctuation function and of the auto correlation function indicates the absence of both short and long range correlations in the bond walk a simple monte carlo simulation of a random walk with trinomial probability distribution is able to reproduce the main features of the bond walk

a branch and price algorithm for the generalized assignment problem the generalized assignment problem examines the maximum profit assignment of jobs to agents such that each job is assigned to precisely one agent subject to capacity restrictions on the agents a new algorithm for the generalized assignment problem is presented that employs both column generation and branch and bound to obtain optimal integer solutions to a set partitioning formulation of the problem

efficient implementation of iterative adaptive approach spectral estimation techniques this paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach iaa for uniformly sampled one and two dimensional data sets considering both the complete data case and the cases when the data sets are missing samples either lacking arbitrary locations or having gaps or periodically reoccurring gaps by exploiting the method s inherent low displacement rank together with the development of suitable gohberg semencul representations and the use of data dependent trigonometric polynomials the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude numerical simulations together with theoretical complexity measures illustrate the achieved performance gain

a generalized assignment problem with special ordered sets a polyhedral approach we study a generalized assignment problem that arises in production scheduling in which special ordered sets of type ii appear naturally in the formulation we derive three families of facet defining valid inequalities and we show that they cut off all infeasible vertices of the lp relaxation we also give the complete facetial description for a particular case we then use the inequalities as cuts in a branch and cut scheme and we report computational results that demonstrate the superiority of branch and cut over branch and bound on this class of problems

on the application of parametric models to the transient analysis of resonant and multiband antennas the transient responses of a resonant antenna and a fractal multiband antenna both formed by conducting surfaces are calculated the surfaces are modeled by planar triangular patches and the study is carried out by solving the time domain electric field integral equation td efie linear parametric modeling techniques are applied to considerably reduce the computation time numerical results are compared with experimental measurements

point process model of 1 f noise vs a sum of lorentzians we present a simple point process model of 1 f noise covering different values of the exponent the signal of the model consists of pulses or events the interpulse interevent interarrival recurrence or waiting times of the signal are described by the general langevin equation with the multiplicative noise and stochastically diffuse in some interval resulting in a power law distribution our model is free from the requirement of a wide distribution of relaxation times and from the power law forms of the pulses it contains only one relaxation rate and yields 1 f spectra in a wide range of frequencies we obtain explicit expressions for the power spectra and present numerical illustrations of the model further we analyze the relation of the point process model of 1 f noise with the bernamont surdin mcwhorter model representing the signals as a sum of the uncorrelated components we show that the point process model is complementary to the model based on the sum of signals with a wide range distribution of the relaxation times in contrast to the gaussian distribution of the signal intensity of the sum of the uncorrelated components the point process exhibits asymptotically a power law distribution of the signal intensity the developed multiplicative point process model of 1 f noise may be used for modeling and analysis of stochastic processes in different systems with the power law distribution of the intensity of pulsing signals

exploring relaxation induced neighborhoods to improve mip solutions given a feasible solution to a mixed integer programming mip model a natural question is whether that solution can be improved using local search techniques local search has been applied very successfully in a variety of other combinatorial optimization domains unfortunately local search relies extensively on the notion of a solution neighborhood and this neighborhood is almost always tailored to the structure of the particular problem being solved a mip model typically conveys little information about the underlying problem structure this paper considers two new approaches to exploring interesting domain independent neighborhoods in mip the more effective of the two which we call relaxation induced neighborhood search rins constructs a promising neighborhood using information contained in the continuous relaxation of the mip model neighborhood exploration is then formulated as a mip model itself and solved recursively the second which we call guided dives is a simple modification of the mip tree traversal order loosely speaking it guides the search towards nodes that are close neighbors of the best known feasible solution extensive computational experiments on very difficult mip models show that both approaches outperform default cplex mip and a previously described approach for exploring mip neighborhoods local branching with respect to several different metrics the metrics we consider are quality of the best integer solution produced within a time limit ability to improve a given integer solution of both good and poor quality and time required to diversify the search in order to find a new solution

fuzzy multiobjective modeling and optimization for one shot multiattribute exchanges with indivisible demand the modern economy involves a variety of marketplaces and the internet has led to the development of a new efficient marketplace the one shot multiattribute exchange it is an important decision problem for a matchmaker or broker to achieve the optimal trade matching in one shot multiattribute exchanges however to the best of our knowledge there has been little work on this issue under fuzzy environments this paper proposes an optimal matching approach for one shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations first we employ fuzzy set theory to represent the traders orders with fuzzy information and then put forward a calculation method of the matching degree based on the improved fuzzy information axiom second on the basis of the matching degree we construct a fuzzy multiobjective programming model for one shot multiattribute exchanges with indivisible demand afterward the credibility measure is introduced to convert the model into a crisp one the crisp model belongs to a class of multiobjective nonlinear general assignment problems and has np hard complexity in order to solve the crisp model effectively we develop a problem specified metaheuristic algorithm i e multiobjective discrete differential evolution finally we conduct comprehensive computational experiments on numerical examples to illustrate the application and performance of the proposed model and algorithm

solving the fixed charge problem with lagrangian relaxation and cost allocation heuristics abstract a new heuristic algorithm is presented for solving large general fixed charge problems based on langrangian relaxation and cost allocation heuristics limited computational results indicate the new algorithm provides good solutions to large problems with reasonable computational requirements it demonstrates significant improvement over current methods for solving large general fixed charge problems

hybrid extreme point tabu search abstract we develop a new hybrid tabu search method for optimizing a continuous differentiable function over the extreme points of a polyhedron the method combines extreme point tabu search epts with traditional descent algorithms based on linear programming the tabu search algorithm utilizes both recency based and frequency based memory and oscillates between local improvement and diversification phases the hybrid algorithm iterates between using a descent algorithm to find a local minimum and tabu search to improve locally and then move to a new area of the search space the descent algorithm acts as a form of intensification within the tabu search this algorithm can be used on many important classes of problems in global optimization including bilinear programming multilinear programming multiplicative programming concave minimization and complementarity problems the algorithm is applied to two practical problems the quasistatie multi rigid body contact problem qcp in robotics and the global tree optimization gto problem in machine learning we perform computational experiments on problems of significant real world dimensions the smallest machine learning problem tested has on the order of 10485 extreme points the results show our method obtains high quality solutions both by comparison with the embedded descent algorithm and by comparison to a version of tabu search that does not make use of descent

proper orthogonal decomposition and its applications part i theory abstract in view of the increasing popularity of the application of proper orthogonal decomposition pod methods in engineering fields and the loose description of connections among the pod methods the purpose of this paper is to give a summary of the pod methods and to show the connections among these methods firstly the derivation and the performance of the three pod methods karhunen loeve decomposition kld principal component analysis pca and singular value decomposition svd are summarized then the equivalence problem is discussed via a theoretical comparison among the three methods the equivalence of the matrices for processing the objective functions the optimal basis vectors the mean square errors and the asymptotic connections of the three methods are demonstrated and proved when the methods are used to handle the pod of discrete random vectors

an adaptive correlator receiver for direct sequence spread spectrum communication this paper presents a novel receiver for direct sequence spread spectrum signals over channels containing interference and multipath the receiver employs an adaptive correlator that jointly detects the transmitted data removes interference and compensates for multipath the optimum correlation vector is derived by determining the wiener vector that minimizes the mean squared error mse between the transmitted data bit and the correlator output for an additive white gaussian noise awgn channel the optimal correlation vector is the spreading sequence used by the transmitter for interference and multipath channels the optimal correlation vector will suppress the interference and combine the multipath while optimizing the detection of the transmitted data bit the paper presents analytical and simulation results which illustrate the bit error rate ber performance of the receiver in multipath and narrowband interference additionally simulation results are presented illustrating the convergence performance of the receiver when the tap weights are adjusted using either the least mean square lms or recursive least squares rls adaptive algorithms

semiparametric bayesian inference for time series with mixed spectra summary a bayesian analysis is presented of a time series which is the sum of a stationary component with a smooth spectral density and a deterministic component consisting of a linear combination of a trend and periodic terms the periodic terms may have known or unknown frequencies the advantage of our approach is that different features of the data such as the regression parameters the spectral density unknown frequencies and missing observations are combined in a hierarchical bayesian framework and estimated simultaneously a bayesian test to detect deterministic components in the data is also constructed by using an asymptotic approximation to the likelihood the computation is carried out efficiently using the markov chain monte carlo method in o mn operations where n is the sample size and m is the number of iterations we show empirically that our approach works well on real and simulated samples

approximate subspace based iterative adaptive approach for fast two dimensional spectral estimation in this paper we devise a new approach for fast implementation of two dimensional 2 d iterative adaptive approach iaa using single or multiple snapshots our underlying idea is to apply the subspace methodology in this nonparametric technique by performing the iaa on the dominant singular vectors extracted from the singular value decomposition svd or higher order svd of the multidimensional observations in doing so 2 d iaa is approximately realized by multiple steps of 1 d iaa implying that computational attractiveness is achieved particularly for large data size number of grid points and or snapshot number algorithms based on matrix and tensor operations are developed and their implementation complexities are analyzed computer simulations are also included to compare the proposed approach with the state of the art techniques in terms of resolution probability spectral estimation performance and computational requirement

effective algorithm and heuristic for the generalized assignment problem we present new branch and bound algorithm for the generalized assignment problem a standard subgradient method sm used at each node of the decision tree to solve the lagrangian dual provides an upper bound our key contribution in this paper is a new heuristic applied at each iteration of the sm which tries to exploit the solution of the relaxed problem by solving a smaller generalized assignment problem the feasible solution found is then subjected to a solution improvement heuristic we consider processing the root node as a lagrangian heuristic computational comparisons are made with new existing methods

estimation of the parameters of an arma signal model based on an orthogonal search an iterative procedure for estimating the parameters of an arma signal model input not accessible is empirically compared to the results of several other algorithms the arma identifier reported includes inherent order estimation the empirical results indicate unbiased estimates with less variance than those obtained by the other methods

complexity of multiplication with vectors for structured matrices abstract fast algorithms for computing the product with a vector are presented for a number of classes of matrices whose properties relate to the properties of toeplitz vandermonde or cauchy matrices these matrices are defined using the concept of displacement of a matrix and also for their inverses all the actions which are not dependent upon the coordinates of the input vector are singled out in a separate preprocessing stage the proposed algorithms are based on new representations of these matrices involving factor circulants

fast lcmv based methods for fundamental frequency estimation recently optimal linearly constrained minimum variance lcmv filtering methods have been applied to fundamental frequency estimation such estimators often yield preferable performance but suffer from being computationally cumbersome as the resulting cost functions are multimodal with narrow peaks and require matrix inversions for each point in the search grid in this paper we therefore consider fast implementations of lcmv based fundamental frequency estimators exploiting the estimators inherently low displacement rank of the used toeplitz like data covariance matrices using as such either the classic time domain averaging covariance matrix estimator or if aiming for an increased spectral resolution the covariance matrix resulting from the application of the recent iterative adaptive approach iaa the proposed exact implementations reduce the required computational complexity with several orders of magnitude but as we show further computational savings can be obtained by the adoption of an approximative iaa based data covariance matrix estimator reminiscent of the recently proposed quasi newton iaa technique furthermore it is shown how the considered pitch estimators can be efficiently updated when new observations become available the resulting time recursive updating can reduce the computational complexity even further the experimental results show that the performances of the proposed methods are comparable or better than that of other competing methods in terms of spectral resolution finally it is shown that the time recursive implementations are able to track pitch fluctuations of synthetic as well as real life signals

fixed charge transportation problem facets of the projection polyhedron in this paper we consider the well known fixed charge transportation problem to send any flow from source si to destination tj we incur a unit variable shipping cost of cij and a fixed cost fij here we study the structure of the projection polyhedron of this problem in the space of 0 1 variables associated with fixed charges and we develop several classes of valid inequalities and derive conditions under which they are facet defining in some cases if the conditions are not satisfied we show how they can be lifted to define facets several heuristics for generating and adding these facets are presented using these results we develop a computationally effective algorithm for solving the problem the computational results clearly indicate the usefulness of this approach

on the deterministic code capacity of the multiple access arbitrarily varying channel a method that allows one to decide whether or not the capacity region of a multiple access arbitrarily varying channel avc has a nonempty interior is discussed using the method of types and an approach different from j h jahn ibid vol 27 no 3 p 212 226 1981 this problem is partially solved the notion of symmetrizability for the two user avc as an extension of the same notion for the single user avc is considered it is shown that if a multiple access avc is symmetrizable then its capacity region has an empty interior for the two user avc this means that at least one and perhaps both users cannot reliably transmit information across the channel more importantly it is shown that if the channel is suitably nonsymmetrizable then the capacity region has a nonempty interior and both users can reliably transmit information across the channel the proofs rely heavily on a complicated decoding rule conditions under which simpler multiple message decoding techniques might suffice are therefore examined in particular conditions under which the universal maximal mutual information decoding rule will be effective are given

spectral analysis of nonuniformly sampled data a new approach versus the periodogram we begin by revisiting the periodogram to explain why arguably the plain least squares periodogram lsp is preferable to the ldquoclassicalrdquo fourier periodogram from a data fitting viewpoint as well as to the frequently used form of lsp due to lomb and scargle from a computational standpoint then we go on to introduce a new enhanced method for spectral analysis of nonuniformly sampled data sequences the new method can be interpreted as an iteratively weighted lsp that makes use of a data dependent weighting matrix built from the most recent spectral estimate because this method is derived for the case of real valued data which is typically more complicated to deal with in spectral analysis than the complex valued data case it is iterative and it makes use of an adaptive i e data dependent weighting we refer to it as the real valued iterative adaptive approach riaa lsp and riaa are nonparametric methods that can be used for the spectral analysis of general data sequences with both continuous and discrete spectra however they are most suitable for data sequences with discrete spectra i e sinusoidal data which is the case we emphasize in this paper

displacement structure theory and applications in this survey paper we describe how strands of work that are important in two different fields matrix theory and complex function theory have come together in some work on fast computational algorithms for matrices with what we call displacement structure in particular a fast triangularization procedure can be developed for such matrices generalizing in a striking way an algorithm presented by schur 1917 j reine angew math 147 1917 pp 205 232 in a paper on checking when a power series is bounded in the unit disc this factorization algorithm has a surprisingly wide range of significant applications going far beyond numerical linear algebra we mention among others inverse scattering analytic and unconstrained rational interpolation theory digital filter design adaptive filtering and state space least squares estimation

on the deterministic code capacity of the two user discrete memoryless arbitrarily varying general broadcast channel with degraded message sets an inner bound on the deterministic code capacity region of the two user discrete memoryless arbitrarily varying general broadcast channel avgbc was characterized by jahn assuming that the common message capacity is nonzero however he did not indicate how one could decide whether the latter capacity is positive csiszaacuter and narayan s result for the single user arbitrarily varying channel avc establishes the missing part in jahn s characterization nevertheless being based on ahlswede s elimination technique jahn s characterization is not applicable for symmetrizable channels under state constraint here the various notions of symmetrizability for the two user broadcast avc are defined sufficient non symmetrizability condition that renders the common message capacity of the avgbc positive is identified using an approach different from jahn s the decoding rules we use establish an achievable region under state and input constraints for the family of degraded message sets codes over the avgbc

efficient algorithms for adaptive capon and apes spectral estimation in this paper fast algorithms for adaptive capon and amplitude and phase estimation apes methods for spectral analysis of time varying signals are derived fast stable nonrecursive formulae are derived based on time shifting properties of the pertinent variables as a consequence efficient frequency domain recursive least squares rls based as well as fast rls based algorithms for the adaptive estimation of the power spectra are developed stability issues of the frequency domain estimators are considered and stabilization procedures are proposed the computational complexity of the proposed algorithms is lower than relevant existing methods the performance of the proposed algorithms is demonstrated through extensive simulations

wave oriented signal processing of dispersive time domain scattering data phase space data processing is receiving increased attention because or its potential for furnishing new discriminants relating to classification and identification of targets and other scattering environments primary emphasis has been on time frequency processing because of its impact on transient especially wideband short pulse excitations here we investigate the windowed fourier transform the wavelet transform and model based superresolution algorithms within the context of a fully quantified and calibrated test problem investigated by us previously two dimensional 2 d short pulse plane wave scattering by a finite periodic array of perfectly conducting coplanar flat strips because the forward problem has been fully calibrated and parametrized some quantitative measures can be assigned with respect to the tradeoffs of these time frequency algorithms yielding tentative performance assessments of the tested processing algorithms

complex coefficient nonrecursive digital filter design using a least squares method a method for the design of complex coefficient nonrecursive filters is described in this method a mean square error is formulated in a quadratic form the filter coefficients are obtained by solving a system of linear equations using computationally robust and efficient algorithms examples are presented to demonstrate the efficacy of our method

a multiplier adjustment method for the generalized assignment problem we describe a branch and bound algorithm for the generalized assignment problem in which bounds are obtained from a lagrangian relaxation with the multipliers set by a heuristic adjustment method the algorithm was tested on a large sample of small random problems and a number of large problems derived from a vehicle routing application computation times were reasonable in all cases and the branch and bound trees generated had nearly two orders of magnitude fewer nodes than for competing algorithms although comparison of running times on different machines is difficult the multiplier adjustment method appears to be about one order of magnitude faster than the best previously existing algorithms for this problem

a reduced cost iterated local search heuristic for the fixed charge transportation problem the fixed charge transportation problem fctp is a generalization of the transportation problem where an additional fixed cost is paid for sending a flow from an origin to a destination we propose an iterated local search heuristic based on the utilization of reduced costs for guiding the restart phase the reduced costs are obtained by applying a lower bounding procedure that computes a sequence of nondecreasing lower bounds by solving a three index mathematical formulation of the problem strengthened with valid inequalities the proposed method was tested on two sets of benchmark instances from the literature the first set was used to evaluate the state of the art heuristics for the problem the proposed heuristic was able to provide new best knownupper bounds on all 124 open instances on the second set of instances which was recently introduced for testing the currently best exact method for the problem the new heuristic was able to provide provably good upper bounds within short computing times