interpolation of precipitation under topographic influence at different time scales 1 in this paper new methodologies for interpolating rainfall data in individual time intervals ranging from a day to a year using gaussian copulas and unsymmetrical v copulas with a variety of treatments of altitude as an exogenous variable are described for shorter time aggregations zeros were treated as censored variables for each selected time step the marginal distributions of precipitation amounts were modeled using nonparametric density estimators while the spatial dependence structures were estimated using a maximum likelihood methodology the methodology was compared to other common geostatistical interpolators such as ordinary kriging and external drift kriging several measures of bias and error structure have been used to assess the efficacy of the methods in a range of comparative split sampling studies the data set chosen for the study comprises daily precipitation time series over 41 years in three regions in germany measured at more than 1200 locations over a large area 126 144 km2 among the many findings in the paper the ones that stand out are i correlation between precipitation and topography increases with the length of time interval and is significantly improved by directional smoothing of topography ii the copula methods are superior to kriging methods in terms of quality of interpolation bias and uncertainty estimation iii the treatment of zeros as censored variables improves interpolation quality for daily and pentad values monthly and annual data in this region present no dry periods iv the copula methods yield full conditional distributions of estimates at a target point in an interval improving substantially on the simple uncertainty estimates derived from kriging and v the gauss copula in particular performs best overall in terms of computational efficiency combined with useful error profiles in the interpolations and of all methods is the most realistic in its error estimates

algorithm and architecture for a galois field multiplicative arithmetic processor we present a new algorithm for generic multiplicative computations of the form ab c in gf p sup m including multiplication inversion squaring and division the algorithm is based on solving a sequence of congruences that are derived from the theory of grobner bases in modules over the polynomial ring gf p x its corresponding hardware and software architectures can be successfully used in applications such as error control coding and cryptography we describe a versatile circuit associated with the algorithm for the most important case p 2 the same hardware can be used for a range of field sizes thus permitting applications in which different levels of error control or of security are required by different classes of user the operations listed are all performed by the hardware in the same number of clock cycles which prevents certain side channel attacks the loss in performance by having 2m iterations for multiplication is compensated by the full parameterization of the galois field and the ability to perform division and multiplication in parallel

high speed low complexity systolic designs of novel iterative division algorithms in gf 2 sup m we extend the binary algorithm invented by stein and propose novel iterative division algorithms over gf 2 sup m for systolic vlsi realization while algorithm ebg is a basic prototype with guaranteed convergence in at most 2m 1 iterations its variants algorithms ebd and ebdf are designed for reduced complexity and fixed critical path delay respectively we show that algorithms ebd and ebdf can be mapped to parallel in parallel out systolic circuits with low area time complexities of o m sup 2 loglogm and o m sup 2 respectively compared to the systolic designs based on the extended euclid s algorithm our circuits exhibit significant speed and area advantages

copula based geostatistical models for groundwater quality parameters 1 groundwater quality parameters exhibit considerable spatial variability geostatistical methods including the assessment of variograms are usually used to characterize this variability copulas offer an interesting opportunity to describe dependence structures for multivariate distributions bivariate empirical copulas can be used as an alternative to variograms and covariance functions for the description of the spatial variability rank correlations of these copulas express the strength of the dependence independently of the marginal distributions and thus offer an alternative to the variograms empirical copulas for four quality parameters chloride sulfate ph and nitrate obtained from a large scale groundwater quality measurement network in baden wurttemberg germany are calculated they indicate that the spatial dependence structure of the investigated parameters is not gaussian two theoretical copula based models are presented in this paper a gaussian and a non gaussian bootstrap based statistical tests using stochastic simulation of the multivariate distributions are used to investigate the appropriateness of the models according to the test results the gaussian copula is rejected for most of the parameters while the non gaussian alternative is not rejected in most cases

systolic gaussian elimination over gf p with partial pivoting a systolic architecture is proposed for the triangularization by means of the gaussian elimination algorithm of large dense n n matrices over gf p where p is a prime number the solution of large dense linear systems over gf p is the major computational step in various algorithms issuing from arithmetic number theory and computer algebra the proposed architecture implements the elimination with partial pivoting although the operation of the array remains purely systolic extension of the array to the complete solution of a linear system ax b over gf p is also considered

influence of soil structure and root water uptake strategy on unsaturated flow in heterogeneous media 1 we analyze the combined effects of the spatial variability of soil hydraulic properties and the water uptake by plant roots on unsaturated water flow for this analysis we use a simplified macroscopic root water uptake model which is usually applied only for homogeneous or layered soil and therefore we also determine whether it is applicable for multidimensional heterogeneous media analytical solutions for mean and variance of pressure head first order second moment approximations in layered media and numerical solutions of two dimensional 2 d autocorrelated multi gaussian and non multi gaussian parameter fields are analyzed for steady state and transient flow conditions for non gaussian topological features that have little influence on the mean and the variance of the pressure field if root water uptake is ignored we test whether the influence is significant if root water uptake is accounted for the results reveal that in field structures with large patches of coarse material local regions with pressure head values at the wilting point develop these are surrounded by wet material without a compensation mechanism for local stress the global transpiration demand is not met if local wilting occurs various compensation mechanisms are tested that depend respectively on the saturation the relative conductivity or a strategy where the deficit in the global uptake rate is equally distributed to unstressed locations the strategies lead to a global actual transpiration rate at the potential value and attenuate the formation of locally wilted areas wilted regions can however still occur and may be an artifact of the simplified model concept as root soil interactions are neglected therefore simplified macroscopic models for root water uptake should be used with caution in heterogeneous media

multivariate copula analysis toolbox mvcat describing dependence and underlying uncertainty using a bayesian framework we present a newly developed multivariate copula analysis toolbox mvcat which includes a wide range of copula families with different levels of complexity mvcat employs a bayesian framework with a residual based gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties the contribution of this paper is threefold a providing a bayesian framework to approximate the predictive uncertainties of fitted copulas b introducing a hybrid evolution markov chain monte carlo mcmc approach designed for numerical estimation of the posterior distribution of copula parameters and c enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties we show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima the proposed method however addresses this limitation and improves describing the dependence structure mvcat also enables evaluation of uncertainties relative to the length of record which is fundamental to a wide range of applications such as multivariate frequency analysis

projected sequential quadratic programming methods in this paper we introduce and analyze a class of optimization methods called projected sequential quadratic programming sqp methods for the solution of optimization problems with nonlinear equality constraints and simple bound constraints on parts of the variables such problems frequently arise in the numerical solution of optimal control problems projected sqp methods combine the ideas of projected newton methods and sqp methods they use the simple projection onto the set defined by the bound constraints and maintain feasibility with respect to these constraints the iterates are computed using an extension of sqp methods and require only the solution of the linearized equality constraint global convergence of these methods is enforced using a constrained merit function and an armijo like line search we discuss global and local convergence properties of these methods the identification of active indices and we present numerical examples for an optimal control problem governed by a nonlinear h

improved spatial prediction a combinatorial approach 1 this paper presents a combinatorial approach for improving spatial predictions first copulas are used to interpolate a spatially distributed point rainfall field to a uniform spatial grid it is observed that results vary substantially depending on the parameters chosen for interpolation leading to the hypothesis that it may be advantageous to estimate copula parameters locally or to combine local and global copula predictions it is found that by modifying the method of forecast combinations prediction errors in the spatial interpolation of rainfall can be reduced although this method of combining predictions is applied in the context of rainfall interpolation using local and global copula predictions it can be used on other spatial variables and interpolation methods

multivariate type g matern stochastic partial differential equation random fields for many applications with multivariate data random field models capturing departures from gaussianity within realizations are appropriate for this reason we formulate a new class of multivariate non gaussian models based on systems of stochastic partial differential equations with additive type g noise whose marginal covariance functions are of matern type we consider four increasingly flexible constructions of the noise where the first two are similar to existing copula based models in contrast with these the last two constructions can model non gaussian spatial data without replicates computationally efficient methods for likelihood based parameter estimation and probabilistic prediction are proposed and the flexibility of the models suggested is illustrated by numerical examples and two statistical applications less

generation of canonical m sequences and dual bases an expression for the shift necessary to obtain a canonical m sequence is obtained in terms of the coefficients of the primitive polynomial that generates this m sequence this shift for obtaining a canonical m sequence is also directly related to the shift for obtaining the location of the span 100 0 in the m sequence in addition a recursion is developed for obtaining the dual basis elements of the basis expressing gf q sup m over gf q canonical m sequences and dual bases are important in the design of sequences for spread spectrum multiple access systems as well as in the design of general decoder structures using bit serial encoders

double basis multiplicative inversion over gf 2 sup m inversion over galois fields is much more difficult than the corresponding multiplication efficient computation of inverses in gf 2 sup m is considered by solving a set of linear equations over the ground field gf 2 the proposed algorithm uses two separate bases for the representation of its input and output elements and has low computational complexity the algorithm is also suitable for hardware implementation using vlsi technologies

a comparison of vlsi architecture of finite field multipliers using dual normal or standard bases three different finite field multipliers are presented 1 a dual basis multiplier due to e r berlekamp 1982 the massey omura normal basis multiplier and 3 the scott tavares peppard standard basis multiplier these algorithms are chosen because each has its own distinct features that apply most suitably in particular areas they are implemented on silicon chips with nmos technology so that the multiplier most desirable for vlsi implementation can readily be ascertained

streamflow prediction in ungauged catchments using copula based dissimilarity measures 1 there are many procedures in the available literature to perform prediction in ungauged basins commonly the euclidean metric is used as a proxy of the hydrologic dissimilarity here we propose a procedure to find a metric on the basis of dissimilarity measures that are estimated from pairwise empirical copula densities of runoff a metric is then defined in an transformed space of basin descriptors whose parameterization is obtained with a variance reducing technique a hydrologic model was run in an ungauged basin with sets of global parameters obtained from the k nearest neighboring donor basins using various metrics to take into account the uncertainty of its parameterization hydrologic model parameters were regionalized with a multiscale parameter regionalization technique whose transfer function parameters were found via calibration the streamflow in an ungauged basin was found as an ensemble streamflow prediction to account for the uncertainties of the transfer function parameters as well as those to define the metric this technique was applied in 38 german basins ranging in size from 70 to 4000 km2 for each basin a number of catchment descriptors and several climatic indices were quantified e g mean slope aspect shape factor mean elevation and mean monthly temperature in january among others daily streamflow time series correspond to the period from 1961 to 2000 simulated daily discharge was validated with a jackknife cross validation technique nash sutcliffe efficiencies obtained in this way ranged between 0 76 and 0 86 these results suggested that the proposed technique would produce reasonable results in ungauged basins

systolic multipliers for finite fields gf 2 m two systolic architectures are developed for performing the product sum computation ab c in the finite field gf 2m of 2melements where a b and c are arbitrary elements of gf 2m the first multiplier is a serial in serial out one dimensional systolic array while the second multiplier is a parallel in parallel out two dimensional systolic array the first multiplier requires a smaller number of basic cells than the second multiplier the second multiplier heeds less average time per computation than the first multiplier if a number of computations are performed consecutively to perform single computations both multipliers require the same computational time in both cases the architectures are simple and regular and possess the properties of concurrency and modularity as a consequence they are well suited for use in vlsi systems

bit serial systolic divider and multiplier for finite fields gf 2 sup m a systolic structure for bit serial division over the field gf 2 sup m is developed consideration is given to avoid global data communications and dependency of the time step duration on m this is important for applications where the value of m is large the divider requires only three basic processors and one simple control signal and its circuit and time complexities are proportional to m sup 2 and m respectively it does not depend on the irreducible polynomial and can be expanded easily moreover with m additional simple processors a bit serial systolic multiplier is developed which uses part of the divider structure this is advantageous from the implementation point of view as both the divider and multiplier can be fabricated on a single chip resulting in a reduction of area

geostatistical interpolation using copulas 1 in many applications of geostatistical methods the dependence structure of the investigated parameter is described solely with the variogram or covariance functions which are susceptible to measurement anomalies and implies the assumption of gaussian dependence moreover the kriging variance respects only observation density data geometry and the variogram model to address these problems we borrow the idea from copulas to depict the dependence structure without the influence of the marginal distribution the methodology and basic hypotheses for application of copulas as geostatistical methods are discussed and the gaussian copula as well as a non gaussian copula are used in this paper copula parameters are estimated using a division of the observations into multipoint subsets and a subsequent maximization of the corresponding likelihood function the interpolation is carried out with two different copulas where the expected and median values are calculated from the copulas conditioned with the nearby observations the full conditional copulas provide the estimation distributions for the unobserved locations and can be used to define confidence intervals which depend on both the observation geometry and values observations of a large scale groundwater quality measurement network in baden wurttemberg are used to demonstrate the methodology five groundwater quality parameters chloride nitrate ph sulfate and dissolved oxygen are investigated all five parameters show non gaussian dependence the copula based interpolation results of the five parameters are compared to the results of conventional ordinary and indicator kriging different statistical measures including mean squared error relative differences and probability scores are used to compare cross validation and split sampling results of the interpolation methods the non gaussian copulas give better results than the geostatistical interpolations validation of the confidence intervals shows that they are more realistic than the estimation variances obtained by ordinary kriging

a pde sensitivity equation method for optimal aerodynamic design the use of gradient based optimization algorithms in inverse design is well established as a practical approach to aerodynamic design a typical procedure uses a simulation scheme to evaluate the objective function from the approximate states and its gradient then passes this information to an optimization algorithm once the simulation scheme cfd flow solver has been selected and used to provide approximate function evaluations there are several possible approaches to the problem of computing gradients one popular method is to differentiate the simulation scheme and compute design sensitivities that are then used to obtain gradients although this black box approach has many advantages in shape optimization problems one must compute mesh sensitivities in order to compute the design sensitivity in this paper we present an alternative approach using the pde sensitivity equation to develop algorithms for computing gradients this approach has the advantage that mesh sensitivities need not be computed moreover when it is possible to use the cfd scheme for both the forward problem and the sensitivity equation then there are computational advantages an apparent disadvantage of this approach is that it does not always produce consistent derivatives however for a proper combination of discretization schemes one can showasymptotic consistencyunder mesh refinement which is often sufficient to guarantee convergence of the optimal design algorithm in particular we show that when asymptotically consistent schemes are combined with a trust region optimization algorithm the resulting optimal design method converges we denote this approach as thesensitivity equation method the sensitivity equation method is presented convergence results are given and the approach is illustrated on two optimal design problems involving shocks

augmented lagrangian sqp methods in hilbert spaces and application to control in the coefficients problems augmented lagrangian methods for optimization problems in hilbert spaces with second order updates of the lagrangian are analyzed the relationship and the differences to sqp methods are pointed out the convergence results depend on the second order sufficient optimality condition applications to parameter estimation problems are given and illustrated by numerical results these illustrate the global behavior of the method

new systolic architectures for inversion and division in gf 2 sup m we present two systolic architectures for inversion and division in gf 2 sup m based on a modified extended euclidean algorithm our architectures are similar to those proposed by others in that they consist of two dimensional arrays of computing cells and control cells with only local intercell connections and have o m sup 2 area time product however in comparison to similar architectures both our architectures have critical path delays that are smaller gate counts that range from being considerably smaller to only slightly larger and latencies that are identical for inversion but somewhat larger for division one architecture uses an adder or an m l bit ring counter inside each control cell while the other architecture distributes the ring counters into the computing cells thereby reducing each control cell to just two gates

efficient bit serial multiplication and the discrete time wiener hopf equation over finite fields discrete time wiener hopf equations dtwhes over finite fields are considered it is shown that solving the dtwhe is equivalent to performing division over finite fields the proof provides a new interpretation of the relationship between bit serial multiplication and dtwhes the interpretation enables bit serial multiplication over gf 2 sup m to be understood more easily as an example bit serial multiplication methods for multiplying any two elements that can be done without performing any transformation or with only a simple transformation of the bases are presented

on a mapping approach to investigating the bootstrap accuracy a simple mapping approach is proposed to study the bootstrap accuracy in a rather general setting it is demonstrated that the bootstrap accuracy can be obtained through this method for a broad class of statistics to which the commonly used edgeworth expansion approach may not be successfully applied we then consider some examples to illustrate how this approach may be used to find the bootstrap accuracy and show the advantage of the bootstrap approximation over the gaussian approximation for the multivariate kolmogorov smirnov statistic we show the error of bootstrap approximation is as small as that of the gaussian approximation for the multivariate kernel type density estimate we obtain an order of the bootstrap error which is smaller than the order of the error of the gaussian approximation given in rio 1994 we also consider an application of the bootstrap accuracy for empirical process to that for the copula process

on bit serial multiplication and dual bases in gf 2 sup m the existence of certain types of dual bases in finite fields gf 2 sup m is discussed these special types of dual bases are needed for efficient implementation of generalized bit serial multiplication in gf 2 sup m in particular the question of choosing a polynomial basis of gf 2 sup m for example 1 alpha alpha sup 2 alpha sup 3 alpha sup m 1 such that the change of basis matrix from the dual basis to a scalar multiple of the original basis has as few 1 entries as possible is studied it was previously shown by m wang and i f blake 1990 that the optimal situation occurs when the minimal polynomial of alpha is an irreducible trinomial of degree m then an appropriate scalar multiple beta yields a change of basis matrix that is a permutation matrix a construction is presented that often yields bases where the change of basis matrix has low weight in the case where no irreducible trinomial of degree m exists a simple formula can be used to compute beta and the weight of the change of basis matrix given the minimal polynomial of alpha

local analysis of a new multipliers method abstract in this paper we introduce a penalty function and a corresponding multipliers method for the solution of a class of nonlinear programming problems where the equality constraints have a particular structure the class models optimal control and engineering design problems with bounds on the state and control variables and has wide applicability the multipliers method updates multipliers corresponding to inequality constraints maintaining their nonnegativity instead of dealing with multipliers associated with equality constraints the basic local convergence properties of the method are proved and a dual framework is introduced we also analyze the properties of the penalized problem related with the penalty function

on the key equation we consider the set m a b a bh mod x2t of all solutions of the key equation for alternant codes where h is the syndrome polynomial in decoding these codes a particular solution m is sought subject to and being relatively prime and satisfying certain degree conditions we prove that these requirements specify uniquely as the minimal element of m analogous to the monic polynomial of minimal degree generating an ideal of f x with respect to a certain term order and that as such may be determined from an appropriate grobner basis of m motivated by this and other variations of the key equation such as that appropriate to errors and erasures decoding we derive a general algorithm for solving the congruence a bg mod xn for a range of term orders defined by the conditions on the particular solution required our techniques provide a unified approach to the solution of these key equations

exact penalty functions in constrained optimization in this paper formal definitions of exactness for penalty functions are introduced and sufficient conditions for a penalty function to be exact according to these definitions are stated thus providing a unified framework for the study of both nondifferentiable and continuously differentiable penalty functions in this framework the best known classes of exact penalty functions are analyzed and new results are established concerning the correspondence between the solutions of the constrained problem and the unconstrained minimizers of the penalty functions

vlsi architectures for computing multiplications and inverses in gf 2 m finite field arithmetic logic is central in the implementation of reed solomon coders and in some cryptographic algorithms there is a need for good multiplication and inversion algorithms that can be easily realized on vlsi chips massey and omura 1 recently developed a new multiplication algorithm for galois fields based on a normal basis representation in this paper a pipeline structure is developed to realize the massey omura multiplier in the finite field gf 2m with the simple squaring property of the normal basis representation used together with this multiplier a pipeline architecture is also developed for computing inverse elements in gf 2m the designs developed for the massey omura multiplier and the computation of inverse elements are regular simple expandable and therefore naturally suitable for vlsi implementation

gaussian and non gaussian inverse modeling of groundwater flow using copulas and random mixing this paper presents a new copula based methodology for gaussian and non gaussian inverse modeling of groundwater flow the presented approach is embedded in a monte carlo framework and it is based on the concept of mixing spatial random fields where a spatial copula serves as spatial dependence function the target conditional spatial distribution of hydraulic transmissivities is obtained as a linear combination of unconditional spatial fields the corresponding weights of this linear combination are chosen such that the combined field has the prescribed spatial variability and honors all the observations of hydraulic transmissivities the constraints related to hydraulic head observations are nonlinear in order to fulfill these constraints a connected domain in the weight space inside which all linear constraints are fulfilled is identified this domain is defined analytically and includes an infinite number of conditional fields i e conditioned on the observed hydraulic transmissivities and the nonlinear constraints can be fulfilled via minimization of the deviation of the modeled and the observed hydraulic heads this procedure enables the simulation of a great number of solutions for the inverse problem allowing a reasonable quantification of the associated uncertainties the methodology can be used for fields with gaussian copula dependence and fields with specific non gaussian copula dependence further arbitrary marginal distributions can be considered

high radix multiplier dividers theory design and hardware this paper describes the theory and design of digital high radix multiplier dividers patent pending the theory of high radix division is extended to high radix multiplier dividers that can perform fused multiplication and division operations using a single recurrence relation with the fused implementation of multiplication and division the two operations can be executed using a single instruction implying only a single rounding operation the recurrence relation is described the quotient digit selection function derived and important design parameters together with their optimal values and relations are defined efficient design procedure and implementation hardware are described and important system parameter values for various radix systems computed compared to pure dividers the multiplier divider requires a slightly more complex data path and quotient digit selection function

efficient multiplier architectures for galois fields gf 2 sup 4n this contribution introduces a new class of multipliers for finite fields gf 2 sup n sup 4 the architecture is based on a modified version of the karatsuba ofman algorithm koa by determining optimized field polynomials of degree four the last stage of the koa and the module reduction can be combined this saves computation and area in vlsi implementations the new algorithm leads to architectures which show a considerably improved gate complexity compared to traditional approaches and reduced delay if compared with koa based architectures with separate module reduction the new multipliers lead to highly modular architectures and are thus well suited for vlsi implementations three types of field polynomials are introduced and conditions for their existence are established for the small fields where n 2 3 8 which are of primary technical interest optimized field polynomials were determined by an exhaustive search for each field order exact space and time complexities are provided

efficient pairwise composite likelihood estimation for spatial clustered data spatial clustered data refer to high dimensional correlated measurements collected from units or subjects that are spatially clustered such data arise frequently from studies in social and health sciences we propose a unified modeling framework termed as geocopula to characterize both large scale variation and small scale variation for various data types including continuous data binary data and count data as special cases to overcome challenges in the estimation and inference for the model parameters we propose an efficient composite likelihood approach in that the estimation efficiency is resulted from a construction of over identified joint composite estimating equations consequently the statistical theory for the proposed estimation is developed by extending the classical theory of the generalized method of moments a clear advantage of the proposed estimation method is the computation feasibility we conduct several simulation studies to assess the performance of the proposed models and estimation methods for both gaussian and binary spatial clustered data results show a clear improvement on estimation efficiency over the conventional composite likelihood method an illustrative data example is included to motivate and demonstrate the proposed method

factor copula models for replicated spatial data abstractwe propose a new copula model that can be used with replicated spatial data unlike the multivariate normal copula the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process moreover the proposed copula can model tail dependence and tail asymmetry the model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature such as the matern model for some choice of common factors the joint copula density is given in closed form and therefore likelihood estimation is very fast in the general case one dimensional numerical integration is needed to calculate the likelihood but estimation is still reasonably fast even with large datasets we use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors we apply the proposed model to spatial temperature data and com

multivariate hydrological frequency analysis using copulas 1 this article presents the modeling of multivariate extreme values using copulas our approach allows us to model the dependence structure independently of the marginal distributions which is not possible with standard classical methods the methodology has been applied on two different problems in hydrology the first application is concerned with the combined risk in the framework of frequency analysis four copulas have been tested on peak flows from the watershed of peribonka in quebec canada the second application relates to the joint modeling of peak flows and volumes three copulas have been applied to the watershed of the rimouski river in quebec canada this approach using copulas is promising since it allows us to take into account a wide range of correlation which can happen in hydrology

superlinear and quadratic convergence of affine scaling interior point newton methods for problems with simple bounds without strict complementarity assumption a class of affine scaling interior point methods for bound constrained optimization problems is introduced which are locally q superlinear or q quadratic convergent it is assumed that the strong second order sufficient optimality conditions at the solution are satisfied but strict complementarity is not required the methods are modifications of the affine scaling interior point newton methods introduced by t f coleman and y li math programming 67 189 224 1994 there are two modifications one is a modification of the scaling matrix the other one is the use of a projection of the step to maintain strict feasibility rather than a simple scaling of the step a comprehensive local convergence analysis is given a simple example is presented to illustrate the pitfalls of the original approach by coleman and li in the degenerate case and to demonstrate the performance of the fast converging modifications developed in this paper