feature subset selection by gravitational search algorithm optimization a new method for feature subset selection in machine learning fss mgsa feature subset selection by modified gravitational search algorithm is presented fss mgsa is an evolutionary stochastic search algorithm based on the law of gravity and mass interactions and it can be executed when domain knowledge is not available a wrapper approach over naive bayes id3 k nearest neighbor and support vector machine learning algorithms is used to evaluate the goodness of each visited solution the key to the success of the mgsa is to utilize the piecewise linear chaotic map for increasing its diversity of species and to use sequential quadratic programming for accelerating local exploitation promising results are achieved in a variety of tasks where domain knowledge is not available the experimental results show that the proposed method has the ability of selecting the discriminating input features correctly and can achieve high accuracy of classification which is comparable to or better than well known similar classifier systems furthermore the mgsa is tested on ten functions provided by cec 2005 special session and compared with various modified gravitational search algorithm particle swarm optimization and genetic algorithm the obtained results confirm the high performance of the mgsa in solving various problems in optimization

weightless cohomology of algebraic varieties abstract using morel s weight truncations in categories of mixed sheaves we attach to varieties over finite fields or the complex numbers a series of groups called the weightless cohomology groups these lie between the usual cohomology and intersection cohomology have natural ring structures and are functorial for certain morphisms

improved algorithms for economic lot size problems many problems in inventory control production planning and capacity planning can be formulated in terms of a simple economic lot size model proposed independently by a s manne 1958 and by h m wagner and t m whitin 1958 the manne wagner whitin model and its variants have been studied widely in the operations research and management science communities and a large number of algorithms have been proposed for solving various problems expressed in terms of this model most of which assume concave costs and rely on dynamic programming in this paper we show that for many of these concave cost economic lot size problems the dynamic programming formulation of the problem gives rise to a special kind of array called a monge array we then show how the structure of monge arrays can be exploited to obtain significantly faster algorithms for these economic lot size problems we focus on uncapacitated problems i e problems without bounds on production inventory or backlogging capacitated problem

a hybrid genetic algorithm for a type of nonlinear programming problem abstract based on the introduction of some new concepts of semifeasible direction feasible degree fd1 of semifeasible direction feasible degree fd2 of illegal points belonging to feasible domain etc this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special hybrid genetic algorithm hga with penalty function and gradient direction search for nonlinear programming problems it uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process simulation of some examples show that this method is effective

on the cohomology of a simple normal crossings divisor abstract we establish a formula which decomposes the cohomologies of various sheaves on a simple normal crossings divisor snc d in terms of the simplicial cohomologies of the dual complex d with coefficients in a presheaf of vector spaces this presheaf consists precisely of the corresponding cohomology data on the components of d and on their intersections we use this formula to give a hodge decomposition for snc divisors and investigate the toric setting we also conjecture the existence of such a formula for effective non reduced divisors with snc support and show that this would imply the vanishing of the higher simplicial cohomologies of the dual complex associated to a resolution of an isolated rational singularity

a piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization this paper presents a novel robust hybrid particle swarm optimization rhpso based on piecewise linear chaotic map pwlcm and sequential quadratic programming sqp the aim of the present research is to develop a new single objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems this novel algorithm makes the best of ergodicity of pwlcm to help pso with the global search while employing the sqp to accelerate the local search five unconstrained benchmarks eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach the simulation results compared with other state of art methods demonstrate the effectiveness and robustness of the proposed rhpso for both unconstrained and constrained problems of different dimensions

formulation of general possibilistic linear programming problems for complex industrial systems the current models and methods for plp are usually restricted on some special types and usually the same type of possibilistic distribution this paper focuses on linear programming problems with general possibilistic resources grplp and linear programming problems with general possibilistic objective coefficients goplp by introducing some new concepts of the largest most possible point the smallest most possible point the most optimistic point and the most possible decision a new approach for formulating possibilistic constraints through general possibilistic resources and a satisfactory solution method will be discussed in this paper a most possible decision method for grplp and a dual approach for goplp will be proposed for solving complex industrial decision problems

multilevel capacitated lotsizing complexity and lp based heuristics abstract this paper presents the first heuristics capable of solving multilevel lotsizing problems with capacity constraints on more than one level moreover the form of the heuristics is quite general so that they can easily be extended to solve a variety of problems if one wants to solve these problems on a routine basis in a real environment one needs to find fast and easy algorithms however we show that for certain problem classes this is a very difficult task far more difficult than has been suggested in the literature for problems with setup times we show that finding a feasible solution is np complete even without setup times testing for feasibility can be very difficult just how time consuming such heuristics must be is demonstrated this leaves little chance to build fast and easy heuristics except for the most simple cases our exploration of the complexity issues points to mathematical programming as a potential source of heuristics for these problems this paper presents a new and general approach based on rounding an lp solution for the problem without setup times the methods use different information and patterns evident in the lp solution are explored the approach is tested on a large set of problems the main contributions of this paper are the way in which we distinguish between the easy and hard lotsizing problems the lp based heuristics and the test set of capacitated multilevel lotsizing problems

a novel parallel hybrid intelligence optimization algorithm for a function approximation problem a novel parallel hybrid intelligence optimization algorithm phioa is proposed based on combining the merits of particle swarm optimization with genetic algorithms the phioa uses the ideas of selection crossover and mutation from genetic algorithms gas and the update velocity and situation of particle swarm optimization pso under the independence of pso and gas the proposed algorithm divides the individuals into two equation groups according to their fitness values the subgroup of the top fitness values is evolved by gas and the other subgroup is evolved by the pso algorithm the optimal number is selected as a global optimum at every circulation which shows better results than both pso and gas then improves the overall performance of the algorithm the phioa is used to optimize the structure and parameters of the fuzzy neural network finally the experimental results have demonstrated the superiority of the proposed phioa to search the global optimal solution the phioa can improve the error accuracy while speeding up the convergence process and effectively avoid the premature convergence to compare with the existing methods

a reduced variable neighborhood search algorithm for uncapacitated multilevel lot sizing problems multilevel lot sizing mlls problems which involve complicated product structures with interdependence among the items play an important role in the material requirement planning mrp system of modern manufacturing assembling lines in this paper we present a reduced variable neighborhood search rvns algorithm and several implemental techniques for solving uncapacitated mlls problems computational experiments are carried out on three classes of benchmark instances under different scales small medium and large compared with the existing literature rvns shows good performance and robustness on a total of 176 tested instances for the 96 small sized instances the rvns algorithm can find 100 of the optimal solutions in less computational time for the 40 medium sized and the 40 large sized instances the rvns algorithm is competitive against other methods enjoying good effectiveness as well as high computational efficiency in the calculations rvns updated 7 17 5 best known solutions for the medium sized instances and 16 40 best known solutions for the large sized instances

convergence of optimal solutions about approximation scheme for fuzzy programming with minimum risk criteria this paper presents a class of two stage fuzzy programming with minimum risk criteria in the sense of value at risk var since the proposed two stage fuzzy minimum risk problem fmrp often includes fuzzy variable coefficients defined through possibility distributions with infinite supports it is inherently an infinite dimensional optimization problem that can rarely be solved directly thus algorithm procedures for solving such an optimization problem must rely on soft computing and approximation schemes which result in a finite dimensional optimization problem in this paper we develop an approximation method to compute the objective function of the two stage fmrp and discuss the convergent results about the use of the approximation method in fmrp including the convergence of the objective value optimal value and the optimal solutions to apply the convergent results about the approximation method we consider a two stage fuzzy facility location allocation fla problem with var objective and solve the problem indirectly by solving its approximating problem since the approximating fuzzy fla problem is neither linear nor convex conventional optimization algorithms cannot be used to solve it in this paper we design a hybrid particle swarm optimization pso algorithm to solve the approximating fuzzy fla problem one numerical example with five facilities and ten customers is also presented to demonstrate the effectiveness of the designed algorithm

a new motion compensated reduced order model kalman filter for space varying restoration of progressive and interlaced video we propose a new approach for motion compensated reduced order model kalman filtering for restoration of progressive and interlaced video in the case of interlaced inputs the proposed filter also performs deinterlacing in contrast to the literature both motion compensation and reduced order state modeling are achieved by augmenting the observation equation as opposed to modifying the state transition equation the proposed modeling which includes the two dimensional 2 d reduced order model kalman filtering romkf of angwin and kaufman 1989 as a special case results in significant performance improvement in fixed lag kalman filtering of space varying blurred images this is demonstrated by experimental results

mpso median oriented particle swarm optimization particle swarm optimization pso is a bio inspired optimization algorithm which has been empirically demonstrated to perform well on many optimization problems however it has two main weaknesses which have restricted the wider applications of pso the algorithm can easily get trapped in the local optima and has slow convergence speed therefore improvement and or elimination of these disadvantages are the most important objective in pso research in this paper we propose median oriented particle swarm optimization mpso to carry out a global search over entire search space with accelerating convergence speed and avoiding local optima the median position of particles and the worst and median fitness values of the swarm are incorporated in the standard pso to achieve the mentioned goals the proposed algorithm is evaluated on 20 unimodal multimodal rotated and shifted high dimensional benchmark functions and the results are compared with some well known pso algorithms in the literature the results show that mpso substantially enhances the performance of the pso paradigm in terms of convergence speed and finds global or good near global optimal in the functions

integer linear programming model for multidimensional two way number partitioning problem this paper introduces a multidimensional generalization of the two way number partitioning problem as well as an integer linear programming formulation of the problem there are n binary variables and 2p constraints the numerical experiments are made on a randomly generated set in view of its integer linear programming formulation tests are run in cplex this np hard problem uses a set of vectors rather than a set of numbers the presented experimental results indicate that the generalized problem is much harder than the initial problem

an application of swarm optimization to nonlinear programming particle swarm optimization pso is an optimization technique based on population which has similarities to other evolutionary algorithms it is initialized with a population of random solutions and searches for optima by updating generations particle swarm optimization has become the hotspot of evolutionary computation because of its excellent performance and simple implementation after introducing the basic principle of the pso a particle swarm optimization algorithm embedded with constraint fitness priority based ranking method is proposed in this paper to solve nonlinear programming problem by designing the fitness function and constraints handling method the proposed pso can evolve with a dynamic neighborhood and varied inertia weighted value to find the global optimum the results from this preliminary investigation are quite promising and show that this algorithm is reliable and applicable to almost all of the problems in multiple dimensional nonlinear and complex constrained programming it is proved to be efficient and robust by testing some example and benchmarks of the constrained nonlinear programming problems

heuristics for multilevel lot sizing with a bottleneck in this paper we present a heuristic method based on lagrangian relaxation for multilevel lot sizing when there is a single bottleneck facility a series of lagrangian relaxations one for each item in the product structure is imbedded in a branch and bound procedure the objective is to find a production schedule that fits within available capacity at minimum cost the method has two solution phases dual and primal in the dual phase of the procedure implied costs of setups and production are determined based on a tentative schedule the primal phase is repeated with these new prices and we iterate to reach a good solution the solution procedure is first tested on two special cases uncapacitated multilevel lot sizing and the capacitated single level multi item lot sizing problem the results show that the solution procedure can provide better solutions than some heuristics designed especially for those problems test results on the bottleneck problem indicate that good feasible solutions are found for problems too difficult to solve with exact methods

estimation and identification for 2 d block kalman filtering the development of a recursive identification and estimation procedure for two dimensional block kalman filtering is discussed the recursive identification scheme can be used online to update the image model parameters at each iteration based on the local statistics within a block of the observed noisy image the covariance matrix of the driving noise can also be estimated at each iteration of this algorithm a recursive procedure for computing the parameters of the higher order models is given simulation results are also provided

l infinity pairs and applications to singularities abstract over a field of characteristic zero every deformation problem with cohomology constraints is controlled by a pair consisting of a differential graded lie algebra together with a module unfortunately these pairs are usually infinite dimensional we show that every deformation problem with cohomology constraints is controlled by a typically finite dimensional l pair as a first application we show that for complex algebraic varieties with no weight zero 1 cohomology classes the components of the cohomology jump loci of rank one local systems containing the constant sheaf are tori this imposes restrictions on the fundamental groups the same holds for links and milnor fibers

subspace algorithms for identifying separable in denominator 2d systems with deterministic stochastic inputs abstractin this paper we present an algorithm for identifying two dimensional 2d causal recursive and separable in denominator crsd state space models in the roesser form with deterministic stochastic inputs the algorithm implements the n4sid po moesp and cca methods which are well known in the literature on 1d system identification but here we do so for the 2d crsd roesser model the algorithm solves the 2d system identification problem by maintaining the constraint structure imposed by the problem i e toeplitz and hankel and computes the horizontal and vertical system orders system parameter matrices and covariance matrices of a 2d crsd roesser model from a computational point of view the algorithm has been presented in a unified framework where the user can select which of the three methods to use furthermore the identification task is divided into three main parts 1 computing the deterministic horizontal model parameters 2 computing the deterministic vertical model parameters

a fast kalman filter for images degraded by both blur and noise in this paper a fast kalman filter is derived for the nearly optimal recursive restoration of images degraded in a deterministic way by blur and in a stochastic way by additive white noise straightforwardly implemented optimal restoration schemes for two dimensional images degraded by both blur and noise create dimensionality problems which in turn lead to large storage and computational requirements when the band toeplitz structure of the model matrices and of the distortion matrices in the matrix vector formulations of the original image and of the noisy blurred observation are approximated by circulant matrices these matrices can be diagonalized by means of the fft consequently a parallel set of n dynamical models suitable for the derivation of n low order vector kalman filters in the transform domain is obtained in this way the number of computations is reduced from the order of o n4 to that of o n 2 log 2 n for n n images

particle swarm optimization applied to the design of water supply systems in the past decade evolutionary methods have been used by various researchers to tackle optimal design problems for water supply systems wss particle swarm optimization pso is one of these evolutionary algorithms which in spite of the fact that it has primarily been developed for the solution of optimization problems with continuous variables has been successfully adapted in other contexts to problems with discrete variables in this work we have applied one of the variants of this algorithm to two case studies the hanoi water distribution network and the new york city water supply tunnel system both cases occur frequently in the related literature and provide two standard networks for benchmarking studies this allows us to present a detailed comparison of our new results with those previously obtained by other authors

incorporating a database approach into the large scale multi level lot sizing problem traditionally optimization for large scale multi level lot sizing mlls problems always encountered heavy computational burden scholars also indicated that whatever the optimal method chosen to solve the mlls problem standard optimization packages were still faced with computer memory constraints and computational limits that prevented them from solving realistic size cases therefore the main purpose of this paper is to propose an optimal method to reduce the computer memory while solving the large scale mlls problems the optimal method is designed to implement on a database entirely because the demand for computer memory can be reduced significantly by means of the utilization of database storage an example is given to illustrate the proposed method and computation capability is tested for the mlls problems with up to 1000 levels and 12 periods

characterization and estimation of two dimensional arma models a class of finite order two dimensional autoregressive moving average arma is introduced that can represent any process with rational spectral density in this model the driving noise is correlated and need not be gaussian currently known classes of arma models or ar models are shown to be subsets of the above class the three definitions of markov property are discussed and the class of arma models are precisely stated which have the noncausal and semicausal markov property without imposing any specific boundary conditions next two approaches are considered to estimate the parameters of a model to fit a given image the first method uses only the empirical correlations and involves the solution of linear equations the second method is the likelihood approach since the exact likelihood function is difficult to compute we resort to approximations suggested by the toroidal models numerical experiments compare the quality of the two estimation schemes finally the problem of synthesizing a texture obeying an arma model is considered

the convergent results about approximating fuzzy random minimum risk problems abstract based on mean chance theory this paper presents a new class of two stage fuzzy random minimum risk problem frmrp in order to solve the two stage frmrp we use a sequence of finitely supported primitive fuzzy random variables to approximate a continuous fuzzy random variable which result in a finite dimensional two stage frmrp we also discuss the convergence of the approximation method including the convergence of the objective value optimal value and optimal solutions to illustrate the modeling idea of this paper we apply the proposed two stage frmrp to the capacitated location allocation problem with fuzzy random demands finally we integrate the approximation method neural network nn and particle swarm optimization pso to design a hybrid pso algorithm to solve the two stage capacitated location allocation problem and provide a numerical example with five facilities and ten customers to demonstrate the effectiveness of the algorithm

design optimization of wastewater collection networks by pso optimal design of wastewater collection networks is addressed in this paper by making use of the so called pso particle swarm optimization technique this already popular evolutionary technique is adapted for dealing both with continuous and discrete variables as required by this problem an example of a wastewater collection network is used to show the algorithm performance and the obtained results are compared with those given by using dynamic programming to solve the same problem under the same conditions pso is shown to be a promising method to solve optimal design problems regarding in particular wastewater collection networks according to the results herein obtained

a co evolving framework for robust particle swarm optimization particle swarm optimization pso as an efficient and powerful problem solving strategy has been widely used but appropriate adjustment of its parameters usually requires a lot of time and labor so a co evolving framework is proposed to improve the robustness of the pso in this paper within this framework the fuzzy rules for the manipulation of the inertia weights are co evolved with the particles and the simulation results on a suite of test functions show that the use of this co evolving framework improves the performance of the pso especially the robustness against the dimensional variation of the test functions

image reconstruction and restoration overview of common estimation structures and problems developments in the theory of image reconstruction and restoration over the past 20 or 30 years are outlined particular attention is paid to common estimation structures and to practical problems not properly solved yet the problem of image reconstruction and restoration is first formulated some of the current regularization approaches used to solve the problem are then described the concepts of a priori information and compound criterion are introduced a bayesian interpretation of the regularization techniques is given which clarifies the role of the tuning parameters and indicates how they could be estimated the practical aspects of computing the solution first when the hyperparameters are known and second when they must be estimated are then considered conclusions are drawn and points that still need to be investigated are outlined

an orthogonal array based particle swarm optimizer with nonlinear time varying evolution particle swarm optimization pso is a population based heuristic optimization technique it has been developed to be a prominent evolution algorithm due to its simplicity of implementation and ability to quickly converge to a reasonable solution however it has also been reported that the algorithm has a tendency to get stuck in a near optimal solution in multi dimensional spaces to overcome the stagnation in searching a globally optimal solution a pso method with nonlinear time varying evolution pso ntve is proposed to approach the optimal solution closely when determining the parameters in the proposed method matrix experiments with an orthogonal array are utilized in which a minimal number of experiments would have an effect that approximates the full factorial experiments to demonstrate the performance of the proposed pso ntve method five well known benchmarks are used for illustration the results will show the feasibility and validity of the proposed method and its superiority over several previous pso algorithms

on weight truncations in the motivic setting abstract using punctual gluing of t structures we construct an analogue of s morel s weight truncation functors for certain weight profiles in the setting of motivic sheaves as an application we construct a canonical motivic analogue of the intersection complex for an arbitrary threefold over any field k we are also able to recover certain invariants of singularities motivically

an optimization algorithm based on chaotic behavior and fractal nature in this paper we propose a new optimization technique by modifying a chaos optimization algorithm coa based on the fractal theory we first implement the weighted gradient direction based chaos optimization in which the chaotic property is used to determine the initial choice of the optimization parameters both in the starting step and in the mutations applied when a convergence to local minima occurred the algorithm is then improved by introducing a method to determine the optimal step size this method is based on the fact that the sensitive dependence on the initial condition of a root finding technique such as the newton raphson search technique has a fractal nature from all roots step sizes found by the implemented technique the one that most minimizes the cost function is employed in each iteration numerical simulation results are presented to evaluate the performance of the proposed algorithm

particle swarm optimization for determining fuzzy measures from data fuzzy measures and fuzzy integrals have been successfully used in many real applications how to determine fuzzy measures is the most difficult problem in these applications though there have existed some methodologies for solving this problem such as genetic algorithms gradient descent algorithms and neural networks it is hard to say which one is more appropriate and more feasible each method has its advantages and limitations therefore it is necessary to develop new methods or techniques to learn distinct fuzzy measures in this paper we make the first attempt to design a special particle swarm algorithm to determine a type of general fuzzy measures from data and demonstrate that the algorithm is effective and efficient furthermore we extend this algorithm to identify and revise other types of fuzzy measures to test our algorithms we compare them with the basic particle swarm algorithms gradient descent algorithms and genetic algorithms in literatures in addition for verifying whether our algorithms are robust in noisy situations a number of numerical experiments are conducted theoretical analysis and experimental results show that for determining fuzzy measures the particle swarm optimization is feasible and has a better performance than the existing genetic algorithms and gradient descent algorithms

solving uncapacitated multilevel lot sizing problems using a particle swarm optimization with flexible inertial weight the multilevel lot sizing mlls problem is a key production planning problem in materials requirements planning mrp system the mlls problem deals with determining the production lot sizes of various items appearing in the product structure over a given finite planning horizon to minimize the production cost the inventory carrying cost the back ordering cost and etc this paper proposed a particle swarm optimization pso algorithm for solving the uncapacitated mlls problem with assembly structure all the mathematical operators in our algorithm are redefined and the inertial weight parameter can be either a negative real number or a positive one the feasibility and effectiveness of our algorithm are investigated by comparing the experimental results with those of a genetic algorithm ga

simple hironaka resolution in characteristic zero building upon work of villamayor and bierstone milman we give a proof of the canonical hironaka principalization and desingularization the idea of homogenized ideals introduced in the paper gives a priori the canonicity of algorithm and radically simplifies the proof

modified quantum behaved particle swarm optimization for parameters estimation of generalized nonlinear multi regressions model based on choquet integral with outliers in this paper a generalized nonlinear multi regression model based on choquet integral nmrci is proposed and applied for estimation of non additive systems that include outliers under inherent interaction among inputs the parameters estimation for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum behavior qpso named mqpso from the proposed model the high breakdown regression estimator a least trimmed squares lts is applied to eliminate the influence caused by these observations that contain outliers besides elitist crossover of genetic algorithm ga and adaptive decay of simulated annealing sa are used for conquering premature and controlling search policy respectively hence the proposed mqpso algorithm which combines the mechanisms of ga sa and lts within the qpso algorithm can deal with the proposed model with outliers from simulation results the proposed mqpso readily corrects the deviation caused by outliers without losing precisions and swiftly achieves convergences on estimating the parameters of the proposed generalized nmrci model for the non additive systems with outliers

kalman filtering in two dimensions further results the two dimensional reduced update kalman filter was recently introduced the corresponding scalar filtering equations were derived for the case of estimating a gaussian signal in white gaussian noise and were shown to constitute a general nonsymmetric half plane recursive filter this paper extends the method to the deconvolution problem of image restoration this paper also provides a more thorough treatment of the uniquely two dimensional boundary condition problems numerical and subjective examples are presented

generalized particle swarm optimization algorithm theoretical and empirical analysis with application in fault detection a generalization of the particle swarm optimization pso algorithm is presented in this paper the novel optimizer the generalized pso gpso is inspired by linear control theory it enables direct control over the key aspects of particle dynamics during the optimization process a detailed theoretical and empirical analysis is presented and parameter tuning schemes are proposed gpso is compared to the classical pso and genetic algorithm ga on a set of benchmark problems the results clearly demonstrate the effectiveness of the proposed algorithm finally an application of the gpso algorithm to the fine tuning of the support vector machines classifier for electrical machines fault detection is presented

a variable neighborhood search with an effective local search for uncapacitated multilevel lot sizing problems abstract in this study we improved the variable neighborhood search vns algorithm for solving uncapacitated multilevel lot sizing mlls problems the improvement is twofold first we developed an effective local search method known as the ancestors depth first traversal search adts which can be embedded in the vns to significantly improve the solution quality second we proposed a common and efficient approach for the rapid calculation of the cost change for the vns and other generate and test algorithms the new vns algorithm was tested against 176 benchmark problems of different scales small medium and large the experimental results show that the new vns algorithm outperforms all of the existing algorithms in the literature for solving uncapacitated mlls problems because it was able to find all optimal solutions 100 for 96 small sized problems and new best known solutions for 5 of 40 medium sized problems and for 30 of 40 large sized problems

predictive transform estimation image processing minimum mean squared error mse linear predictive transform lpt source decoding or modeling and kalman estimation are integrated to yield a unified approach to source modeling and estimation called pt estimation pt estimation enhances classical kalman estimation in two ways first it directly addresses the source modeling problem of scalar or multidimensional kalman estimation by integrating an exact minimum mse lpt decoder with a kalman estimator second it provides a transformation mechanism that inherently leads to significant design and implementation simplifications when the state dimensionality is large in the specific case of image reconstruction the design and implementation requirements of 2 d lpt smoother structures are lessened with respect to those of classical 2 d kalman smoother structures with exactly equivalent performance by factors that approach eight and four respectively simple nonadaptive 2 d lpt smoothers perform quite well when compared with previous adaptive linear minimum mse estimators

comparison of different one dimensional maps as chaotic search pattern in chaos optimization algorithms the aim of this paper is to propose and compare different one dimensional maps as chaotic search patterns in the constraint nonlinear optimization problems for this purpose about 10 one dimensional maps are introduced that can be used as search pattern in chaos optimization algorithms we apply these maps in specific optimization algorithm weighted gradient direction based chaos optimization algorithm and compare them based on numerical simulation results

toroidal varieties and the weak factorization theorem we develop the theory of stratified toroidal varieties which gives together with the theory of birational cobordisms 73 a proof of the weak factorization conjecture for birational maps in characteristic zero a birational map between complete nonsingular varieties over an algebraically closed field k of characteristic zero is a composite of blow ups and blow downs with smooth centers

tropical compactification in log regular varieties in this article we define a natural tropicalization procedure for closed subsets of log regular varieties in the case of constant coefficients and study its basic properties this framework allows us to generalize some of tevelev s results on tropical compactification as well as hacking s result on the cohomology of the link of a tropical variety to log regular varieties

a lagrangean based heuristic for dynamic multilevel multiitem constrained lotsizing with setup times in this paper a heuristic approach for the dynamic multilevel multiitem lotsizing problem in general product structures with multiple constrained resources and setup times is proposed with the help of lagrangean relaxation the capacitated multilevel multiitem lotsizing problem is decomposed into several uncapacitated single item lotsizing problems from the solutions of these single item problems lower bounds on the minimum objective function value are derived upper bounds are generated by means of a heuristic finite scheduling procedure the quality of the approach is tested with reference to various problem groups of differing sizes

modeling fuzzy multi period production planning and sourcing problem with credibility service levels a great deal of research has been done on production planning and sourcing problems most of which concern deterministic or stochastic demand and cost situations and single period systems in this paper we consider a new class of multi period production planning and sourcing problem with credibility service levels in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers in the proposed problem demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions the objective of the problem is to minimize the total expected cost including the expected value of the sum of the inventory holding and production cost in the planning horizon because the proposed problem is too complex to apply conventional optimization algorithms we suggest an approximation approach aa to evaluate the objective function after that two algorithms are designed to solve the proposed production planning problem the first is a pso algorithm combining the aa and the second is a hybrid pso algorithm integrating the aa neural network nn and pso finally one numerical example is provided to compare the effectiveness of the proposed two algorithms

two dimensional block kalman filtering for image restoration this paper is concerned with developing an efficient two dimensional 2 d block kalman filtering for digital image restoration a new 2 d multiinput multioutput mimo state space structure for modeling the image generation process is introduced this structure is derived by arranging a vector autoregressive ar model with a causal quarter plane region of support in block form this model takes into account the correlations of the image data in successive neighboring blocks and as a result reduces the edge effects prominent in the available kalman strip filtering techniques the degradation model for an infinite extent linear space invariant lsi blur and white gaussian wg noise is also modeled by an mimo block state space equation stemmed from a single input single output siso 2 d state space structure the image generation model and the degradation model are combined to yield a composite block state dynamic structure the block kalman filtering equations are obtained for this dynamic structure and then used to compute the suboptimal filter estimates of a noisy and blurred image

weights on cohomology invariants of singularities and dual complexes we study the weight filtration on the cohomology of a proper complex algebraic variety and obtain natural upper bounds on its size when it is the exceptional divisor of a singularity we also give bounds for the cohomology of links the invariants of singularities introduced here gives rather strong information about the topology of rational and related singularities

solving multi item capacitated lot sizing problems using variable redefinition mixed integer programming models are typically not used to solve realistic sized production scheduling problems because they require exorbitant solution times we impose a useful taxonomy on production scheduling problems and develop alternative formulations for a wide variety of problems within the taxonomy the linear programming relaxation of the new models is very effective in generating bounds we show that these bounds are equal to those that could be generated using lagrangian relaxation or column generation the linear programming bounds increase in effectiveness as the problems become larger perhaps of greatest significance is that practitioners can obtain our results using only standard off the shelf codes such as lindo or mpsx 370 we report computational experience in several computing environments hardware and software on problems with up to 200 products and 10 time periods 2000 0 1 variables