This paper presents the study of traffic load variations effect on the dynamic analysis of cable-stayed bridges. Time histories of displacement, velocity, acceleration, normal force, and bending moment are presented for different traffic... more
This paper presents the study of traffic load variations effect on the dynamic analysis of cable-stayed bridges. Time histories of displacement, velocity, acceleration, normal force, and bending moment are presented for different traffic load speeds. This study is concerned with harp bridges having five spans considering single plane of cables with 140 m for exterior spans, and 280 m for interior spans. The total length of the bridge is 1120 m. The own weight of all structural elements, and traffic load including impact are taken into account. In the dynamic analysis, the energy method, based on the minimization of the total potential energy (TPE) of structural elements, via conjugate gradient technique is used. The procedure is carried out using the iterative steps to acquire the final configuration. All prepared computer programs in FORTRAN language for this work and their verification is written by the second author. The conclusion, which have been drawn from the present work are outlined.
We present an active-set algorithm for finding a local minimizer to a nonconvex bound-constrained quadratic problem. Our algorithm extends the ideas developed by Dostal and Schoberl that is based on the linear conjugate gradient... more
We present an active-set algorithm for finding a local minimizer to a nonconvex bound-constrained quadratic problem. Our algorithm extends the ideas developed by Dostal and Schoberl that is based on the linear conjugate gradient algorithm for (approximately) solving a linear system with a positive-defi nite coefficient matrix. This is achieved by making two key changes. First, we perform a line search along negative curvature directions when they are encountered in the linear conjugate
gradient iteration. Second, we use Lanczos iterations to compute approximations to leftmost eigen-pairs, which is needed to promote convergence to points satisfying certain
second-order optimality conditions. Preliminary numerical results show that our method is efficient and robust on nonconvex bound-constrained quadratic problems.
We present a new algorithm for nonconvex bound-constrained quadratic optimization. In the strictly convex case, our method is equivalent to the state-of-the-art algorithm by Dostal and Schoberl [Comput. Optim. Appl., 30 (2005), pp.... more
We present a new algorithm for nonconvex bound-constrained quadratic optimization. In the strictly convex case, our method is equivalent to the state-of-the-art algorithm by Dostal and Schoberl [Comput. Optim. Appl., 30 (2005), pp. 23–43]. Unlike their method, however, we establish a convergence theory for our algorithm that holds even when the problems are nonconvex. This is achieved by carefully addressing the challenges associated with directions of negative curvature, in particular, those that may naturally arise when applying the conjugate gradient algorithm to an indefinite system of equations. Our presentation and analysis deal explicitly with both lower and upper bounds on the optimization variables, whereas the work by Dostal and Schoberl considers only strictly convex problems with lower bounds. To handle this generality, we introduce the reduced chopped gradient that is analogous to the reduced free gradient previously used. The reduced chopped gradient leads to a new condition that is used to determine when optimization over a given subspace should be terminated. This condition, although not equivalent, is motivated by a similar condition used by Dostal and Schoberl. Numerical results illustrate the superior performance of our method over commonly used solvers that employ gradient projection steps and subspace acceleration.
We present two algorithms to compute system-specific polarizabilities and dispersion coefficients such that required memory and computational time scale linearly with increasing number of atoms in the unit cell for large systems. The... more
We present two algorithms to compute system-specific polarizabilities and dispersion coefficients such that required memory and computational time scale linearly with increasing number of atoms in the unit cell for large systems. The first algorithm computes the atom-in-material (AIM) static polarizability tensors, force-field polarizabilities, and C 6 , C 8 , C 9 , C 10 dispersion coefficients using the MCLF method. The second algorithm computes the AIM polarizability tensors and C 6 coefficients using the TS-SCS method. Linear-scaling computational cost is achieved using a dipole interaction cutoff length function combined with iterative methods that avoid large dense matrix multiplies and large matrix inversions. For MCLF, Richardson extrapolation of the screening increments is used. For TS-SCS, a failproof conjugate residual (FCR) algorithm is introduced that solves any linear equation system having Hermitian coefficients matrix. These algorithms have mathematically provable stable convergence that resists round-off errors. We parallelized these methods to provide rapid computation on multi-core computers. Excellent parallelization efficiencies were obtained, and adding parallel processors does not significantly increase memory requirements. This enables system-specific polarizabilities and dispersion coefficients to be readily computed for materials containing millions of atoms in the unit cell. The largest example studied herein is an ice crystal containing >2 million atoms in the unit cell. For this material, the FCR algorithm solved a linear equation system containing >6 million rows, 7.57 billion interacting atom pairs, 45.4 billion stored non-negligible matrix components used in each large matrix-vector multiplication, and $19 million unknowns per frequency point (>300 million total unknowns).
Informe que contiene el método de gradientes conjugados, para matriz sparse simétrica de 14000x14000. Se utiliza el método de compresión por diagonales. El informe fue evaluado con nota 69(Chile), que es como una "A" en estados unidos.... more
Informe que contiene el método de gradientes conjugados, para matriz sparse simétrica de 14000x14000. Se utiliza el método de compresión por diagonales. El informe fue evaluado con nota 69(Chile), que es como una "A" en estados unidos. En caso de requerir el codigo utilizado, contactarme
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as... more
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation
learning, and Levenberg-Marquardt algorithm.
The purpose of this research is to explore improvements to non-linear search for problem sets that have large objective function computation times. This research investigates a variety of non-linear search algorithms and modifications to... more
The purpose of this research is to explore improvements to non-linear search for problem sets that have large objective function computation times. This research investigates a variety of non-linear search algorithms and modifications to maximize the utilization of the information obtained from each objective computation; the number of function computations needed for convergence are minimized. This research focuses on the use of Nelder-Mead method and Powell’s method of non-linear search and other non-derivative based search algorithms. The problem sets considered in this study are considered “black-box” functions where objective function differentiation is indeterminate; as such other methods are used to determine gradient information to improve the search algorithm. Lastly, the algorithms designed use massive parallel computing techniques to minimize objective function computation times and minimize convergence time.
This paper presents a parallel implementation of the Hybrid Bi-Conjugate Gradient Stabilized (BiCGStab(2)) iterative method in Graphics Processing Unit (GPU) for solution of large and sparse linear systems. This implementation uses the... more
This paper presents a parallel implementation of the Hybrid Bi-Conjugate Gradient Stabilized (BiCGStab(2)) iterative method in Graphics Processing Unit (GPU) for solution of large and sparse linear systems. This implementation uses the CUDA-Matlab integration, in which the method operations are performed in a GPU cores using Matlab built-in functions. The goal is to show that the exploitation of parallelism by using this new technology can provide a significant computational performance. For the validation of the work we compared the proposed implementation with a BiCGStab(2) sequential and parallelized implementation in the C and CUDA-C languages, respectively. The results showed that the proposed implementation is more efficient and can be indispensable for simulations being carried out with quality and in a timely manner. The gains in computational efficiency were, respectively, 76x and 6x compared to the implementation in C and CUDA-C.
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as... more
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
