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In this paper, we propose an iterative algorithm for approximating a common fixed point of an infinite family of quasi-Bregman nonexpansive mappings which is also a solution to finite systems of convex minimization problems and variational inequality problems in real reflexive Banach spaces. We obtain a strong convergence result and give applications of our result to finding zeroes of an infinite family of Bregman inverse strongly monotone operators and a finite system of equilibrium problems in real reflexive Banach spaces. Our result extends many recent corresponding results in literature. [ABSTRACT FROM AUTHOR]

In this paper, a regularized sampling algorithm for band-limited signals is presented in the twodimensional case. The convergence of the regularized sampling algorithm is studied and compared with Shannon's sampling theorem and the Tikhonov regularization method by some examples. [ABSTRACT FROM AUTHOR]

Matsumoto, Saito and Matoba recently proposed the Walsh figure of merit (WAFOM), which is a computable criterion for quasi-Monte Carlo point sets using digital nets. Several algorithms have been proposed for finding low-WAFOM point sets. In the existing algorithms, the number of points is fixed in advance, but extensible point sets are preferred in some applications. In this paper, we propose a random search algorithm for extensible low-WAFOM point sets. For this, we introduce a method that uses lookup tables to compute WAFOM faster. Numerical results show that our extensible low-WAFOM point sets are comparable with Niederreiter-Xing sequences for some low-dimensional and smooth test functions. [ABSTRACT FROM AUTHOR]

This paper proposes an iterative method for solving an operator equation on a separable Hilbert space H equipped with a g-frame. We design an algorithm based on the conjugate gradient method and investigate the convergence and optimality of this algorithm. [ABSTRACT FROM AUTHOR]

In the matrix completion problem, most methods to solve the nuclear norm model are relaxing it to the nuclear norm regularized least squares problem. In this paper, we propose a new unconstraint model for matrix completion problem based on nuclear norm and indicator function and design a proximal point algorithm (PPA-IF) to solve it. Then the convergence of our algorithm is established strictly. Finally, we report numerical results for solving noiseless and noisy matrix completion problems and image reconstruction. [ABSTRACT FROM AUTHOR]

In this paper we generalize an explicit numerical scheme for the CIR process that we have proposed before. The advantage of the new proposed scheme is that preserves positivity and is well posed for a (little bit) broader set of parameters among the positivity preserving schemes. The order of convergence is at least logarithmic in general and for a smaller set of parameters is at least 1/4. [ABSTRACT FROM AUTHOR]

- We propose a new smoothing function in this paper by smoothing the symmetric perturbed Fischer-Burmeister function. Based on this new function, we present a new noninterior continuation method for solving the second-order cone programming. We adopt a variant merit function. Our algorithm needs to solve only one system of linear equations and to perform only one line search at each iteration. It can start from an arbitrary point and does not require the iteration points to be in the set of strictly feasible solutions. We prove the global and local quadratic convergence of the algorithm under suitable assumptions. Numerical results indicate that our algorithm performs well. [ABSTRACT FROM AUTHOR]

This is a review paper of the role of Carleman estimates in the theory of multidimensional coefficient inverse problems since the first inception of this idea in 1981. [ABSTRACT FROM AUTHOR]

In this paper, we describe a stochastic algorithm to solve sudoku puzzles. Our method consists in computing probabilities for each symbol of each cell updated at each step of the algorithm using estimation of distributions algorithms (EDA). This update is done using the empirical estimators of these probabilities for a fraction of the best puzzles according to a cost function. We develop also some partial restart techniques in the RESEDA algorithm to obtain a convergence for the most difficult puzzles. Our algorithm is tested numerically on puzzles with various levels of difficulty starting from very easy ones to very hard ones including the famous puzzle AI Escargot. The CPU times vary from few hundreds of a second for the easy ones to about one minute for the most difficult one. [ABSTRACT FROM AUTHOR]

The behavior of the multiplicative acceleration of Selmer's algorithm is widely unknown and no general result on convergence has been detected yet. Solely for its 2-dimensional, periodic expansions, there exist some results on convergence and approximation due to Fritz Schweiger. In this paper we show that periodic expansions of any dimension do in fact converge and that the coordinates of the limit points are rational functions of the largest eigenvalue of the periodicity matrix. [ABSTRACT FROM AUTHOR]

This paper proposes using an adaptive immune algorithm (AIA) for reactive power optimization. The adaptive immune algorithm automatically adjusts the parameters to achieve the balance between fast convergence and high diversity of antibodies, according to the distance between the antibodies. It leads to the reduction of the computation time, compared with other methods. The test results of several examples demonstrate that reactive power optimization based on AIA method has advantages in terms of computation speed and convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a full multigrid algorithm with a symmetric V-cycle for a grid problem obtained by discretization of a second-order elliptic equation with quadratic finite elements on triangles is studied. The multigrid complexity of the algorithm is proved. This means that the number of arithmetic operations required to achieve the order of accuracy of an approximate solution equal to that of the discretization error linearly depends on the number of unknowns. The rate of convergence is found to be higher than that for linear finite elements in spite of the higher order of accuracy. [ABSTRACT FROM AUTHOR]

Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are two risk measures which are widely used in the practice of risk management. This paper deals with the problem of estimating both VaR and CVaR using stochastic approximation (with decreasing steps): we propose a first Robbins–Monro (RM) procedure based on Rockafellar–Uryasev's identity for the CVaR. Convergence rate of this algorithm to its target satisfies a Gaussian Central Limit Theorem. As a second step, in order to speed up the initial procedure, we propose a recursive and adaptive importance sampling (IS) procedure which induces a significant variance reduction of both VaR and CVaR procedures. This idea, which has been investigated by many authors, follows a new approach introduced in [Lemaire and Pagès, Unconstrained Recursive Importance Sampling, 2008]. Finally, to speed up the initialization phase of the IS algorithm, we replace the original confidence level of the VaR by a slowly moving risk level. We prove that the weak convergence rate of the resulting procedure is ruled by a Central Limit Theorem with minimal variance and its efficiency is illustrated on several typical energy portfolios. [ABSTRACT FROM AUTHOR]

The preconditioned iteratively regularized Gauss–Newton algorithm for the minimization of general nonlinear functionals was introduced by Smirnova, Renaut and Khan (Inverse Problems 23: 1547–1563, 2007). In this paper, we establish theoretical convergence results for an extended stabilized family of Generalized Preconditioned Iterative methods which includes ℳ-times iterated Tikhonov regularization with line search. Numerical schemes illustrating the theoretical results are also presented. [ABSTRACT FROM AUTHOR]

In the paper we present several numerical algorithms to simulate the fractional Brownian motion (the generalized Wiener process). The algorithms have been developed on the basis of the integral representation of the fractional Brownian motion and the spectral decomposition for its increments. The convergence of the numerical methods has been studied. [ABSTRACT FROM AUTHOR]

Monte Carlo methods are now widely used in solving various computational fluid dynamics systems. This paper presents an improved scrambled quasi-Monte Carlo method for solving fluid dynamics applications. In our parallel implementation we use an independent scrambled quasirandom sequence for each processor. We explore the use of both scrambled quasi-Monte Carlo and variance reduction methods to improve the accuracy for Monte Carlo schemes. We also present theoretical analyses and numerical experiments to validate our numerical algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose a numerical method for solving nonstationary Maxwell equations on unstructured triangular grids, which is based on the finite volume method. We give the results of test calculations which confirm the second order of convergence of the method both for homogeneous media and media with space-variable dielectric permittivity. [ABSTRACT FROM AUTHOR]

In this paper we introduce reproducing kernel Hilbert spaces based on Taylor series. The unit ball of this space contains functions which are infinite at the boundary. We investigate multivariate integration in such spaces and show how functions in such spaces can be integrated with orderO(N–τ) for τ > 0 arbitrarily large, in spite of the unboundedness of the functions at the boundary. Further we prove that the Taylor space contains functions with infinite variance and hence the function space contains functions for which a simple Monte Carlo algorithm converges with probability one but convergence could be arbitrarily slow. [ABSTRACT FROM AUTHOR]

In this paper we propose and justify at the differential level an iteration method for solving the Stokes problem in a rectangular domain with the rate of convergence independent of the ratio of the sides. The numerical implementation of the algorithm is constructed for the cavity test problem and computational results are given. [ABSTRACT FROM AUTHOR]

The paper presents a new class of memory gradient methods with inexact line searches for unconstrained minimization problems. The methods use more previous iterative information than other methods to generate a search direction and use inexact line searches to select a step-size at each iteration. It is proved that the new methods have global convergence under weak mild conditions. The convergence rate of these methods is also investigated under some special cases. Some numerical experiments show that these new algorithms converge more stably than other line search methods and are effective in solving large scale unconstrained minimization problems. [ABSTRACT FROM AUTHOR]

In this paper we propose an improved quasi-Monte Carlo method for solving Linear Algebra problems. We show that by using low-discrepancy sequences both the convergence and the CPU time of the algorithm are improved. Two parallelization schemes using the Message Passing Interface with static and dynamic load balancing are proposed. The dynamic scheme is useful for computing in the GRID environment. [ABSTRACT FROM AUTHOR]