In this paper we present error analysis for a Monte Carlo algorithm for evaluating bilinear forms of matrix powers. An almost Optimal Monte Carlo (MAO) algorithm for solving this problem is formulated. Results for the structure of the probability error are presented and the construction of robust and interpolation Monte Carlo algorithms are discussed. Results are presented comparing the performance of the Monte Carlo algorithm with that of a corresponding deterministic algorithm. The two algorithms are tested on a well balanced matrix and then the effects of perturbing this matrix, by small and large amounts, is studied
In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algori...
Abstract. The convergence of Monte Carlo method for numerical in-tegration can often be improved by ...
In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on...
Abstract. In this paper we present error analysis for a Monte Carlo algorithm for evaluating bilinea...
In this paper we consider bilinear forms of matrix polynomials and show that these polynomials can b...
In this paper we deal with performance analysis of Monte Carlo algorithm for large linear algebra pr...
In this paper we analyse applicability and robustness of Markov chain Monte Carlo algorithms for eig...
Abstract. The problem of evaluating the dominant eigenvalue of real matrices using Monte Carlo numer...
A novel algorithm for computing the action of a matrix exponential over a vector is proposed. The al...
Many scientific and engineering applications involve inverting large matrices or solving systems of ...
Motivated by applications in which the data may be formulated as a matrix, we consider algorithms fo...
We analyze the Monte Carlo algorithm for the approximation of multivariate integrals when a pseudor...
Monte Carlo methods have become important in analysis of nonlinear inverse problems where no analyti...
AbstractA new parallel algorithm for the solution of linear systems, based upon the Monte Carlo appr...
The problem of obtaining the smallest and the largest generalised eigenvalues using quasi Monte Carl...
In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algori...
Abstract. The convergence of Monte Carlo method for numerical in-tegration can often be improved by ...
In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on...
Abstract. In this paper we present error analysis for a Monte Carlo algorithm for evaluating bilinea...
In this paper we consider bilinear forms of matrix polynomials and show that these polynomials can b...
In this paper we deal with performance analysis of Monte Carlo algorithm for large linear algebra pr...
In this paper we analyse applicability and robustness of Markov chain Monte Carlo algorithms for eig...
Abstract. The problem of evaluating the dominant eigenvalue of real matrices using Monte Carlo numer...
A novel algorithm for computing the action of a matrix exponential over a vector is proposed. The al...
Many scientific and engineering applications involve inverting large matrices or solving systems of ...
Motivated by applications in which the data may be formulated as a matrix, we consider algorithms fo...
We analyze the Monte Carlo algorithm for the approximation of multivariate integrals when a pseudor...
Monte Carlo methods have become important in analysis of nonlinear inverse problems where no analyti...
AbstractA new parallel algorithm for the solution of linear systems, based upon the Monte Carlo appr...
The problem of obtaining the smallest and the largest generalised eigenvalues using quasi Monte Carl...
In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algori...
Abstract. The convergence of Monte Carlo method for numerical in-tegration can often be improved by ...
In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on...