Monte Carlo method for linear and nonlinear boundary value problems
Foundation, algorithms, numerical solutions and applications
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Availability of multiprocessor computers, allows us to solve many complex problems using Monte Carlo method. Monte Carlo methods have been further developed to solve a variety of multidimensional integrals, linear and nonlinear boundary value problems. In monograph we used the deep connections between differential equations and random processes. This connection has been known long ago, the results of the theory of differential equations have been widely used in the theory of probability and vice versa. The solutions of large class linear and nonlinear equations of elliptic and parabolic type m...
Availability of multiprocessor computers, allows us to solve many complex problems using Monte Carlo method. Monte Carlo methods have been further developed to solve a variety of multidimensional integrals, linear and nonlinear boundary value problems. In monograph we used the deep connections between differential equations and random processes. This connection has been known long ago, the results of the theory of differential equations have been widely used in the theory of probability and vice versa. The solutions of large class linear and nonlinear equations of elliptic and parabolic type may be represented in the form of integrals over the trajectories of Markov process. In the current work we study the approaches connected with simple and branching Markov processes. We obtained effective unbiased estimators for the solutions. Constructed numerical algorithms are strict proved and used for the solution of problems of mathematical physics. This book meant for specialists of numerical methods who applied Monte Carlo methods for the solution boundary value problems, calculating multidimensional integrals and work in the area financial mathematics and statistics.
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