Sparse grid

About: Sparse grid is a research topic. Over the lifetime, 1013 publications have been published within this topic receiving 20664 citations.

A stochastic collocation approach for parabolic PDEs with random domain deformations.

Abstract: In this article we analyze the linear parabolic partial differential equation with a stochastic domain deformation. In particular, we concentrate on the problem of numerically approximating the statistical moments of a given Quantity of Interest (QoI). The geometry is assumed to be random. The parabolic problem is remapped to a fixed deterministic domain with random coefficients and shown to admit an extension on a well defined region embedded in the complex hyperplane. The stochastic moments of the QoI are computed by employing a collocation method in conjunction with an isotropic Smolyak sparse grid. Theoretical sub-exponential convergence rates as a function to the number of collocation interpolation knots are derived. Numerical experiments are performed and they confirm the theoretical error estimates.

Méthodes numériques sur des grilles sparse appliquées à l'évaluation d'options en finance

Abstract: In this work, we present some numerical methods to approximate Partial Differential Equation(PDEs) or Partial Integro-Differential Equations (PIDEs) commonly arising in finance. This thesis is split into three part. The first one deals with the study of Sparse Grid techniques. In an introductory chapter, we present the construction of Sparse Grid spaces and give some approximation properties. The second chapter is devoted to the presentation of a numerical algorithm to solve PDEs on these spaces. This chapter gives us the opportunity to clarify the finite difference method on Sparse Grid by looking at it as a collocation method. We make a few remarks on the practical implementation. The second part of the thesis is devoted to the application of Sparse Grid techniques to mathematical finance. We will consider two practical problems. In the first one, we consider a European vanilla contract with a multivariate generalisation of the one dimensional Ornstein-Ulenbeck-based stochastic volatility model. A relevant generalisation is to assume that the underlying asset is driven by a jump process, which leads to a PIDE. Due to the curse of dimensionality, standard deterministic methods are not competitive with Monte Carlo methods. We discuss sparse grid finite difference methods for solving the PIDE arising in this model up to dimension 4. In the second problem, we consider a Basket option on several assets (five in our example) in the Black & Scholes model. We discuss Galerkin methods in a sparse tensor product space constructed with wavelets. The last part of the thesis is concerned with a posteriori error estimates in the energy norm for the numerical solutions of parabolic obstacle problems allowing space/time mesh adaptive refinement. These estimates are based on a posteriori error indicators which can be computed from the solution of the discrete problem. We present the indicators for the variational inequality obtained in the context of the pricing of an American option on a two dimensional basket using the Black & Scholes model. All these techniques are illustrated by numerical examples.

Nested Sparse Successive Galerkin Approximation for Nonlinear Optimal Control Problems

Abstract: Solving the Hamilton-Jacobi-Bellman (HJB) equation for nonlinear optimal control problems usually suffers from the so-called curse of dimensionality. In this letter, a nested sparse successive Galerkin method is presented for HJB equations, and the computational cost only grows polynomially with the dimension. Based on successive approximation techniques, the nonlinear HJB partial differential equation (PDE) is transformed into a sequence of linear PDEs. Then the nested sparse grid methods are employed to solve the resultant linear PDEs. The designed method is sparse in two aspects. Firstly, the solution of the linear PDE is constructed based on sparse combinations of nested basis functions. Secondly, the multi-dimensional integrals in the Galerkin method are efficiently calculated using nested sparse grid quadrature rules. Once the successive approximation process is finished, the optimal controller can be analytically given based on the sparse basis functions and coefficients. Numerical results also demonstrate the accuracy and efficiency of the designed nested sparse successive Galerkin method.