Average case complexity of linear multivariate problems

We study the average case complexity of a linear multivariate problem (\lmp) defined on functions of $d$ variables. We consider two classes of information. The first \lstd consists of function values and the second \lall of all continuous linear functionals. Tractability of \lmp means that the average case complexity is O((1/\e)^p) with $p$ independent of $d$. We prove that tractability of an \lmp in \lstd is equivalent to tractability in \lall, although the proof is {\it not} constructive. We provide a simple condition to check tractability in \lall. We also address the optimal design problem for an \lmp by using a relation to the worst case setting. We find the order of the average case complexity and optimal sample points for multivariate function approximation. The theoretical results are illustrated for the folded Wiener sheet measure.Comment: 7 page

A Probabilistic Analysis of Linear Operator Testing

AbstractWe test for β-conformance of an implementation linear operator A to a specification linear operator S where the operator domain and range are separable Hilbert spaces and the domain space F is equipped with a Gaussian measure μ. Given an error bound ε>0 and a tolerance parameter β∈(0, 1), we want to determine either that there is an element f in a ball Bq of radius q in domain F such that ‖Sf−Af‖>ε or that Aβ-conforms to S on a set of measure at least 1−β in the ball Bq; i.e., μq(f:‖Sf−Af‖⩽ε)⩾1−β where μq is the truncated Gaussian measure to Bq. We present a deterministic algorithm that solves this problem and uses almost a minimal number of tests where each test is an evaluation of operators S and A at an element of F. We prove that optimal tests are conducted on the eigenvectors of the covariance operator of μ. They are universal; they are independent of the operators under consideration and other problem parameters. We show that finite testing is conclusive in this probabilistic setting. In contrast, finite testing is inconclusive in the worst and average case settings; see [5, 7]. We discuss the upper and lower bounds on the minimal number of tests. For q=∞ we derive the exact bounds on the minimal number of tests, which depend on β very weakly. On the other hand, for a finite q, the bounds on the minimal number of tests depend on β more significantly. We explain our approach by an example with the Wiener measure

Finite-order weights imply tractability of multivariate integration

AbstractMultivariate integration of high dimension s occurs in many applications. In many such applications, for example in finance, integrands can be well approximated by sums of functions of just a few variables. In this situation the superposition (or effective) dimension is small, and we can model the problem with finite-order weights, where the weights describe the relative importance of each distinct group of variables up to a given order (where the order is the number of variables in a group), and ignore all groups of variables of higher order.In this paper we consider multivariate integration for the anchored and unanchored (non-periodic) Sobolev spaces equipped with finite-order weights. Our main interest is tractability and strong tractability of QMC algorithms in the worst-case setting. That is, we want to find how the minimal number of function values needed to reduce the initial error by a factor ε depends on s and ε−1. If there is no dependence on s, and only polynomial dependence on ε−1, we have strong tractability, whereas with polynomial dependence on both s and ε−1 we have tractability.We show that for the anchored Sobolev space we have strong tractability for arbitrary finite-order weights, whereas for the unanchored Sobolev space we have tractability for all bounded finite-order weights. In both cases, the dependence on ε−1 is quadratic. We can improve the dependence on ε−1 at the expense of polynomial dependence on s. For finite-order weights, we may achieve almost linear dependence on ε−1 with a polynomial dependence on s whose degree is proportional to the order of the weights.We show that these tractability bounds can be achieved by shifted lattice rules with generators computed by the component-by-component (CBC) algorithm. The computed lattice rules depend on the weights. Similar bounds can also be achieved by well-known low discrepancy sequences such as Halton, Sobol and Niederreiter sequences which do not depend on the weights. We prove that these classical low discrepancy sequences lead to error bounds with almost linear dependence on n−1 and polynomial dependence on d. We present explicit worst-case error bounds for shifted lattice rules and for the Niederreiter sequence. Better tractability and error bounds are possible for finite-order weights, and even for general weights if they satisfy certain conditions. We present conditions on general weights that guarantee tractability and strong tractability of multivariate integration

Lattice rule algorithms for multivariate approximation in the average case setting

AbstractWe study multivariate approximation for continuous functions in the average case setting. The space of d variate continuous functions is equipped with the zero mean Gaussian measure whose covariance function is the reproducing kernel of a weighted Korobov space with the smoothness parameter α>1 and weights γd,j for j=1,2,…,d. The weight γd,j moderates the behavior of functions with respect to the jth variable, and small γd,j means that functions depend weakly on the jth variable. We study lattice rule algorithms which approximate the Fourier coefficients of a function based on function values at lattice sample points. The generating vector for these lattice points is constructed by the component-by-component algorithm, and it is tailored for the approximation problem. Our main interest is when d is large, and we study tractability and strong tractability of multivariate approximation. That is, we want to reduce the initial average case error by a factor ε by using a polynomial number of function values in ε-1 and d in the case of tractability, and only polynomial in ε-1 in the case of strong tractability. Necessary and sufficient conditions on tractability and strong tractability are obtained by applying known general tractability results for the class of arbitrary linear functionals and for the class of function values. Strong tractability holds for the two classes in the average case setting iff supd⩾1∑j=1dγd,js<∞ for some positive s<1, and tractability holds iff supd⩾1∑j=1dγd,jt/log(d+1)<∞ for some positive t<1.. The previous results for the class of function values have been non-constructive. We provide a construction in this paper and prove tractability and strong tractability error bounds for lattice rule algorithms. This paper can be viewed as a continuation of our previous paper where we studied multivariate approximation for weighted Korobov spaces in the worst case setting. Many technical results from that paper are also useful for the average case setting. The exponents of ε-1 and d corresponding to our error bounds are not sharp. However, for α close to 1 and for slow decaying weights, we obtain almost the minimal exponent of ε-1. We also compare the results from the worst case and the average case settings in weighted Korobov spaces