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International Journal of Computer Mathematics

On the complexity of a two-point boundary value problem in different settings

Maciej Goćwin, Marek Szczęsny

Published Nov 5, 2010• 2 citations
Publisher

Abstract

We study the complexity of a two-point boundary value problem. We concentrate on the linear problem of order k with separated boundary conditions. Right-hand side functions are assumed to be r times differentiable with all derivatives bounded by a constant. We consider three models of computation: deterministic with standard and linear information, randomized and quantum. In each setting, we construct an algorithm for solving the problem, which allows us to establish upper complexity bounds. In the deterministic setting, we show that the use of linear information gives us a speed-up of at least one order of magnitude compared with the standard information. For randomized algorithms, we show that the speed-up over standard deterministic algorithms is by 1/2 in the exponent. For quantum algorithms, we can achieve a speed-up by one order of magnitude. We also provide lower complexity bounds. They match upper bounds in the deterministic setting with the standard information, and almost match upper bounds in the randomized and quantum settings. In the deterministic setting with the linear information, a gap still remains between the upper and lower complexity bounds.

DOI
10.1080/00207160903401852

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We study possible advantages of randomized and quantum computing over deterministic computing for scalar initial-value problems for ordinary differential equations of order k. For systems of equations of the first order this question has been settled modulo some details in \cite{Kacewicz05}. A speed-up over deterministic computing shown in \cite{Kacewicz05} is related to the increased regularity of the solution with respect to that of the right-hand side function. For a scalar equation of order k (which can be transformed into a special system of the first order), the regularity of the solution is increased by k orders of magnitude. This leads to improved complexity bounds depending on k for linear information in the deterministic setting, see \cite{Szczesny05}. This may suggest that in the randomized and quantum settings a speed-up can also be achieved depending on k. We show in this paper that a speed-up dependent on k is not possible in the randomized and quantum settings. We establish lower complexity bounds, showing that the randomized and quantum complexities remain at the some level as for systems of the first order, no matter how large k is. Thus, the algorithms from \cite{Kacewicz05} remain (almost) optimal, even if we restrict ourselves to a subclass of systems arising from scalar equations of order k.
The complexity of the following numerical problem is studied in the quantum model of computation: Consider a general elliptic partial differential equation of order 2m in a smooth, bounded domain Q\subset \R^d with smooth coefficients and homogeneous boundary conditions. We seek to approximate the solution on a smooth submanifold M\subseteq Q of dimension 0\le d_1 \le d. With the right hand side belonging to C^r(Q), and the error being measured in the L_\infty(M) norm, we prove that the n-th minimal quantum error is (up to logarithmic factors) of order n^{-min((r+2m)/d_1,r/d+1)}. For comparison, in the classical deterministic setting the n-th minimal error is known to be of order n^{-r/d}, for all d_1, while in the classical randomized setting it is (up to logarithmic factors) n^{-min((r+2m)/d_1,r/d+1/2)}.
We give a short introduction to quantum computing and its relation to numerical analysis. We survey recent research on quantum algorithms and quantum complexity theory for two basic numerical problems – high dimensional integration and approximation. Having matching upper and lower complexity bounds for the quantum setting, we are in a position to compare them with those for the classical deterministic and randomized setting, previously obtained in information-based complexity theory. This enables us to assess the possible speedups quantum computation could provide over classical deterministic or randomized algorithms for these numerical problems.
The complexity of the following numerical problem is studied in the quantum model of computation: Consider a general elliptic partial differential equation of order 2m in a smooth, bounded domain Q\\subset \\R^d with smooth coefficients and homogeneous boundary conditions. We seek to approximate the solution on a smooth submanifold M\\subseteq Q of dimension 0\\le d_1 \\le d. With the right hand side belonging to C^r(Q), and the error being measured in the L_\\infty(M) norm, we prove that the n-th minimal quantum error is (up to logarithmic factors) of order n^{-min((r+2m)/d_1,r/d+1)}. For comparison, in the classical deterministic setting the n-th minimal error is known to be of order n^{-r/d}, for all d_1, while in the classical randomized setting it is (up to logarithmic factors) n^{-min((r+2m)/d_1,r/d+1/2)}.