Bit-complexity of classical solutions of linear evolutionary systems of partial differential equations

IF 1.8 2区 数学 Q1 MATHEMATICS
Ivan Koswara , Gleb Pogudin , Svetlana Selivanova , Martin Ziegler
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引用次数: 1

Abstract

We study the bit-complexity intrinsic to solving the initial-value and (several types of) boundary-value problems for linear evolutionary systems of partial differential equations (PDEs), based on the Computable Analysis approach. Our algorithms are guaranteed to compute classical solutions to such problems approximately up to error 1/2n, so that n corresponds to the number of reliable bits of the output; bit-cost is measured with respect to n. Computational Complexity Theory allows us to prove in a rigorous sense that PDEs with constant coefficients are algorithmically ‘easier’ than general ones. Indeed, solutions to the latter are shown (under natural assumptions) computable using a polynomial number of memory bits, and we prove that the complexity class PSPACE is in general optimal; while the case of constant coefficients can be solved in #P—also essentially optimally so: the Heat Equation ‘requires’ #P1. Our algorithms raise difference schemes to exponential powers, efficiently: we compute any desired entry of such a power in #P, provided that the underlying exponential-sized matrices are circulant of constant bandwidth. Exponentially powering modular two-band circulant matrices is established even feasible in P; and under additional conditions, also the solution to certain linear PDEs becomes polynomial time computable.

偏微分方程线性演化系统经典解的位复杂度
基于可计算分析方法,我们研究了求解偏微分方程线性进化系统(PDE)的初值和(几种类型)边值问题所固有的比特复杂性。我们的算法保证计算这些问题的经典解,误差约为1/2n,因此n对应于输出的可靠位数;比特成本是相对于n来衡量的。计算复杂性理论使我们能够在严格意义上证明,具有常数系数的偏微分方程在算法上比一般偏微分方程“更容易”。事实上,后者的解决方案显示(在自然假设下)使用多项式数量的内存位是可计算的,并且我们证明复杂性类PSPACE是一般最优的;而常数系数的情况可以在#P中求解——也基本上是最优的:热方程“需要”#P1。我们的算法有效地将差分方案提高到指数幂:我们计算#P中这种幂的任何期望项,前提是底层指数大小的矩阵是恒定带宽的循环矩阵。指数幂模两带循环矩阵在P中成立甚至可行;并且在附加条件下,某些线性偏微分方程的解也变得多项式时间可计算。
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来源期刊
Journal of Complexity
Journal of Complexity 工程技术-计算机:理论方法
CiteScore
3.10
自引率
17.60%
发文量
57
审稿时长
>12 weeks
期刊介绍: The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited. Areas Include: • Approximation theory • Biomedical computing • Compressed computing and sensing • Computational finance • Computational number theory • Computational stochastics • Control theory • Cryptography • Design of experiments • Differential equations • Discrete problems • Distributed and parallel computation • High and infinite-dimensional problems • Information-based complexity • Inverse and ill-posed problems • Machine learning • Markov chain Monte Carlo • Monte Carlo and quasi-Monte Carlo • Multivariate integration and approximation • Noisy data • Nonlinear and algebraic equations • Numerical analysis • Operator equations • Optimization • Quantum computing • Scientific computation • Tractability of multivariate problems • Vision and image understanding.
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