{"title":"Complexity for a class of elliptic ordinary integro-differential equations","authors":"A.G. Werschulz","doi":"10.1016/j.jco.2023.101820","DOIUrl":null,"url":null,"abstract":"<div><p>Consider the variational form of the ordinary integro-differential equation (OIDE)<span><span><span><math><mo>−</mo><msup><mrow><mi>u</mi></mrow><mrow><mo>″</mo></mrow></msup><mo>+</mo><mi>u</mi><mo>+</mo><munderover><mo>∫</mo><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow></munderover><mi>q</mi><mo>(</mo><mo>⋅</mo><mo>,</mo><mi>y</mi><mo>)</mo><mi>u</mi><mo>(</mo><mi>y</mi><mo>)</mo><mrow><mtext>dy</mtext></mrow><mo>=</mo><mi>f</mi></math></span></span></span> on the unit interval <em>I</em><span>, subject to homogeneous Neumann boundary conditions. Here, </span><em>f</em> and <em>q</em> respectively belong to the unit ball of <span><math><msup><mrow><mi>H</mi></mrow><mrow><mi>r</mi></mrow></msup><mo>(</mo><mi>I</mi><mo>)</mo></math></span> and the ball of radius <span><math><msub><mrow><mi>M</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> of <span><math><msup><mrow><mi>H</mi></mrow><mrow><mi>s</mi></mrow></msup><mo>(</mo><msup><mrow><mi>I</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></math></span>, where <span><math><msub><mrow><mi>M</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>∈</mo><mo>[</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></math></span>. For <span><math><mi>ε</mi><mo>></mo><mn>0</mn></math></span>, we want to compute <em>ε</em>-approximations for this problem, measuring error in the <span><math><msup><mrow><mi>H</mi></mrow><mrow><mn>1</mn></mrow></msup><mo>(</mo><mi>I</mi><mo>)</mo></math></span> sense in the worst case setting. Assuming that standard information is admissible, we find that the <em>n</em>th minimal error is <span><math><mi>Θ</mi><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mi>min</mi><mo></mo><mo>{</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>/</mo><mn>2</mn><mo>}</mo></mrow></msup><mo>)</mo></math></span>, so that the information <em>ε</em>-complexity is <span><math><mi>Θ</mi><mo>(</mo><msup><mrow><mi>ε</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mi>min</mi><mo></mo><mo>{</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>/</mo><mn>2</mn><mo>}</mo></mrow></msup><mo>)</mo></math></span><span>; moreover, finite element methods of degree </span><span><math><mi>max</mi><mo></mo><mo>{</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>}</mo></math></span><span> are minimal-error algorithms. We use a Picard method to approximate the solution of the resulting linear systems, since Gaussian elimination will be too expensive. We find that the total </span><em>ε</em>-complexity of the problem is at least <span><math><mi>Ω</mi><mo>(</mo><msup><mrow><mi>ε</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mi>min</mi><mo></mo><mo>{</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>/</mo><mn>2</mn><mo>}</mo></mrow></msup><mo>)</mo></math></span> and at most <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>ε</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mi>min</mi><mo></mo><mo>{</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>/</mo><mn>2</mn><mo>}</mo></mrow></msup><mi>ln</mi><mo></mo><msup><mrow><mi>ε</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>)</mo></math></span>, the upper bound being attained by using <span><math><mi>O</mi><mo>(</mo><mi>ln</mi><mo></mo><msup><mrow><mi>ε</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>)</mo></math></span><span> Picard iterations.</span></p></div>","PeriodicalId":50227,"journal":{"name":"Journal of Complexity","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Complexity","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885064X23000894","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Consider the variational form of the ordinary integro-differential equation (OIDE) on the unit interval I, subject to homogeneous Neumann boundary conditions. Here, f and q respectively belong to the unit ball of and the ball of radius of , where . For , we want to compute ε-approximations for this problem, measuring error in the sense in the worst case setting. Assuming that standard information is admissible, we find that the nth minimal error is , so that the information ε-complexity is ; moreover, finite element methods of degree are minimal-error algorithms. We use a Picard method to approximate the solution of the resulting linear systems, since Gaussian elimination will be too expensive. We find that the total ε-complexity of the problem is at least and at most , the upper bound being attained by using Picard iterations.
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.