Sensitivity analysis of ODEs and DAEs — theory and implementation guide

IF 1.4 3区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
M. Kiehl
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引用次数: 15

Abstract

The solution y(t,t 0,y 0) of an initial-value problem (IVP)[ydot](t)=f(t,y,p) with initial value y(t 0)=y 0 at a point t is a differentiable function of the initial value y 0 and the parameter vector p, provided f y and f p are continuous. The computation of y(t,t 0,y 0) and the sensitivity matrix play an important role in the efficient numerical solution of boundary-value problems, optimal-control problems and for parameter identification in dynamical systems. There is a wide variety of algorithms for the solution of IVPs but till now, there are just a few efficient implementations for the computation of , which is the even more important part. Here a number of implementations are introduced, treating non-stiff, stiff and differential algebraic equations. The basic new idea is to regard the numerical approximation of as the solution of the variational differential equation of a linearised IVP with approximation of the linear right-hand side by the difference quotient of the original non-linear f. For fur...
ODEs和DAEs的敏感性分析——理论与实施指南
初值问题(IVP)[ydot](t)=f(t,y,p),初值为y(t0)= y0,在点t处的解y(t, t0, y0)是初值y 0与参数向量p的可微函数,只要f y和f p连续。y(t, t0, y0)和灵敏度矩阵的计算在边值问题、最优控制问题和动力系统参数辨识的有效数值求解中起着重要的作用。求解ivp的算法多种多样,但迄今为止,对于ivp的计算,只有少数有效的实现,而ivp的计算是更重要的部分。这里介绍了一些实现,处理非刚性,刚性和微分代数方程。基本的新思想是把的数值近似看作线性化IVP的变分微分方程的解,线性右边近似为原非线性f的差商。
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来源期刊
Optimization Methods & Software
Optimization Methods & Software 工程技术-计算机:软件工程
CiteScore
4.50
自引率
0.00%
发文量
40
审稿时长
7 months
期刊介绍: Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Topics include: Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.
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