{"title":"Sensitivity analysis of ODEs and DAEs — theory and implementation guide","authors":"M. Kiehl","doi":"10.1080/10556789908805742","DOIUrl":null,"url":null,"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...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":"10 1","pages":"803-821"},"PeriodicalIF":1.4000,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optimization Methods & Software","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10556789908805742","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
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...
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.