Classical and vector sturm—liouville problems: recent advances in singular-point analysis and shooting-type algorithms

AbstractSignificant advances have been made in the last year or two in algorithms and theory for Sturm—Liouville problems (SLPs). For the classical regular or singular SLP −(p(x)u′)′ + q(x)u = λw(x)u, a < x < b, we outline the algorithmic approaches of the recent library codes and what they can now routinely achieve.For a library code, automatic treatment of singular problems is a must. New results are presented which clarify the effect of various numerical methods of handling a singular endpoint.For the vector generalization −(P(x)u′)′+Q(x)u = λW(x)u where now u is a vector function of x, and P, Q, W are matrices, and for the corresponding higher-order vector self-adjoint problem, we outline the equally impressive advances in algorithms and theory

Graph theory, irreducibility, and structural analysis of differential-algebraic equation systems

The $\Sigma$-method for structural analysis of a differential-algebraic equation (DAE) system produces offset vectors from which the sparsity pattern of a system Jacobian is derived. This pattern implies a block-triangular form (BTF) of the DAE that can be exploited to speed up numerical solution. The paper compares this fine BTF with the usually coarser BTF derived from the sparsity pattern of the \sigmx. It defines a Fine-Block Graph with weighted edges, which gives insight into the relation between coarse and fine blocks, and the permitted ordering of blocks to achieve BTF. It also illuminates the structure of the set of normalised offset vectors of the DAE, e.g.\ this set is finite if and only if there is just one coarse block

How AD Can Help Solve Differential-Algebraic Equations

A characteristic feature of differential-algebraic equations is that one needs to find derivatives of some of their equations with respect to time, as part of so called index reduction or regularisation, to prepare them for numerical solution. This is often done with the help of a computer algebra system. We show in two significant cases that it can be done efficiently by pure algorithmic differentiation. The first is the Dummy Derivatives method, here we give a mainly theoretical description, with tutorial examples. The second is the solution of a mechanical system directly from its Lagrangian formulation. Here we outline the theory and show several non-trivial examples of using the "Lagrangian facility" of the Nedialkov-Pryce initial-value solver DAETS, namely: a spring-mass-multipendulum system, a prescribed-trajectory control problem, and long-time integration of a model of the outer planets of the solar system, taken from the DETEST testing package for ODE solvers

Structural analysis based dummy derivative selection for differential algebraic equations

The signature matrix structural analysis method developed by Pryce provides more structural information than the commonly used Pantelides method and applies to differential-algebraic equations (DAEs) of arbitrary order. It is useful to consider how existing methods using the Pantelides algorithm can benefit from such structural analysis. The dummy derivative method is a technique commonly used to solve DAEs that can benefit from such exploitation of underlying DAE structures and information found in the Signature Matrix method. This paper gives a technique to find structurally necessary dummy derivatives and how to use different block triangular forms effectively when performing the dummy derivative method and then provides a brief complexity analysis of the proposed approach. We finish by outlining an approach that can simplify the task of dummy pivoting

Multibody dynamics in natural coordinates through automatic differentiation and high-index DAE solving

The Natural Coordinates (NCs) method for Lagrangian modelling and simulation of multibody systems is valued for giving simple, sparse models. We describe our version of it and compare with the classical approach of Jal´on and Bayo (JBNCs). Our NCs use the high-index differential-algebraic equation solver Daets. Algorithmic differentiation, not symbolic algebra, forms the equations of motion from the Lagrangian. We obtain significantly smaller equation systems than JBNCs, at the cost of a non-constant mass matrix for fully 3D models—a minor downside in the Daets context. Examples in 2D and 3D are presented, with numerical results

Fast automatic differentiation Jacobians by compact LU factorization

For a vector function coded without branches or loops, a code for the Jacobian is generated by interpreting Griewank and Reese's vertex elimination as Gaussian elimination and implementing this as compact LU factorization. Tests on several platforms show such a code is typically 4 to 20 times faster than that produced by tools such as Adifor, Tamc, or Tapenade, on average significantly faster than vertex elimination code produced by the EliAD tool [Tadjouddine et al., in Proceedings of ICCS (2), Lecture Notes in Comput. Sci. 2330, Springer, New York, 2002] and can outperform a hand-coded Jacobian. The LU approach is promising, e.g., for CFD flux functions that are central to assembling Jacobians in finite element or finite volume calculations and, in general, for any inner-loop basic block whose Jacobian is crucial to an overall computation involving derivatives