Abstract

Using exact computer arithmetic, it is possible to determine the (exact) solution of a numerical model without any rounding error. For such purposes, a corresponding system of equations should be exactly defined, either directly or by rationalising the numerically given input data. In the latter case, there is an initial round-off error, but this does not propagate during the solution process. If this system is exactly solved first and then using floating-point arithmetic, the convergence of the numerical method easily follows. As an example, the IRM–CG, which is an alternative to the Conjugate Gradient (CG) method and a special case of the more general Iterated Ritz Method (IRM), is verified. The method is not based on conjugacy; therefore, restarting strategies are not required, while an overrelaxation factor and preconditioning like techniques could be easily adopted. The exact arithmetic approach is introduced by means of a simple example and is then applied to small structural engineering problems. The perturbation of the displacement increment and the different condition numbers of the system matrix are used to check the stability of the algorithm. Interestingly, a large difference in the number of steps between the exact and numerical approaches is detected, even for well-conditioned systems. According to the tests, the IRM-CG may be considered to be stable and useful for not well-posed or well-posed but ill-conditioned models. Because the computer demands and execution time grow enormously with the number of unknowns using this strategy, three possibilities for larger systems are also provided.

Highlights

  • The Iterated Ritz Method (IRM) is an iterative approach to solving the symmetric positive definite (SPD) system Ax 1⁄4 b based on successive minimisation of the corresponding energy

  • If the Conjugate Gradient (CG) is preferred, we suggest that a single IRM-CG step is occasionally executed, before the orthogonality error becomes too large

  • The method is not based on conjugacy; it has several advantages over the classical CG

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Summary

Introduction

The Iterated Ritz Method (IRM) is an iterative approach to solving the symmetric positive definite (SPD) system Ax 1⁄4 b based on successive minimisation of the corresponding energy (the quadratic function). Minimisation of (3) leads to a system of equations that should be solved at each step: AðiÞaðiÞ 1⁄4 rðiÞ (4) This is a very small system, because only several coordinate vectors are applied ðm ≪ nÞ. At each step, coordinate vectors spanning the subspace are created, within which the energy of the system is reduced This is why the small system (4) needs to be solved (most often by some direct solver). For the convergence of the IRM one coordinate vector not orthogonal to the current residual is sufficient It can be rðiþ1Þ itself, or multiplied by some SPD matrix. A previous solution increment pðiÞ contributes to faster convergence, and it is frequently used This vector is known from the previous step, it ‘costs’ nothing, contrary to other vectors that should be generated somehow.

Non-recursive CG-like algorithm without the need to restart
The IRM-CG is equivalent to the CG
Simple illustrative example
Check of the algorithm stability
More general examples
Possibilities for large systems
Conclusion
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