A Proposal of a Mathematical Roadmap for Scilab DRAFT - Projects

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UseCasesAutomotiveIndustryAerospace?ChemistryFinance...LinearAlgebraODEsOptimizationStatistics...ComputationalFeaturesFigure 1: The two dimensions of Scilab : features and use cases.• new features or an update of existing ones.Indeed, it may happen that existing features have performance or accuracy limitations.In some cases, existing features are not allowing to completely solve aparticular use case.This situation is presented in the figure 2, where the round shapes represent theareas defined by existing features, the ”U” symbols represent existing features andthe question marks ”?” represent the new features to be created.The need for update comes in two flavors : sometimes only a part of the featureis to be updated, while sometimes the whole component is to be replaced. Forexample, the ODE solver in Scilab is based on OdePack, which is efficient, butmay be completely replaced by the up-to-date Sun Dials library. In some cases,there a complete domain which is not covered by Scilab. For example, there iscurrently no component to solve systems of partial differential equations associatedwith conservation laws (that is, non linear hyperbolic systems). In some other cases,there are existing features which are almost completely filling a domain, but notexactly. For example, Scilab provide iterative sparse linear algebra solvers, but notthe associated preconditionners. There may be situations where there are a smallnumber of existing features which fills basic situations, but more advanced needs areunsatisfied. For example, the current statistics features are existing in Scilab, butthere are many more advanced statistics features which are missing.6
UseCases?UU????UUComputationalFeatures1.2 What for ?Figure 2: The current state of Scilab.There may be a variety of reasons for the update or the creation of new features inScilab.• A feature is completely missing. For example, there is currently no nonlinearoptimization solver with non linear constraints. This feature is provided bythe fmincon function in Matlab, for example.• Accuracy : the feature exists in Scilab, but its accuracy is poor. For example,we may wonder why the value of sin(2 6 4) is not the same in Scilab on alloperating systems.• Speed : the feature exists in Scilab, but it is slow. For example, the sparse linearalgebra ”lufact” function is slow, compared to the sparse LU decompositionprovided in the UMFPACK module of Scilab.• Documentation : the feature exists, but is so poorly documented that it is,eventually, unusable. For example, the previous versions of Scilab did not provideenough examples for the nonlinear unconstrained ”optim” function. Thetolerances for this functions still lack of documentation, because we actuallydo not know much about their actual use in the source code.• Bugs / limitations / lack of tests : the feature exists, but has bugs. Forexample, the bug #7101 shows that there is a problem with the use of the”roots” function, depending on the operating system. For example, the bug#7569 shows that several inverse distribution functions are associated with alimited precision of 8 digits (instead of the upper limit 17 digits).7
Page 2 and 3: 4 Optimization 134.1 Why it fits in
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Page 8 and 9: 1.3 The current stateIn order to su
Page 10 and 11: softwares. The author compares vari
Page 12 and 13: to use iterative linear algebra sol
Page 14 and 15: 4.3 Suggested library to connectOPT
Page 16 and 17: 6.2 Existing features in MatlabMatl
Page 18 and 19: 7.2 Why it fits in Scilab scopeDOEs
Page 20 and 21: McQueen, Donna Calhoun, Peter Bloss
Page 22 and 23: The practical situation is that we

A Proposal of a Mathematical Roadmap for Scilab DRAFT - Projects

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