Matrix operations for the simulation and immediate reverse-engineering of time series data

Michael A. Idowu, James L. Bown

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)
    142 Downloads (Pure)


    We present a new method for constructing and decomposing square matrices. This method, based on the computed parameterisation of their implied determinants and minors, operates on the product of factors of a new form of matrix decomposition. This method may be employed to build new matrices with fixed determinant(s). We demonstrate that this new approach is fundamentally well-connected to the Cholesky decomposition if applied on symmetric matrices. We also demonstrate that it is related to the LU decomposition method via a diagonal matrix multiplier. Also through this new method a direct relation between Cholesky decomposition and LU factorisation is shown. This method, presented for the first time, is useful for (re)constructing matrices with a predefined determinant and simulating inverse problems. The inference method introduced here also is based on new matrix manipulation techniques that we have developed for the identification of systems from reproducible time series data.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publication2012 14th International Conference on Modelling and Simulation
    EditorsDavid Al-Dabass, Alessandra Orsoni, Richard Cant
    Place of PublicationPiscataway, NJ
    Number of pages6
    ISBN (Electronic)9780769546827
    ISBN (Print)9781467313667
    Publication statusPublished - 2012
    Event14th International Conference on Computer Modelling and Simulation - Cambridge, United Kingdom
    Duration: 28 Mar 201230 Mar 2012
    Conference number: 14


    Conference14th International Conference on Computer Modelling and Simulation
    Abbreviated titleUKSim 2012
    Country/TerritoryUnited Kingdom


    • Inverse problems
    • Mathematical modelling
    • Simulation
    • Optimisation algorithms
    • Network inference
    • Matrix algebra and calculus
    • Time series analysis
    • System identification and parameter estimation methods


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