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Matrix preconditioning: a robust operation for optical linear algebra processors.

A Ghosh, P Paparao

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    Summary
    This summary is machine-generated.

    This study presents a parallel matrix preconditioning algorithm for optical processors. Numerical experiments confirm the algorithm

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    Area of Science:

    • Computational Science
    • Optical Computing
    • Linear Algebra

    Background:

    • Analog electrooptical processors offer high computational throughput for tasks tolerant to minor inaccuracies.
    • Matrix preconditioning is crucial for improving gradient algorithm convergence and solution accuracy by reducing matrix condition numbers.

    Purpose of the Study:

    • To introduce a straightforward parallel algorithm for matrix preconditioning.
    • To demonstrate the algorithm's efficient implementation on a pipelined optical linear algebra processor.

    Main Methods:

    • Development of a simple parallel algorithm for matrix preconditioning.
    • Implementation and testing on a pipelined optical linear algebra processor.

    Main Results:

    • The proposed parallel algorithm is suitable for optical linear algebra processors.
    • Numerical experiments show minimal impact of optical system errors on preconditioning efficacy.

    Conclusions:

    • The developed matrix preconditioning algorithm is robust in the presence of optical system errors.
    • This approach enhances the practicality of optical processors for numerical computations.