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End date: 2023 Ă— Start date: 2020 Ă— End date: 2018 Ă— Start date: 2018 Ă— Publisher: Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH Ă— Subject: Algebra and Number Theory Ă—

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Now showing 1 - 4 of 4
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    Computing with symmetries
    (Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH, 2018) Roney-Dougal, Colva M.
    Group theory is the study of symmetry, and has many applications both within and outside mathematics. In this snapshot, we give a brief introduction to symmetries, and how to compute with them.
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    Number theory in quantum computing
    (Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH, 2018) Schönnenbeck, Sebastian
    Algorithms are mathematical procedures developed to solve a problem. When encoded on a computer, algorithms must be "translated" to a series of simple steps, each of which the computer knows how to do. This task is relatively easy to do on a classical computer and we witness the benefits of this success in our everyday life. Quantum mechanics, the physical theory of the very small, promises to enable completely novel architectures of our machines, which will provide specific tasks with higher computing power. Translating and implementing algorithms on quantum computers is hard. However, we will show that solutions to this problem can be found and yield surprising applications to number theory.
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    The Algebraic Statistics of an Oberwolfach Workshop
    (Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH, 2018) Seigal, Anna
    Algebraic Statistics builds on the idea that statistical models can be understood via polynomials. Many statistical models are parameterized by polynomials in the model parameters; others are described implicitly by polynomial equalities and inequalities. We explore the connection between algebra and statistics for some small statistical models.
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    Prony’s method: an old trick for new problems
    (Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH, 2018) Sauer, Tomas
    In 1795, French mathematician Gaspard de Prony invented an ingenious trick to solve a recovery problem, aiming at reconstructing functions from their values at given points, which arose from a specific application in physical chemistry. His technique became later useful in many different areas, such as signal processing, and it relates to the concept of sparsity that gained a lot of well-deserved attention recently. Prony’s contribution, therefore, has developed into a very modern mathematical concept.