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Paper / 2024SMQ / 190603078805847
Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing
Atithi Acharya / Dylan Herman / Grant Salton / Helmut G. Katzgraber / Marco Pistoia / Martin J. A. Schuetz / Pierre Minssen / Romina Yalovetzky / Ruben S. Andrist / Rudy Raymond / Ruslan Shaydulin / Shouvanik Chakrabarti / Yue Sun
algorithms / optimizationResource overview

Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing

Paper2024

by Atithi Acharya, Dylan Herman, Grant Salton, Helmut G. Katzgraber, Marco Pistoia, Martin J. A. Schuetz, Pierre Minssen, Romina Yalovetzky, Ruben S. Andrist, Rudy Raymond, Ruslan Shaydulin, Shouvanik Chakrabarti, Yue Sun

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algorithmsoptimizationportfolio optimization

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Suggested foundations for portfolio construction and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. Eigenvalues and Covariance Matrices

    Understand the covariance structure of portfolio risk.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

Go further

  1. Mean-Variance Optimisation

    Construct allocations and examine sensitivity to estimated inputs.

  2. Portfolio Constraints

    Turn mathematical weights into feasible portfolio decisions.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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Atithi Acharya, Dylan Herman, Grant Salton, Helmut G. Katzgraber, Marco Pistoia, Martin J. A. Schuetz, Pierre Minssen, Romina Yalovetzky, Ruben S. Andrist, Rudy Raymond, Ruslan Shaydulin, Shouvanik Chakrabarti, Yue Sun

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