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The PPADMM Method for Solving Quadratic Programming Problems

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  • Hai-Long Shen

    (Department of Mathematics College of Sciences, Northeastern University Shenyang, Shenyang 100819, China)

  • Xu Tang

    (Northwest Institute of Mechanical and Electrical Engineering Xianyang, Xianyang 712000, China)

Abstract

In this paper, a preconditioned and proximal alternating direction method of multipliers (PPADMM) is established for iteratively solving the equality-constraint quadratic programming problems. Based on strictly matrix analysis, we prove that this method is asymptotically convergent. We also show the connection between this method with some existing methods, so it combines the advantages of the methods. Finally, the numerical examples show that the algorithm proposed is efficient, stable, and flexible for solving the quadratic programming problems with equality constraint.

Suggested Citation

  • Hai-Long Shen & Xu Tang, 2021. "The PPADMM Method for Solving Quadratic Programming Problems," Mathematics, MDPI, vol. 9(9), pages 1-15, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:941-:d:542017
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    References listed on IDEAS

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    1. Hansheng Wang & Guodong Li & Chih‐Ling Tsai, 2007. "Regression coefficient and autoregressive order shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 63-78, February.
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