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<p>In this paper, an improved alternating direction method of multipliers (ADMM) algorithm is proposed and applied to solve the Lasso (Least Absolute Shrinkage and Selection Operator) problem. The Lagrange multiplier is enhanced by means of the Nesterov accelerated gradient method, and hence the dual problem can approach the optimal value quickly. Compared with the traditional ADMM algorithm, the proposed algorithm has faster convergence rates. Finally, the simulation example verifies the efficiency of the proposed algorithm.</p>

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Enhanced ADMM for solving the Lasso problem: integrating the Nesterov accelerated gradient method

  • 期刊论文

  • 发表日期：

    2026年2月13日

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  • Shengke Yang,
  • Jing Chen,
  • Yawen Mao,
  • Yang Yi

摘要

In this paper, an improved alternating direction method of multipliers (ADMM) algorithm is proposed and applied to solve the Lasso (Least Absolute Shrinkage and Selection Operator) problem. The Lagrange multiplier is enhanced by means of the Nesterov accelerated gradient method, and hence the dual problem can approach the optimal value quickly. Compared with the traditional ADMM algorithm, the proposed algorithm has faster convergence rates. Finally, the simulation example verifies the efficiency of the proposed algorithm.