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A new smoothing modified three-term conjugate gradient method for $l_{1}$-norm minimization problem

机译:$ l_ {1} $-范数最小化问题的一种新的平滑修正的三项共轭梯度方法

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We consider a kind of nonsmooth optimization problems with (l_{1})-norm minimization, which has many applications in compressed sensing, signal reconstruction, and the related engineering problems. Using smoothing approximate techniques, this kind of nonsmooth optimization problem can be transformed into a general unconstrained optimization problem, which can be solved by the proposed smoothing modified three-term conjugate gradient method. The smoothing modified three-term conjugate gradient method is based on Polaka??Ribi?¨rea??Polyak conjugate gradient method. For the Polaka??Ribi?¨rea??Polyak conjugate gradient method has good numerical properties, the proposed method possesses the sufficient descent property without any line searches, and it is also proved to be globally convergent. Finally, the numerical experiments show the efficiency of the proposed method.
机译:我们考虑一种具有(l_ {1} )-范数最小化的非平滑优化问题,该问题在压缩感知,信号重建及相关工程问题中有许多应用。使用平滑近似技术,可以将这种非平滑优化问题转化为一般的无约束优化问题,可以通过提出的平滑改进的三项共轭梯度法解决该问题。平滑修正的三项共轭梯度法是基于Polaka ?? Ribi?rea?Polyak共轭梯度法的。由于Polaka ?? Ribi?rea?Polyak共轭梯度法具有良好的数值特性,该方法具有足够的下降特性而无需进行任何线搜索,并且被证明是全局收敛的。最后,数值实验表明了该方法的有效性。

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