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**Sparse solution of nonnegative least squares problems with applications in the construction of probabilistic Boolean networks.**
*(English)*
Zbl 1349.65140

The authors propose a projection-based gradient descent method for numerical computation of sparse solutions of constrained least squares problems. In each iteration, a projection operator can be also viewed as a soft-thresholding operator where the value of the thresholding is adaptively changing during the iterations. To illustrate the performance of the proposed algorithm, the authors apply the proposed algorithm to solve the inverse problem of constructing a probabilistic Boolean network.

Reviewer: Constantin Popa (Constanţa)

### MSC:

65F20 | Numerical solutions to overdetermined systems, pseudoinverses |

65F10 | Iterative numerical methods for linear systems |

65F22 | Ill-posedness and regularization problems in numerical linear algebra |

65C50 | Other computational problems in probability (MSC2010) |

### Keywords:

gradient descent method; least squares; projection; probabilistic Boolean networks; sparse solutions; algorithm; inverse problem### Software:

Matlab
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\textit{Y.-W. Wen} et al., Numer. Linear Algebra Appl. 22, No. 5, 883--899 (2015; Zbl 1349.65140)

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