Optimization methods for large-scale machine learning.

*(English)*Zbl 1397.65085This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, is discussed how optimization problems arise in machine learning and what makes them challenging. A main theme of this study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, a comprehensive theory of a straightforward, yet versatile stochastic gradient algorithm, discussed its practical behavior, and highlight opportunities for designing algorithms with improved performance is presented. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations.

Reviewer: Tiit Riismaa (Tallinn)

##### MSC:

65K05 | Numerical mathematical programming methods |

68Q25 | Analysis of algorithms and problem complexity |

68T05 | Learning and adaptive systems in artificial intelligence |

90C06 | Large-scale problems in mathematical programming |

90C30 | Nonlinear programming |

49Q10 | Optimization of shapes other than minimal surfaces |

##### Keywords:

numerical optimization; machine learning; stochastic gradient methods; algorithm complexity analysis; noise reduction methods; second-order methods##### Software:

AdaGrad; Adam; HOGWILD; L-BFGS; LIBLINEAR; Pegasos; QUIC; RCV1; RMSprop; SGD-QN; TFOCS; TRON##### References:

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