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Second-Order Methods

In: Lectures on Convex Optimization

Author

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  • Yurii Nesterov

    (Catholic University of Louvain)

Abstract

In this chapter, we study Black-Box second-order methods. In the first two sections, these methods are based on cubic regularization of the second-order model of the objective function. With an appropriate proximal coefficient, this model becomes a global upper approximation of the objective function. At the same time, the global minimum of this approximation is computable in polynomial time even if the Hessian of the objective is not positive semidefinite. We study global and local convergence of the Cubic Newton Method in convex and non-convex cases. In the next section, we derive the lower complexity bounds and show that this method can be accelerated using the estimating sequences technique. In the last section, we consider a modification of the standard Gauss–Newton method for solving systems of nonlinear equations. This modification is also based on an overestimating principle as applied to the norm of the residual of the system. Both global and local convergence results are justified.

Suggested Citation

  • Yurii Nesterov, 2018. "Second-Order Methods," Springer Optimization and Its Applications, in: Lectures on Convex Optimization, edition 2, chapter 0, pages 241-322, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-91578-4_4
    DOI: 10.1007/978-3-319-91578-4_4
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