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## Weitere Kapitel dieses Buchs durch Wischen aufrufen

2022 | OriginalPaper | Buchkapitel

# Recent Theoretical Advances in Non-Convex Optimization

verfasst von: Marina Danilova, Pavel Dvurechensky, Alexander Gasnikov, Eduard Gorbunov, Sergey Guminov, Dmitry Kamzolov, Innokentiy Shibaev

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## Abstract

Motivated by recent increased interest in optimization algorithms for non-convex optimization in application to training deep neural networks and other optimization problems in data analysis, we give an overview of recent theoretical results on global performance guarantees of optimization algorithms for non-convex optimization. We start with classical arguments showing that general non-convex problems could not be solved efficiently in a reasonable time. Then we give a list of problems that can be solved efficiently to find the global minimizer by exploiting the structure of the problem as much as it is possible. Another way to deal with non-convexity is to relax the goal from finding the global minimum to finding a stationary point or a local minimum. For this setting, we first present known results for the convergence rates of deterministic first-order methods, which are then followed by a general theoretical analysis of optimal stochastic and randomized gradient schemes, and an overview of the stochastic first-order methods. After that, we discuss quite general classes of non-convex problems, such as minimization of α-weakly quasi-convex functions and functions that satisfy Polyak–Łojasiewicz condition, which still allow obtaining theoretical convergence guarantees of first-order methods. Then we consider higher-order and zeroth-order/derivative-free methods and their convergence rates for non-convex optimization problems.
Fußnoten
1
See also this webpage with the list of references being updated https://​sunju.​org/​research/​nonconvex/​.

2
By ϕ(a), where $$a = (a_1,\ldots ,a_n)^\top \in {\mathbb R}^n$$ is multidimensional vector, we mean vector (ϕ(a1), …, ϕ(an)).

3
Here $$\mathbb {E}_{\xi _k}[\cdot ]$$ is a mathematical expectation conditioned on everything despite ξk, i.e., expectation is taken w.r.t. the randomness coming only from ξk.

4
In the original paper , the authors considered more general situation when stochastic realizations f(x, ξ) have Hölder-continuous gradients.

5
This technique is applied in distributed optimization to reduce the overall communication cost (e.g., see [4, 27, 113]). However, methods for distributed optimization are out of scope of our survey.

6
For simplicity, we neglect all parameters except m and ε, see the details in Table 2.

7
To distinguish exponents from superindexes, we use braces (⋅) for exponents.

8
In fact, most of the results from  do not rely on the finite-sum structure of f.

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Titel
Recent Theoretical Advances in Non-Convex Optimization
verfasst von
Marina Danilova
Pavel Dvurechensky
Alexander Gasnikov
Eduard Gorbunov
Sergey Guminov
Dmitry Kamzolov
Innokentiy Shibaev