Nearly Optimal First-Order Methods for Convex Optimization under Gradient Norm Measure: an Adaptive Regularization Approach


In the development of first-order methods for smooth (resp., composite) convex optimization problems, where smooth functions with Lipschitz continuous gradients are minimized, the gradient (resp., gradient mapping) norm becomes a fundamental optimality measure. Under this measure, a fixed iteration algorithm with the optimal iteration complexity for the smooth case is known, while determining this number of iteration to obtain a desired accuracy requires the prior knowledge of the distance from the initial point to the optimal solution set. In this paper, we report an adaptive regularization approach, which attains the nearly optimal iteration complexity without knowing the distance to the optimal solution set. To obtain further faster convergence adaptively, we secondly apply this approach to construct a first-order method that is adaptive to the Hölderian error bound condition (or equivalently, the Łojasiewicz gradient property), which covers moderately wide classes of applications. The proposed method attains nearly optimal iteration complexity with respect to the gradient mapping norm.

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  1. 1.

    Suppose that \(\varphi \) is an L-smooth convex function satisfying (4) for some exponent \(\rho \in [1,2[\). For \(\rho =1\), Lemma 2.2 implies \(\left\| \nabla {\varphi }(x)\right\| \ge \kappa \) for \(x \not \in \mathop {\text {Argmin}}\varphi \). If \(\rho \in ]1,2[\), on the other hand, Lemmas 2.1 and 2.2 imply \({\frac{1}{2L}\left\| \nabla {\varphi }(x)\right\| ^2 \le \varphi (x)-\varphi ^* \le \kappa ^{-\frac{1}{\rho -1}}\left\| \nabla {\varphi }(x)\right\| ^{\frac{\rho }{\rho -1}}}\) for all x, which yields \(\left\| \nabla {\varphi }(x)\right\| \ge \text {const.}\) for all \(x \not \in \text {Argmin }\varphi \). This contradicts to the continuity of \(\nabla {\varphi }\) at points in \(\text {Argmin }\varphi \).

  2. 2.

    By the strong convexity of \(\left\langle a,x\right\rangle +h(x)\) and \(\left\langle b,x\right\rangle +h(x)\), we have

    $$\begin{aligned} \frac{\mu }{2}\left\| x_a^*-x_b^*\right\| ^2 \le [\left\langle a,x_b^*\right\rangle +h(x_b^*)] - [\left\langle a,x_a^*\right\rangle +h(x_a^*)] ~~\text {and}~~ \frac{\mu }{2}\left\| x_a^*-x_b^*\right\| ^2 \le [\left\langle b,x_a^*\right\rangle +h(x_a^*)]-[\left\langle b,x_b^*\right\rangle +h(x_b^*)], \end{aligned}$$

    respectively. Adding them implies \( \mu \left\| x_a^*-x_b^*\right\| ^2 \le \left\langle a-b,x_b^*-x_a^*\right\rangle \le \left\| a-b\right\| \left\| x_b^*-x_a^*\right\| . \)

  3. 3.

    In fact, since the derivative \(\log (1+x)\) of the function \(h(x):=(1+x)\log (1+x)-x\) is increasing and vanishes at \(x=0\), we have \(\min _{x>-1}h(x)=h(0)=0\).

  4. 4.

    This assumption on \(L_f/L_{\min }\) means that we know a good estimate \(L_{\min }\) close to \(L_f\) in advance.

  5. 5.

    Although (29) is asserted in the case \({x^{(t)}_+} \not \in X^*\), it trivially holds if \({x^{(t)}_+} \in X^*\) unless \(\rho = 1\).


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The authors are thankful to an anonymous referee for valuable comments that improved this paper. This work was partially supported by the Grant-in-Aid for Young Scientists (B) (17K12645) and the Grant-in-Aid for Scientific Research (C) (18K11178) from Japan Society for the Promotion of Science.

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Correspondence to Masaru Ito.

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Ito, M., Fukuda, M. Nearly Optimal First-Order Methods for Convex Optimization under Gradient Norm Measure: an Adaptive Regularization Approach. J Optim Theory Appl 188, 770–804 (2021).

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  • Smooth/composite convex optimization
  • Accelerated proximal gradient methods
  • Hölderian error bound
  • Adaptive methods

Mathematics Subject Classification

  • 90C25
  • 68Q25
  • 49M37