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Analysis of biased stochastic gradient descent using sequential semidefinite programs


We present a convergence rate analysis for biased stochastic gradient descent (SGD), where individual gradient updates are corrupted by computation errors. We develop stochastic quadratic constraints to formulate a small linear matrix inequality (LMI) whose feasible points lead to convergence bounds of biased SGD. Based on this LMI condition, we develop a sequential minimization approach to analyze the intricate trade-offs that couple stepsize selection, convergence rate, optimization accuracy, and robustness to gradient inaccuracy. We also provide feasible points for this LMI and obtain theoretical formulas that quantify the convergence properties of biased SGD under various assumptions on the loss functions.

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  1. 1.

    When \(\delta =c=0\), this rate bound does not reduce to \(\rho ^2=1-2m\alpha +O(\alpha ^2)\). This is due to the inherent differences between the analyses of biased SGD and the standard SGD. See Remark 4 for a detailed explanation.

  2. 2.

    This case is a variant of the common assumption \(\frac{1}{n}\sum _{i=1}^n\left\| \nabla f_i(x)\right\| ^2 \le \beta \). One can check that this case holds for several \(\ell _2\)-regularized problems including SVM and logistic regression.

  3. 3.

    The loss functions for SVM are non-smooth, and \(u_k\) is actually updated using the subgradient information. For simplicity, we abuse our notation and use \(\nabla f_i\) to denote the subgradient of \(f_i\) for SVM problems.

  4. 4.

    Ensuring such a condition in practice can be challenging for many cases since it heavily relies on the estimations of problem parameters.

  5. 5.

    When \(M_{21}=0\), this condition always holds. When \(\delta =0\), this condition is equivalent to \(M_{21}\alpha \le 1\). Hence the above corollary can be directly applied if \(M_{21}=0\) or \(\delta =0\). If \(M_{21}> 0\) and \(\delta >0\), the condition \(M_{21}\left( \alpha +\frac{\delta ^2}{m}\right) \le 1\) can be rewritten as a condition on \(\alpha \) in a case-by-case manner.


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Correspondence to Bin Hu.

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This work is supported by the NSF Awards 1656951, 1750162, 1254129, and the NASA Langley NRA Cooperative Agreement NNX12AM55A. Bin Hu and Laurent Lessard also acknowledge support from the Wisconsin Institute for Discovery, the College of Engineering, and the Department of Electrical and Computer Engineering at the University of Wisconsin–Madison.



Proof of Theorem 1

First notice that since \(i_k\) is uniformly distributed on \(\{1,\dots ,n\}\) and \(x_k\) and \(i_k\) are independent, we have:

$$\begin{aligned} \,{\mathbb {E}} \bigl ( u_k \,\big |\, x_k \bigr ) = \,{\mathbb {E}} \bigl ( \nabla f_{i_k}(x_k) \,\big |\, x_k \bigr ) = \frac{1}{n}\sum _{i=1}^n \nabla f_i(x_k) = \nabla g(x_k) \end{aligned}$$

Consequently, we have:

$$\begin{aligned} \,{\mathbb {E}} \left( \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}^{\mathsf {T}}\! \begin{bmatrix} -2mI_p &{} I_p \\ I_p &{} 0_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}\bigg |\ x_k\right) = \begin{bmatrix} x_k-x_\star \\ \nabla g(x_k) \end{bmatrix}^{\mathsf {T}}\! \begin{bmatrix} -2m I_p &{} I_p\\ I_p &{} 0_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ \nabla g(x_k) \end{bmatrix} \ge 0 \end{aligned}$$

where the inequality in (54) follows from the definition of \(g \in {\mathcal {S}}(m,\infty )\).

Next we prove (12), let’s start with Case I, the boundedness constraint \(\Vert \nabla f_i(x_k)\Vert \le \beta \) implies that \(\left\| u_k\right\| \le \beta \) for all k. Rewrite as a quadratic form to obtain:

$$\begin{aligned} \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}^{\mathsf {T}}\begin{bmatrix} 0_p &{}\quad 0_p \\ 0_p &{}\quad -I_p \end{bmatrix} \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix} \ge -\beta ^2 \end{aligned}$$

The boundedness constraint \(\Vert \nabla f_i(x_k)-mx_k\Vert \le \beta \) implies that:

$$\begin{aligned} \left\| u_k-m(x_k-x_\star )\right\| ^2&\le \left\| (u_k-mx_k)+mx_\star \right\| ^2 + \left\| (u_k-mx_k)-mx_\star \right\| ^2\\&=2\left\| u_k-mx_k\right\| ^2 + 2m^2\left\| x_\star \right\| ^2 \\&\le 2\beta ^2 + 2m^2\left\| x_\star \right\| ^2 \end{aligned}$$

As in the proof of Case I, rewrite the above inequality as a quadratic form and we obtain the second row of Table 1.

To prove the three remaining cases, we begin by showing that an inequality of the following form holds for each \(f_i\):

$$\begin{aligned} \begin{bmatrix} x_k-x_\star \\ \nabla f_i(x_k)-\nabla f_i(x_\star ) \end{bmatrix}^{\mathsf {T}}\begin{bmatrix} M_{11}I_p &{}\quad M_{12}I_p \\ M_{21}I_p &{}\quad -2I_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ \nabla f_i(x_k)-\nabla f_i(x_\star ) \end{bmatrix} \ge 0 \end{aligned}$$

The verification for (56) follows directly from the definitions of L-smoothness and convexity. In the smooth case (Definition 1), for example, \(\Vert \nabla f_i(x_k)-\nabla f_i(x_\star )\Vert \le L\Vert x_k-x_\star \Vert \). So (56) holds with \(M_{11}=2L^2\), \(M_{12}=M_{21}=0\). The cases for \({\mathcal {F}}(0,L)\) and \({\mathcal {F}}(m,L)\) follow directly from Definition 2. In Table 1, we always have \(M_{22}=-1\). Therefore,

$$\begin{aligned}&\,{\mathbb {E}} \left( \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}^{\mathsf {T}}\begin{bmatrix} M_{11}I_p &{} M_{12}I_p \\ M_{21}I_p &{} M_{22}I_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}\,\,\bigg |\ \, x_k\right) \nonumber \\&\quad =\frac{1}{n} \sum _{i=1}^n \begin{bmatrix} x_k-x_\star \\ \nabla f_i(x_k)\end{bmatrix}^{\mathsf {T}}\begin{bmatrix} M_{11}I_p &{} M_{12}I_p \\ M_{21}I_p &{} 0_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ \nabla f_i(x_k)\end{bmatrix}- \frac{1}{n} \sum _{i=1}^n \Vert \nabla f_i(x_k)\Vert ^2 \end{aligned}$$

Since \(\frac{1}{n}\sum _{i=1}^n \nabla f_i(x_\star ) = \nabla g(x_\star ) = 0\), the first term on the right side of (57) is equal to

$$\begin{aligned} \frac{1}{n} \sum _{i=1}^n \begin{bmatrix} x_k-x_\star \\ \nabla f_i(x_k)-\nabla f_i(x_\star )\end{bmatrix} \begin{bmatrix} M_{11}I_p &{} M_{12}I_p \\ M_{21}I_p &{} 0_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ \nabla f_i(x_k)-\nabla f_i(x_\star ) \end{bmatrix} \end{aligned}$$

Based on the constraint condition (56), we know that the above term is greater than or equal to \(\frac{2}{n} \sum _{i=1}^n \Vert \nabla f_i(x_k)-\nabla f_i(x_\star ) \Vert ^2\). Substituting this fact back into (57) leads to the inequality:

$$\begin{aligned}&\,{\mathbb {E}} \left( \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}^{\mathsf {T}}\begin{bmatrix} M_{11}I_p &{} M_{12}I_p \\ M_{21}I_p &{} M_{22}I_p\end{bmatrix} \begin{bmatrix} x_k-x_\star \\ u_k \end{bmatrix}\,\,\bigg |\ \, x_k\right) \nonumber \\&\quad \ge \frac{1}{n} \sum _{i=1}^n \left( 2\Vert \nabla f_i(x_k)-\nabla f_i(x_\star ) \Vert ^2- \Vert \nabla f_i(x_k)\Vert ^2\right) \nonumber \\&\quad = \frac{1}{n} \sum _{i=1}^n \left( \Vert \nabla f_i(x_k)-2\nabla f_i(x_\star ) \Vert ^2- 2\Vert \nabla f_i(x_\star )\Vert ^2\right) \nonumber \\&\quad \ge -\frac{2}{n} \sum _{i=1}^n \Vert \nabla f_i(x_\star )\Vert ^2 \end{aligned}$$

Taking the expectation of both sides, we arrive at (12), as desired. Now we are ready to prove our main theorem. By Schur complement, (10) is equivalent to (15), which can be further rewritten as

$$\begin{aligned} \left( \begin{bmatrix} 1-\rho _k^2 &{}\quad -\alpha _k &{}\quad -\alpha _k\\ alpha_k &{}\quad \alpha _k^2 &{}\quad \alpha _k^2 \\ alpha_k &{}\quad \alpha _k^2 &{}\quad \alpha _k^2 \end{bmatrix} +\nu _k\!\begin{bmatrix} -2m &{}\quad 1 &{}\quad 0\\ 1 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 \end{bmatrix} +\lambda _k\!\begin{bmatrix} M_{11} &{}\quad M_{12} &{}\quad 0\\ M_{21} &{}\quad M_{22} &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 \end{bmatrix} +\mu _k\!\begin{bmatrix} 0 &{}\quad 0 &{}\quad 0\\ 0 &{}\quad \delta ^2 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad -1 \end{bmatrix}\right) \otimes I_p\preceq 0 \end{aligned}$$

Since \(x_{k+1}-x_\star =x_k-x_\star -\alpha _k(u_k+e_k)\), we have

$$\begin{aligned} \begin{bmatrix} x_k-x_\star \\ u_k \\ e_k \end{bmatrix}^{\mathsf {T}}\left( \begin{bmatrix} 1 &{}\quad -\alpha _k &{}\quad -\alpha _k\\ alpha_k &{}\quad \alpha _k^2 &{}\quad \alpha _k^2 \\ -\alpha _k &{}\quad \alpha _k^2 &{}\quad \alpha _k^2 \end{bmatrix}\otimes I_p\right) \begin{bmatrix} x_k-x_\star \\ u_k \\ e_k \end{bmatrix}=\Vert x_{k+1}-x_\star \Vert ^2 \end{aligned}$$

Now we can left and right multiply (59) by \([(x_k-x_\star )^{\mathsf {T}}, u_k^{\mathsf {T}}, e_k^{\mathsf {T}}]\) and \([(x_k-x_\star )^{\mathsf {T}}, u_k^{\mathsf {T}}, e_k^{\mathsf {T}}]^{\mathsf {T}}\), and apply the inequalities (4), (54), and (12) to get the desired conclusion. \(\square \)

Proof of Lemma 2

We use an induction argument to prove Item 1. For simplicity, we denote (48) as \(V_{k+1}=h(V_k)\). Suppose we have \(V_k=h(V_{k-1})\) and \(V_{k-1}>V_\star \). We are going to show \(V_{k+1}=h(V_k)\) and \(V_k>V_\star \). We can rewrite (48) as

$$\begin{aligned} V_{k+1}=\,\, \mathop {{{\,\mathrm{minimize}\,}}}\limits _{\zeta > 0} \quad A_k(1+Z_k^{-1}) + B_k(1+Z_k) \end{aligned}$$

where \(A_k\), \(B_k\), and \(Z_k\) are defined as

$$\begin{aligned} A_k&=\frac{m^2 V_k^2 \left( c^2+(2G^2+{\tilde{M}} V_k)\delta ^2\right) }{(2G^2+{\tilde{M}} V_k)^2}\\ B_k&=\frac{(2G^2 V_k+({\tilde{M}}-m^2) V_k^2)((2G^2+{\tilde{M}} V_k)(1-\delta ^2)-c^2)}{(2G^2+{\tilde{M}} V_k)^2}\\ Z_k&=\frac{\bigl (2 G^2+{\tilde{M}} V_k\bigr )(\delta ^2+\zeta _k )+c^2}{(2G^2+{\tilde{M}} V_k)(1-\delta ^2)-c^2} \end{aligned}$$

Note that \(A_k \ge 0\) and \(B_k \ge 0\) due to the condition \(2G^2(1-\delta ^2) \ge c^2\). The objective in (61) therefore has a form very similar to the objective in (26). Applying Lemma 1, we deduce that \(V_{k+1} =(\sqrt{A_k}+\sqrt{B_k})^2\), which is the same as (49). The associated \(Z_k^opt \) is \(\sqrt{\tfrac{A_k}{B_k}}\). To ensure this is a feasible choice, it remains to check that the associated \(\zeta _k^opt > 0\) as well. Via algebraic manipulations, one can show that \(\zeta _k > 0\) is equivalent to \(V_k > V_\star \). We can also verify \(A_k\) is a monotonically increasing function of \(V_k\), and \(B_k\) is a monotonically nondecreasing function of \(V_k\). Hence h is a monotonically increasing function. Also notice \(V_\star \) is a fixed point of (49). Therefore, if we assume \(V_k=h(V_{k-1})\) and \(V_{k-1}>V_\star \), we have \(V_k=h(V_{k-1})>h(V_\star )=V_\star \). Hence we guarantee \(\zeta _k>0\) and \(V_{k+1}=h(V_k)\). By similar arguments, one can verify \(V_1=h(V_0)\). And it is assumed that \(V_0>V_\star \). This completes the induction argument.

Item 2 follows from a similar argument to the one used in Sect. 2.2. Finally, Item 3 can be proven by choosing a sufficiently small constant stepsize \(\alpha \) to make \({\hat{U}}_k\) arbitrarily close to \(V_\star \). Since \(V_\star \le V_k \le {\hat{U}}_k\), we conclude that \(\lim _{k\rightarrow \infty } V_k = V_\star \), as required. \(\square \)

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Hu, B., Seiler, P. & Lessard, L. Analysis of biased stochastic gradient descent using sequential semidefinite programs. Math. Program. 187, 383–408 (2021).

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  • Biased stochastic gradient
  • Robustness to inexact gradient
  • Convergence rates
  • Convex optimization
  • First-order methods

Mathematics Subject Classification

  • 90C06
  • 90C15
  • 90C25
  • 90C30
  • 68Q25
  • 62L20