Stochastic Gradient Descent with Variance Reduction

Transcription

1 Stochastic Gradient Descent with Variance Reduction Rie Johnson, Tong Zhang Presenter: Jiawen Yao March 17, 2015 Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

2 Outline 1 Problem 2 Stochastic Average Gradient (SAG) 3 Accelerating SGD using Predictive Variance Reduction (SVRG) 4 Conclusion Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

3 Problem Outline 1 Problem 2 Stochastic Average Gradient (SAG) 3 Accelerating SGD using Predictive Variance Reduction (SVRG) 4 Conclusion Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

4 Problem f (αx 1 + (1 α)x 2 ) αf (x 1 ) + (1 α)f (x 2 ) 1 2 α(1 α)β x 1 x 2 2 (2) Preliminaries Recall a few definitions from convex analysis. Definition 1. A function f (x) is a L-Lipschitz continuous function if f (x 1 ) f (x 2 ) L x 1 x 2 (1) for all x 1, x 2 dom(f ) Definition 2. A convex function f (x) is β-strong convex if there exists a constant β > 0 and for any α [0, 1], it holds: Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

5 Problem Preliminaries When f (x) is differentiable, the strong convexity is equivalent to f (x 1 ) f (x 2 )+ < f (x 2 ), x 1 x 2 > + β 2 x 1 x 2 2 (3) Typically, we use the standard Euclidean norm to define Lipschitz and strong convex functions. Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

6 Problem Minimizing finite average of convex functions Let ψ 1,..., ψ n be a sequence of vector functions from R d to R. minp(ω), P(ω) = 1 n n ψ i (ω) (4) i=1 assumptions: each ψ i (ω) is convex and differentiable on dom(r) each ψ i (ω) is smooth with Lipschitz constant L ψ i (ω) ψ i (ω ) L ω ω (5) P(ω) is strongly convex P(ω) P(ω ) + γ 2 ω ω 2 + P(ω ) (ω ω ) (6) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

7 Problem Gradient Descent ω (t) = ω (t 1) η t P(ω (t 1) ) = ω (t 1) η t n n ψ i (ω (t 1) ) (7) i=1 Stochastic Gradient Descent Draw i t randomly from {1,..., n} ω (t) = ω (t 1) η t ψ it (ω (t 1) ) (8) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

8 Problem SGD A more general version of SGD is the following ω (t) = ω (t 1) η t g t (ω (t 1), ξ t ) (9) where ξ t is a random variable that may depend on ω (t 1), the expectation E[g t (ω (t 1), ξ t ) ω (t 1) ] = P(ω (t 1) ) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

9 Problem Variance For general convex optimization, stochastic gradient descent methods can obtain an O(1/ T ) convergence rate in expectation. Randomness introduces large variance if g t (ω (t 1), ξ t ) is very large, it will slow down the convergence. Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

10 Stochastic Average Gradient (SAG) Outline 1 Problem 2 Stochastic Average Gradient (SAG) 3 Accelerating SGD using Predictive Variance Reduction (SVRG) 4 Conclusion Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

11 Stochastic Average Gradient (SAG) Stochastic Average Gradient SAG method (Le Roux, Schmidt, Bach 2012) ω t = ω t 1 η n n g / t (i) (10) i=1 where g (i) t = { ψi (ω t ), if i = i t g (i) t 1, otherwise (11) It needs to store all gradient, not practical for some cases Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

12 Outline 1 Problem 2 Stochastic Average Gradient (SAG) 3 Accelerating SGD using Predictive Variance Reduction (SVRG) 4 Conclusion Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

13 SVRG Motivation Reduce the variance Stochastic gradient descent has slow convergence asymptotically due to the inherent variance. SAG needs to store all gradients Contribution No need to store the intermediate gradients The same convergence rate as SAG can obtain Under mild assumptions, even work on nonconvex cases Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

14 Stochastic variance reduced gradient (SVRG) SVRG (Johnson & Zhang, NIPS 2013) update form ω (t) = ω (t 1) η t ( ψ it (ω (t 1) ) ψ it ( ω) + P( ω)) (12) update ω periodically (every m SGD iterations) ~ it ( ) it ( k ) ~ P ~ ) ~ ( it k P ~ ) ~ ( it P( ~ ) P( k ) Figure: Intuition of variance reduction Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

15 Procedure SVRG input: update frequency m and learning rate η initialization: ω 0 for s=1,2,... do ω = ω s 1 µ = P( ω) = 1 n n i=1 ψ i( ω) ω 0 = ω Randomly pick i t {1,..., n} and update weight, repeat m times ω t = ω t 1 η t ( ψ it (ω t 1 ) ψ it ( ω) + P( ω)) option I: set ω s = ω m option II: set ω s = ω t for randomly chosen t {0,..., m 1} end for Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

16 Convergence for SVRG Theorem Consider SVRG with option II. Assume that all ψ i (ω) are convex and smooth, P(ω) is strongly convex. Let ω = argmin ω P(ω). Assume that m is sufficiently large so that α = 1 γη(1 2Lη)m + 2Lη 1 2Lη < 1 then we have geometric convergence in expectations for SVRG EP( ω s ) EP( ω ) + α s [P( ω 0 ) P(ω )] Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

17 Proof Given any i, consider g i (ω) = ψ i (ω) ψ i (ω ) ψ i (ω ) T (ω ω ) (13) where g i (ω ) = argmin ω g i (ω) and g i (ω ) = 0 0 = g i (ω ) min η [g i (ω η g i (ω))] min η [g i (ω) η g i (ω) Lη 2 g i (ω) 2 ] (14) Here it uses a well-known inequality for a function with 1/L-Lipschitz continuous gradient f (x) f (y) f (y), x y L x y 2 2 Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

18 Proof From (14), we can get η = 1/L, then It can be rewrite as 0 = g i (ω ) g i (ω) 1 2L g i(ω) 2 (15) g i (ω) 2 2Lg i (ω) (16) using the definition of g i (ω) and g i (ω) = ψ i (ω) ψ i (ω ), the (16) will be ψ i (ω) ψ i (ω ) 2 2L[ψ i (ω) ψ i (ω ) ψ i (ω ) T (ω ω )] (17) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

19 Proof By summing the inequality (17) over i = {1,..., n}, the fact that P(ω) = 1 n n i=1 ψ i(ω) and P(ω ) = 0, we can get n 1 n ψ i (ω) ψ i (ω ) 2 2L[P(ω) P(ω )] (18) i=1 Use µ = P( ω) and let v t = ψ it (ω t 1 ) ψ it ( ω) + µ, v t is the approximate gradient of SVRG. Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

20 Proof With respect to i t, expectation can be obtained as E v t 2 = E ψ it (ω t 1 ) ψ it ( ω) + µ 2 2E ψ it (ω t 1 ) ψ it (ω ) 2 + 2E [ ψ it ( ω) ψ it (ω )] µ 2 = 2E ψ it (ω t 1 ) ψ it (ω ) 2 + 2E [ ψ it ( ω) ψ it (ω )] E[ ψ it ( ω) ψ it (ω )] 2 2E ψ it (ω t 1 ) ψ it (ω ) 2 + 2E ψ it ( ω) ψ it (ω ) 2 4L[P(ω t 1 ) P(ω ) + P( ω) P(ω )] (19) The first inequality uses a + b 2 2 a b 2 The second inequality uses E X EX 2 E X 2 The third one uses (18) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

21 Proof The update form of SVRG is ω t = ω t 1 ηv t, conditioned on ω t 1 E ω t ω 2 = E ω t 1 ω ηv t 2 = ω t 1 ω 2 2η(ω t 1 ω ) Ev t + η 2 E v t 2 Here Ev t = E[ ψ it (ω t 1 ) ψ it ( ω) + µ] = P(ω t 1 ) Using (19) then we can get E ω t ω 2 By convexity of P(ω) that ω t 1 ω 2 2η(ω t 1 ω ) P(ω t 1 ) + 4Lη 2 [P(ω t 1 ) P(ω ) + P( ω) P(ω )] (20) (ω t 1 ω ) P(ω t 1 ) P(ω ) P(ω t 1 ) (21) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

22 Proof E ω t ω 2 ω t 1 ω 2 2η[P(ω ) P(ω t 1 )] + 4Lη 2 [P(ω t 1 ) P(ω ) + P( ω) P(ω )] = ω t 1 ω 2 2η(1 2Lη)[P(ω t 1 ) P(ω )] + 4Lη 2 [P( ω) P(ω )] In each fixed stage s, ω = ω s 1 and ω s is selected after all updates have completed. By summing the inequality over t = 1,..., m, taking expectation with all the history E ω m ω 2 + 2η(1 2Lη)mE[P( ω s ) P(ω )] E ω ω 2 + 4Lmη 2 E[P( ω) P(ω )] (22) 2 γ E[P( ω) P(ω )] + 4Lmη 2 E[P( ω) P(ω )] (23) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

23 Proof From above inequality, we can have 2η(1 2Lη)mE[P( ω s ) P(ω )] 2 γ E[P( ω) P(ω )] + 4Lmη 2 E[P( ω) P(ω )] (24) which can be also rewrite as E[P( ω s ) P( ω )] αe[p( ω s 1 ) P(ω )] (25) where α = 1 γη(1 2Lη)m + 2Lη 1 2Lη (26) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

24 Proof From (25), we can get the desired bound in the Theorem E[P( ω s ) P( ω )] α s E[P( ω 0 ) P(ω )] (27) The bound in Theorem 1 is comparable to Le Roux et al. [2012] and Shalev-Shwartz and Zhang [2012]. The convergence rate of SVRG is O(1/T ) which improves the standard SGD convergence rate of O(1/ T ) Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

25 Experiments Figure: (a) Training loss comparison with SGD with fixed learning rates. (b) Training loss residual P(ω) P(ω ) (c) Variance of weight update It is hard to find a good η for SGD. Use a single relatively large value of η, SVRG smoothly goes down faster than SGD. Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

26 Experiments Figure: More convex-case results. Loss residual P(ω) P(ω ) (top) and test error rates (down) SVRG is competitive with SDCA and better than the best-tuned SGD. Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

27 Figure: Neural net results (nonconvex) For nonconvex problems, it is useful to start with an initial vector ω 0 that is close to a local minimum. Results show that SVRG reduces the variance and smoothly converges faster than the best-tuned SGD. Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

28 Conclusion Conclusion For smooth and strongly convex functions, we prove SVRG enjoys the same fast convergence rate as SAG Unlike SAG, no requirement of the storage of gradients Unlike SAG, it is more easily applicable to complex problems Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29

29 Conclusion Thank you! Rie Johnson, Tong Zhang Presenter: JiawenStochastic Yao Gradient Descent with Variance Reduction March 17, / 29