High Order Methods for Empirical Risk Minimization

Transcription

1 High Order Methods for Empirical Risk Minimization Alejandro Ribeiro Department of Electrical and Systems Engineering University of Pennsylvania IPAM Workshop of Emerging Wireless Systems February 7th, 2017 Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 1

2 Introduction Introduction Incremental quasi-newton algorithms Adaptive sample size algorithms Conclusions Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 2

3 Optimal resource allocation in wireless systems Wireless channels characterized by random fading coefficients h Want to assign power p(h) as a function of fading to: Satisfy prescribed constraints, e.g., average power, target SINRs Optimize given criteria, e.g., maximize capacity, minimize power Two challenges Resultant optimization problems are infinite dimensional (h is) In most cases problems are not convex However, duality gap is null under mild conditions (Ribeiro-Giannakis 10) And in the dual domain the problem is finite dimensional and convex Motivate use of stochastic optimization algorithms that Have manageable computational complexity per iteration Use sample channel realizations instead of channel distributions Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 3

4 Large-scale empirical risk minimization We aim to solve expected risk minimization problem min w R p E θ [f (w, θ)] The distribution is unknown We have access to N independent realizations of θ We settle for solving the Empirical Risk Minimization (ERM) problem 1 min F (w) := min w Rp w R p N N f (w, θ i ) i=1 Large-scale optimization or machine learning: large N, large p N: number of observations (inputs) p: number of parameters in the model Not just wireless Many (most) machine learning algorithms reduce to ERM problems Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 4

5 Optimization methods Stochastic methods: a subset of samples is used at each iteration SGD is the most popular; however, it is slow because of Noise of stochasticity Variance reduction (SAG, SAGA, SVRG,...) Poor curvature approx. Stochastic QN (SGD-QN, RES, olbfgs,...) Decentralized methods: samples are distributed over multiple processors Primal methods: DGD, Acc. DGD, NN,... Dual methods: DDA, DADMM, DQM, EXTRA, ESOM,... Adaptive sample size methods: start with a subset of samples and increase the size of training set at each iteration Ada Newton The solutions are close when the number of samples are close Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 5

6 Incremental quasi-newton algorithms Introduction Incremental quasi-newton algorithms Adaptive sample size algorithms Conclusions Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 6

7 Incremental Gradient Descent Objective function gradients s(w) := F (w) = 1 N N f (w, θ i ) (Deterministic) gradient descent iteration w t+1 = w t ɛ t s(w t) Evaluation of (deterministic) gradients is not computationally affordable i=1 Incremental/Stochastic gradient Sample average in lieu of expectations ŝ(w, θ) = 1 L f (w, θ l ) θ = [θ 1;...; θ L ] L l=1 Functions are chosen cyclically or at random with or without replacement Incremental gradient descent iteration w t+1 = w t ɛ t ŝ(w t, θ t) (Incremental) gradient descent is (very) slow. Newton is impractical Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 7

8 BFGS quasi-newton method Approximate function s curvature with Hessian approximation matrix B 1 t w t+1 = w t ɛ t B 1 t s(w t) Make B t close to H(w t) := 2 F (w t). Broyden, DFP, BFGS Variable variation: v t = w t+1 w t. Gradient variation: r t = s(w t+1) s(w t) Matrix B t+1 satisfies secant condition B t+1v t = r t. Underdetermined Resolve indeterminacy making B t+1 closest to previous approximation B t Using Gaussian relative entropy as proximity condition yields update B t+1 = B t + rtrt t v T Btvtvt T B t t r t v T t B tv t Superlinear convergence Close enough to quadratic rate of Newton BFGS requires gradients Use stochastic/incremental gradients Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 8

9 Stochastic/Incremental quasi-newton methods Online (o)bfgs & Online Limited-Memory (ol)bfgs [Schraudolph et al 07] obfgs may diverge because Hessian approximation gets close to singular Regularized Stochastic BFGS (RES) [Mokhtari-Ribeiro 14] olbfgs does (surprisingly) converge [Mokhtari-Ribeiro 15] Problem solved? Alas. RES and olbfgs have sublinear convergence Same as stochastic gradient descent. Asymptotically not better Variance reduced stochastic L-BFGS (SVRG+oLBFGS) [Mortiz et al 16] Linear convergence rate. But this is not better than SAG, SAGA, SVRG Computationally feasible quasi Newton method with superlinear convergence Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 9

10 Incremental aggregated gradient method Utilize memory to reduce variance of stochastic gradient approximation f t 1 f t it f t N f i t (wt+1 ) f t+1 1 f t+1 it f t+1 N Descend along incremental gradient w t+1 = w t α N N i=1 f t i Select update index i t cyclically. Uniformly at random is similar = w t αg t i Update gradient corresponding to function f it f t+1 i t = f it (w t+1 ) Sum easy to compute g t+1 i = gi t f t+1 i t + f t+1 i t. Converges linearly Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 10

11 Incremental BFGS method Keep memory of variables z t i, Hessian approximations B t i, and gradients fi t Functions indexed by i. Time indexed by t. Select function f it at time t z t 1 z t it z t N B t 1 B t it B t N f t 1 f t it f t N w t+1 All gradients, matrices, and variables used to update w t+1 Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 11

12 Incremental BFGS method Keep memory of variables z t i, Hessian approximations B t i, and gradients fi t Functions indexed by i. Time indexed by t. Select function f it at time t z t 1 z t it z t N B t 1 B t it B t N f t 1 f t it f t N w t+1 f i t (wt+1 ) Updated variable w t+1 used to update gradient f t+1 i t = f it (w t+1 ) Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 12

13 Incremental BFGS method Keep memory of variables z t i, Hessian approximations B t i, and gradients fi t Functions indexed by i. Time indexed by t. Select function f it at time t z t 1 z t it z t N B t 1 B t it B t N f t 1 f t it f t N w t+1 B t+1 it f i t (wt+1 ) Update Bi t t to satisfy secant condition for function f it for variable variation z t i t w t+1 and gradient variation f t+1 i t fi t t (more later) Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 13

14 Incremental BFGS method Keep memory of variables z t i, Hessian approximations B t i, and gradients fi t Functions indexed by i. Time indexed by t. Select function f it at time t z t 1 z t it z t N B t 1 B t it B t N f t 1 f t it f t N w t+1 B t+1 it f i t (wt+1 ) z t+1 1 z t+1 it z t+1 N B t+1 1 B t+1 it B t+1 N f t+1 1 f t+1 it f t+1 N Update variable, Hessian approximation, and gradient memory for function f it Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 14

15 Update of Hessian approximation matrices Variable variation at time t for function f i = f it vi t := z t+1 i Gradient variation at time t for function f i = f it ri t := f t+1 i t z t i Update B t i = B t i t to satisfy secant condition for variations v t i and r t i B t+1 i = B t i + rt i r t i T r t i T v t i Bt i v t i v t i T B t i v t i T B t i vt i f t i t We want B t i to approximate the Hessian of the function f i = f it Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 15

16 A naive (in hindsight) incremental BFGS method The key is in the update of w t. Use memory in stochastic quantities ( w t+1 = w t 1 N N i=1 B t i ) 1 ( 1 N N i=1 f t i It doesn t work Better than incremental gradient but not superlinear ) Optimization updates are solutions of function approximations In this particular update we are minimizing the quadratic form f (w) 1 n [ f i (z t i ) + f i (z t i ) T (w w t) + 1 ] n 2 (w wt ) T B t i (w w t ) i=1 Gradients evaluated at z t i. Secant condition verified at z t i The quadratic form is centered at w t. Not a reasonable Taylor series Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 16

17 A proper Taylor series expansion Each individual function f i is being approximated by the quadratic f i (w) f i (z t i ) + f i (z t i ) T (w w t) (w wt ) T B t i (w w t ) To have a proper expansion we have to recenter the quadratic form at z t i f i (w) f i (z t i ) + f i (z t i ) T (w z t i ) (w zt i ) T B t i (w z t i ) I.e., we approximate f (w) with the aggregate quadratic function f (w) 1 N N i=1 [ f i (z t i ) + f i (z t i ) T (w z t i ) + 1 ] 2 (w zt i ) T B t i (w z t i ) This is now a reasonable Taylor series that we use to derive an update Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 17

18 Incremental BFGS Solving this quadratic program yields the update for the IQN method ( w t+1 1 N = N i=1 B t i ) 1 [ 1 N B t i z t i 1 N N i=1 ] N f i (z t i ) i=1 Looks difficult to implement but it is more similar to BFGS than apparent As in BFGS, it can be implemented with O(p 2 ) operations Write as rank-2 update, use matrix inversion lemma Independently of N. True incremental method. Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 18

19 Superlinear convergence rate The functions f i are m-strongly convex. The gradients f i are M-Lipschitz continuous. The Hessians f i are L-Lipschitz continuous Theorem The sequence of residuals w t w in the IQN method converges to zero at a superlinear rate, lim t w t w (1/N)( w t 1 w + + w t N w ) = 0. Incremental method with small cost per iteration converging at superlinear rate Resulting from the use of memory to reduce stochastic variances Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 19

20 Numerical results Quadratic programming f (w) := (1/N) N i=1 wt A i w/2 + b T i w A i R p p is a diagonal positive definite matrix b i R p is a random vector from the box [0, 10 3 ] p N = 1000, p = 100, and condition number (10 2, 10 4 ) Relative error w t w / w 0 w of SAG, SAGA, IAG, and IQN Normalized Error SAG SAGA IAG IQN Normalized Error SAG SAGA IAG IQN Number of Effective Passes (a) small condition number Number of Effective Passes (b) large condition number Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 20

21 Adaptive sample size algorithms Introduction Incremental quasi-newton algorithms Adaptive sample size algorithms Conclusions Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 21

22 Back to the ERM problem Our original goal was to solve the statistical loss problem w := argmin w R p L(w) = argmin E [f (w, Z)] w R p But since the distribution of Z is unknown we settle for the ERM problem w N 1 := argmin L N (w) = argmin w R p w R p N N f (w, z k ) Where the samples z k are drawn from a common distribution ERM approximates actual problem Don t need perfect solution k=1 Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 22

23 Regularized ERM problem From statistical learning we know that there exists a constant V N such that sup L(w) L N (w) V N, w w.h.p. V N = O(1/ N) from CLT. V N = O(1/N) sometimes [Bartlett et al 06] There is no need to minimize L N (w) beyond accuracy O(V N ) This is well known. In fact, this is why we can add regularizers to ERM w N := argmin w R N (w) = argmin L N (w) + cv N w 2 w 2 Adding the term (cv N /2) w 2 moves the optimum of the ERM problem But the optimum w N is still in a ball of order V N around w Goal: Minimize the risk R N within its statistical accuracy V N Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 23

24 Adaptive sample size methods ERM problem R n for subset of n N unif. chosen samples w n := argmin w R n(w) = argmin L n(w) + cvn w 2 w 2 Solutions wm for m samples and wn for n samples are close Find approx. solution w m for the risk R m with m samples Increase sample size to n > m samples Use w m as a warm start to find approx. solution w n for R n If m < n, it is easier to solve R m comparing to R n since The condition number of R m is smaller than R n The required accuracy V m is larger than V n The computation cost of solving R m is lower than R n Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 24

25 Adaptive sample size Newton method Ada Newton is a specific adaptive sample size method using Newton steps Find w m that solves R m to its statistical accuracy V m Apply single Newton iteration w n = w m 2 R n(w m) 1 R n(w m) If m and n close, we have w n within statistical accuracy of R n w n w m This works if statistical accuracy ball of R m is within Newton quadratic convergence ball of R n. Then, w m is within Newton quadratic convergence ball of R n A single Newton iteration yields w n within statistical accuracy of R n Question: How should we choose α? Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 25

26 Assumptions The functions f (w, z) are convex The gradients f (w, z) are M-Lipschitz continuous f (w, z) f (w, z) M w w, for all z. The functions f (w, z) are self-concordant with respect to w for all z Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 26

27 Theoretical result Theorem Consider w m as a V m-optimal solution of R m, i.e., R m(w m) R m(wm) V m, and let n = αm. If the inequalities [ ] 1 2(M +cvm)v 2 m 2(n m) + + ((2+ 2)c c w )(V m V n) cv n nc 1 2 (cv n) , [ 2(n m) 144 V m + (V n m + V m) c w 2 2 (V m V n)] V n n 2 are satisfied, then w n has sub-optimality error V n w.h.p., i.e., R n(w n) R n(w n ) V n, w.h.p. Condition1 w m is in the Newton quadratic convergence ball of R n Condition 2 w n is in the statistical accuracy of R n Condition 2 becomes redundant for large m Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 27

28 Doubling the size of training set Proposition Consider a learning problem in which the statistical accuracy satisfies V m αv n for n = αm and lim n V n = 0. If c is chosen so that ( ) 1/2 2αM + c 2(α 1) αc 1/2 1 4, then, there exists a sample size m such that the conditions in Theorem 1 are satisfied for all m > m and n = αm. We can double the size of training set α = 2 If the size of training set is large enough If the constant c satisfies c > 16(2 M + 1) 2 We achieve the S.A. of the full training set in about 2 passes over the data After inversion of about 3.32 log 10 N Hessians Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 28

29 Adaptive sample size Newton (Ada Newton) Parameters: α 0 = 2 and 0 < β < 1. Initialize: n = m 0 and w n = w m0 with R n(w n) R n(w n ) V n while n N do Update w m = w n and m = n. Reset factor α = α 0 repeat [sample size backtracking loop] 1: Increase sample size: n = min{αm, N}. 2: Comp. gradient: R n(w m) = 1 n n k=1 f (wm, z k) + cv nw m 3: Comp. Hessian: 2 R n(w m) = 1 n n k=1 2 f (w m, z k ) + cv ni 4: Update the variable: w n = w m 2 R n(w m) 1 R n(w m) 5: Backtrack sample size increase α = βα. until R n(w n) R n(w n ) V n end while Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 29

30 Numerical results LR problem Protein homology dataset provided (KDD cup 2004) Number of samples N = 145, 751, dimension p = 74 Parameters V n = 1/n, c = 20, m 0 = 124, and α = SGD SAGA Newton Ada Newton RN(w) R N Number of passes Ada Newton achieves the statistical accuracy of the full training set with about two passes over the dataset Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 30

31 Numerical results We use A9A and SUSY datasets to train a LR problem A9A: N = 32, 561 samples with dimension p = 123 SUSY: N = 5, 000, 000 samples with dimension p = 18 The green line shows the iteration at which Ada Newton reached convergence on the test set Ada Newton Newton SAGA Ada Newton Newton SAGA RN(w) R N RN(w) R N Number of passes Number of passes Figure: Suboptimality vs No. of effective passes. A9A (left) and SUSY (right) Ada Newton achieves the accuracy of R N (w) R N < 1/N by less than 2.3 passes over the full training set Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 31

32 Numerical results We use A9A and SUSY datasets to train a LR problem A9A: N = 32, 561 samples with dimension p = 123 SUSY: N = 5, 000, 000 samples with dimension p = 18 The green line shows the iteration at which Ada Newton reached convergence on the test set Ada Newton Newton SAGA Ada Newton Newton SAGA RN(w) R N RN(w) R N Run time Run time Figure: Suboptimality vs runtime. A9A (left) and SUSY (right) Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 32

33 Numerical results We use A9A and SUSY datasets to train a LR problem A9A: N = 32, 561 samples with dimension p = 123 SUSY: N = 5, 000, 000 samples with dimension p = Ada Newton Ada Newton 0.65 Newton SAGA 0.65 Newton SAGA Error 0.5 Error Number of passes Number of passes Figure: Test error vs No. of effective passes. A9A (left) and SUSY (right) Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 33

34 Ada Newton and the challenges for Newton in ERM There are four reasons why it is impractical to use Newton s method in ERM It is costly to compute Hessians (and gradients). Order O(Np 2 ) operations It is costly to invert Hessians. Order O(p 3 ) operations. A line search is needed to moderate stepsize outside of quadratic region Quadratic convergence is advantageous close to the optimum but we don t want to optimize beyond statistical accuracy Ada Newton (mostly) overcomes these four challenges Compute Hessians for a subset of samples. Two passes over dataset Hessians are inverted in a logarithmic number of steps. But still There is no line search We enter quadratic regions without going beyond statistical accuracy Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 34

35 Conclusions Introduction Incremental quasi-newton algorithms Adaptive sample size algorithms Conclusions Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 35

36 Conclusions We studied different approaches to solve large-scale ERM problems An incremental quasi-newton BFGS method (IQN) was presented IQN only computes the information of a single function at each step Low computation cost IQN aggregates variable, gradient, and BFGS approximation Reduce the noise Superlinear convergence Ada Newton resolves the Newton-type methods drawbacks Unit stepsize No line search Not sensitive to initial point less Hessian inversions Exploits quadratic convergence of Newton s method at each iteration Ada Newton achieves statistical accuracy with about two passes over the data Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 36

37 References N. N. Schraudolph, J. Yu, and S. Gunter, A Stochastic Quasi-Newton Method for Online Convex Optimization, In AISTATS, vol. 7, pp , A. Mokhtari and A. Ribeiro, RES: Regularized Stochastic BFGS Algorithm, IEEE Trans. on Signal Processing (TSP), vol. 62, no. 23, pp , December A. Mokhtari and A. Ribeiro, Global Convergence of Online Limited Memory BFGS, Journal of Machine Learning Research (JMLR), vol. 16, pp , P. Moritz, R. Nishihara, and M. I. Jordan, A Linearly-Convergent Stochastic L-BFGS Algorithm, Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), pp , 2016 A. Mokhtari, M. Eisen, and A. Ribeiro, An Incremental Quasi-Newton Method with a Local Superlinear Convergence Rate, in Proc. Int. Conf. Acoustics Speech Signal Process. (ICASSP), New Orleans, LA, March A. Mokhtari, H. Daneshmand, A. Lucchi, T. Hofmann, and A. Ribeiro, Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy, In Advances in Neural Information Processing Systems (NIPS), Barcelona, Spain, Alejandro Ribeiro High Order Methods for Empirical Risk Minimization 37