Convex Optimization Algorithms for Machine Learning in 10 Slides

Transcription

1 Convex Optimization Algorithms for Machine Learning in 10 Slides Presenter: Jul

2 Outline 1 Quadratic Problem Linear System 2 Smooth Problem Newton-CG 3 Composite Problem Proximal-Newton-CD 4 Non-smooth, Non-separable Augmented Lagrangian Method

3 Quadratic Problem Problem: w f (w) = 1 2 w T Hw + g T w + c Example: w f (w) = 1 2 y Xw w 2 (1) Solution: solve a linear system f (w) = 0 Hw = g (2) How to solve? In the example, H = X T X + I and g = Xy. X is n d solving linear system directly requires O(d 3 ). Hessian-vector product (Hv) only needs O(nnz(X )). Conjugate Gradient (CG) produces reasonable solution using few iters of Hessian-vector product.

4 Smooth Problem Newton-CG Problem: w f (w), where f (w), 2 f (w) are continuous. Ex. w n f (w) = L(w T x i,y i ) + 1 i=1 2 w 2 (3) where L(z,y) = ln(1 + exp( yz)) is logistic loss 1. Newton-CG, where each iter t we solve a quadratic approximation to find the Newton direction w nt w nt = arg w 1 2 w T H t w + g T t w + f (w t ), and do line search to find step size η t. (w t+1 = w t + η t w nt ) In (4), H t = X T DX + I, where D is diagonal matrix with D ii = L (w T t x i,y i ). Hessian-vector product O(nnz(X )). 1 Lin. et al.. Trust region Newton method for logistic regression. ICML 2007.

5 Outline 1 Quadratic Problem Linear System 2 Smooth Problem Newton-CG 3 Composite Problem Proximal-Newton-CD 4 Non-smooth, Non-separable Augmented Lagrangian Method

6 Composite Problem Problem: w f (w) + h(w), where f (w) is smooth, h(w) is not smooth but separable w.r.t. atoms. Ex. LASSO, L1-regularized Logistic Reg. 2 w Ex. Dual of SVM 3 n f (w) = L(w T x i,y i ) + λ w 1, (4) i=1 α s.t. Ex. Matrix Completion 4 W 1 2 αt Qα n α i i=1 0 α i C,i = 1..n. (5) 1 2 (A ij W ij ) 2 + λ W (6) i,j Ω 2 Yuan et al. An improved glmnet for l1-regularized logistic regression. JMLR Hsieh et al. A dual coordinate descent method for large-scale linear SVM. ICML, Hsieh et al.. Nuclear norm imization via active subspace selection. ICML 2014.

7 Composite Problem Problem: w f (w) + h(w), where f (w) is smooth, h(w) is not smooth but separable w.r.t. atoms. Insight: if f (w) is atomic quadratic function, composite problem is easy to solve. Ex. sign( b a )softthd( b a, λ a ) = arg x a 2 x 2 + bx + λ x. (Google proximal operator for... to find formula you need.) Proximal-Newton-CD: 1. Construct local quadratic approximation q( w;w t ). Solve w = arg w q( w;w t ) + h( w + w t ). (7) via Coordinate Descent (optimize w.r.t. one atom at a time). 2. Do line search to find η t and w t+1 = w t + η t w.

8 Composite Problem Problem: w f (w) + h(w), where f (w) is smooth, h(w) is not smooth but separable w.r.t. atoms. Proximal-Newton-CD: 1. Construct local quadratic approximation q( w;w t ). Solve w = arg w q( w;w t ) + h( w + w t ). (8) via Coordinate Descent (optimize w.r.t. one atom at a time). 2. Do line search to find η t and w t+1 = w t + η t w. Key to efficiency: whether q(.) = H t w + g t can be maintained efficiently after coordinate update. What if not? (ex. Multiclass, CRF) Prox-Quasi-Newton: replace H t with low-rank approximation B t constructed from historical f (w 1 ),..., f (w t 1 ). 5 5 Zhong et al. Proximal Quasi-Newton for Computationally Intensive l1-regularized M-estimators. NIPS 2014.

9 Outline 1 Quadratic Problem Linear System 2 Smooth Problem Newton-CG 3 Composite Problem Proximal-Newton-CD 4 Non-smooth, Non-separable Augmented Lagrangian Method

10 Non-smooth, Non-separable Problem What if the non-smooth function is non-separable? Linear Program: Robust PCA: x,ξ 0 c T x s.t. Ax + ξ = b. L,S s.t. L + λ S 1 L + S = X Reduce it to composite problem by Augmented Lagrangian Method! (9) (10)

11 Non-smooth, Non-separable Problem What if the non-smooth function is non-separable? Linear Program: x,ξ 0 c T x s.t. Ax + ξ = b. -max of Lagrangian: (dual variable α) (11) max c T x + α T (Ax b + ξ ) x,ξ 0 α (12) Augmented Lagrangian Method: (x,ξ ) = arg x,ξ 0 α t+1 = α t + (Ax b + ξ ) c T x + αt T (Ax b + ξ ) + 1 Ax b + ξ 2 2 (13)