Optimization for Machine Learning

Transcription

1 Optimization for Machine Learning (Problems; Algorithms - C) SUVRIT SRA Massachusetts Institute of Technology PKU Summer School on Data Science (July 2017)

2 Course materials Some references: Introductory lectures on convex optimization Nesterov Convex optimization Boyd & Vandenberghe Nonlinear programming Bertsekas Convex Analysis Rockafellar Fundamentals of convex analysis Urruty, Lemaréchal Lectures on modern convex optimization Nemirovski Optimization for Machine Learning Sra, Nowozin, Wright Theory of Convex Optimization for Machine Learning Bubeck NIPS 2016 Optimization Tutorial Bach, Sra Some related courses: EE227A, Spring 2013, (Sra, UC Berkeley) , Spring 2014 (Sra, CMU) EE364a,b (Boyd, Stanford) EE236b,c (Vandenberghe, UCLA) Venues: NIPS, ICML, UAI, AISTATS, SIOPT, Math. Prog. Suvrit Sra Optimization for Machine Learning 2 / 29

3 Lecture Plan Introduction (3 lectures) Problems and algorithms (5 lectures) Non-convex optimization, perspectives (2 lectures) Suvrit Sra Optimization for Machine Learning 3 / 29

4 Nonsmooth convergence rates Theorem. (Nesterov.) Let B = { x x x 0 2 D }. Assume, x B. There exists a convex function f in C 0 L (B) (with L > 0), such that for 0 k n 1, the lower-bound f (x k ) f (x ) LD 2(1+ k+1), holds for any algorithm that generates x k by linearly combining the previous iterates and subgradients. Exercise: So design problems where we can do better! Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 4 / 29

5 Composite problems Suvrit Sra Optimization for Machine Learning 5 / 29

6 Composite objectives Frequently ML problems take the regularized form minimize f (x) := l(x) + r(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 6 / 29

7 Composite objectives Frequently ML problems take the regularized form minimize f (x) := l(x) + r(x) l + r Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 6 / 29

8 Composite objectives Frequently ML problems take the regularized form minimize f (x) := l(x) + r(x) l + r Example: l(x) = 1 2 Ax b 2 and r(x) = λ x 1 Lasso, L1-LS, compressed sensing Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 6 / 29

9 Composite objectives Frequently ML problems take the regularized form minimize f (x) := l(x) + r(x) l + r Example: l(x) = 1 2 Ax b 2 and r(x) = λ x 1 Lasso, L1-LS, compressed sensing Example: l(x) : Logistic loss, and r(x) = λ x 1 L1-Logistic regression, sparse LR Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 6 / 29

10 Composite objective minimization minimize f (x) := l(x) + r(x) subgradient: x k+1 = x k α k g k, g k f (x k ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 7 / 29

11 Composite objective minimization minimize f (x) := l(x) + r(x) subgradient: x k+1 = x k α k g k, g k f (x k ) subgradient: converges slowly at rate O(1/ k) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 7 / 29

12 Composite objective minimization minimize f (x) := l(x) + r(x) subgradient: x k+1 = x k α k g k, g k f (x k ) subgradient: converges slowly at rate O(1/ k) but: f is smooth plus nonsmooth we should exploit: smoothness of l for better method! Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 7 / 29

13 Proximal Gradient Method min f (x) x X Projected (sub)gradient x P X (x α f (x)) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 8 / 29

14 Proximal Gradient Method min f (x) x X Projected (sub)gradient x P X (x α f (x)) min f (x) + h(x) Proximal gradient x prox αh (x α f (x)) prox αh denotes proximity operator for h Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 8 / 29

15 Proximal Gradient Method min f (x) x X Projected (sub)gradient x P X (x α f (x)) min f (x) + h(x) Proximal gradient x prox αh (x α f (x)) prox αh denotes proximity operator for h Why? If we can compute prox h (x) easily, prox-grad converges as fast gradient methods for smooth problems! Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 8 / 29

16 Proximity operator Projection P X (y) := argmin x R n 1 2 x y X (x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 9 / 29

17 Proximity operator Projection P X (y) := argmin x R n 1 2 x y X (x) Proximity: Replace 1 X by a closed convex function prox r (y) := argmin x R n 1 2 x y r(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 9 / 29

18 Proximity operator l 1 -norm ball of radius ρ(λ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 10 / 29

19 Proximity operators Exercise: Let r(x) = x 1. Solve prox λr (y). 1 min x R n 2 x y λ x 1. Hint 1: The above problem decomposes into n independent subproblems of the form min x R 1 2 (x y)2 + λ x. Hint 2: Consider the two cases: either x = 0 or x 0 Exercise: Moreau decomposition y = prox h y + prox h y (notice analogy to V = S + S in linear algebra) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 11 / 29

20 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

21 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

22 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) 0 α f (x ) + α h(x ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

23 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) 0 α f (x ) + α h(x ) x α f (x ) + (I + α h)(x ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

24 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) 0 α f (x ) + α h(x ) x α f (x ) + (I + α h)(x ) x α f (x ) (I + α h)(x ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

25 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) 0 α f (x ) + α h(x ) x α f (x ) + (I + α h)(x ) x α f (x ) (I + α h)(x ) x = (I + α h) 1 (x α f (x )) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

26 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) 0 α f (x ) + α h(x ) x α f (x ) + (I + α h)(x ) x α f (x ) (I + α h)(x ) x = (I + α h) 1 (x α f (x )) x = prox αh (x α f (x )) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

27 How to cook-up prox-grad? Lemma x = prox αh (x α f (x )), α > 0 0 f (x ) + h(x ) 0 α f (x ) + α h(x ) x α f (x ) + (I + α h)(x ) x α f (x ) (I + α h)(x ) x = (I + α h) 1 (x α f (x )) x = prox αh (x α f (x )) Above fixed-point eqn suggests iteration x k+1 = prox αk h (x k α k f (x k )) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 12 / 29

28 Convergence Suvrit Sra Optimization for Machine Learning 13 / 29

29 Proximal-gradient works, why? x k+1 = prox αk h (x k α k f (x k )) x k+1 = x k α k G αk (x k ). Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 14 / 29

30 Proximal-gradient works, why? x k+1 = prox αk h (x k α k f (x k )) x k+1 = x k α k G αk (x k ). Gradient mapping: the gradient-like object G α (x) = 1 α (x P αh(x α f (x))) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 14 / 29

31 Proximal-gradient works, why? x k+1 = prox αk h (x k α k f (x k )) x k+1 = x k α k G αk (x k ). Gradient mapping: the gradient-like object G α (x) = 1 α (x P αh(x α f (x))) Our lemma shows: G α (x) = 0 if and only if x is optimal So G α analogous to f If x locally optimal, then G α (x) = 0 (nonconvex f ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 14 / 29

32 Convergence analysis Assumption: Lipschitz continuous gradient; denoted f C 1 L f (x) f (y) 2 L x y 2 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 15 / 29

33 Convergence analysis Assumption: Lipschitz continuous gradient; denoted f C 1 L f (x) f (y) 2 L x y 2 Gradient vectors of closeby points are close to each other Objective function has bounded curvature Speed at which gradient varies is bounded Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 15 / 29

34 Convergence analysis Assumption: Lipschitz continuous gradient; denoted f C 1 L f (x) f (y) 2 L x y 2 Gradient vectors of closeby points are close to each other Objective function has bounded curvature Speed at which gradient varies is bounded Lemma (Descent). Let f C 1 L. Then, f (y) f (x) + f (x), y x + L 2 y x 2 2 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 15 / 29

35 Convergence analysis Assumption: Lipschitz continuous gradient; denoted f C 1 L f (x) f (y) 2 L x y 2 Gradient vectors of closeby points are close to each other Objective function has bounded curvature Speed at which gradient varies is bounded Lemma (Descent). Let f C 1 L. Then, f (y) f (x) + f (x), y x + L 2 y x 2 2 For convex f, compare with f (y) f (x) + f (x), y x. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 15 / 29

36 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

37 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Add and subtract f (x), y x on rhs we have f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

38 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Add and subtract f (x), y x on rhs we have f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

39 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Add and subtract f (x), y x on rhs we have f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt 1 0 f (z t) f (x), y x dt Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

40 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Add and subtract f (x), y x on rhs we have f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt 1 0 f (z t) f (x), y x dt 1 0 f (z t) f (x) 2 y x 2 dt Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

41 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Add and subtract f (x), y x on rhs we have f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt 1 0 f (z t) f (x), y x dt 1 0 f (z t) f (x) 2 y x 2 dt L 1 0 t x y 2 2 dt Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

42 Descent lemma Proof. Since f C 1 L, by Taylor s theorem, for the vector z t = x + t(y x) we have f (y) = f (x) f (z t), y x dt. Add and subtract f (x), y x on rhs we have f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt f (y) f (x) f (x), y x = 1 0 f (z t) f (x), y x dt Bounds f (y) around x with quadratic functions 1 0 f (z t) f (x), y x dt 1 0 f (z t) f (x) 2 y x 2 dt L 1 0 t x y 2 2 dt = L 2 x y 2 2. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 16 / 29

43 Descent lemma corollary Let y = x αg α (x), then f (y) f (x) + f (x), y x + L 2 y x 2 2 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 17 / 29

44 Descent lemma corollary Let y = x αg α (x), then f (y) f (x) + f (x), y x + L 2 y x 2 2 f (y) f (x) α f (x), G α (x) + α2 L 2 G α(x) 2 2. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 17 / 29

45 Descent lemma corollary Let y = x αg α (x), then f (y) f (x) + f (x), y x + L 2 y x 2 2 f (y) f (x) α f (x), G α (x) + α2 L 2 G α(x) 2 2. Corollary. So if 0 α 1/L, we have f (y) f (x) α f (x), G α (x) + α 2 G α(x) 2 2. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 17 / 29

46 Descent lemma corollary Let y = x αg α (x), then f (y) f (x) + f (x), y x + L 2 y x 2 2 f (y) f (x) α f (x), G α (x) + α2 L 2 G α(x) 2 2. Corollary. So if 0 α 1/L, we have f (y) f (x) α f (x), G α (x) + α 2 G α(x) 2 2. Lemma Let y = x αg α (x). Then, for any z we have f (y) + h(y) f (z) + h(z) + G α (x), x z α 2 G α(x) 2 2. Exercise: Prove! (hint: f, h are convex, G α (x) f (x) h(y)) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 17 / 29

47 Convergence analysis We ve actually shown x = x αg α (x) is a descent method. Write φ = f + h; plug in z = x to obtain φ(x ) φ(x) α 2 G α(x) 2 2. Exercise: Why this inequality suffices to show convergence. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 18 / 29

48 Convergence analysis We ve actually shown x = x αg α (x) is a descent method. Write φ = f + h; plug in z = x to obtain φ(x ) φ(x) α 2 G α(x) 2 2. Exercise: Why this inequality suffices to show convergence. Use z = x in corollary to obtain progress in terms of iterates: φ(x ) φ G α (x), x x α 2 G α(x) 2 2 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 18 / 29

49 Convergence analysis We ve actually shown x = x αg α (x) is a descent method. Write φ = f + h; plug in z = x to obtain φ(x ) φ(x) α 2 G α(x) 2 2. Exercise: Why this inequality suffices to show convergence. Use z = x in corollary to obtain progress in terms of iterates: φ(x ) φ G α (x), x x α 2 G α(x) 2 2 = 1 [ 2 αgα (x), x x αg α (x) 2 2α 2] = 1 [ x x 2 2 2α x x αg α (x) 2 2] Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 18 / 29

50 Convergence analysis We ve actually shown x = x αg α (x) is a descent method. Write φ = f + h; plug in z = x to obtain φ(x ) φ(x) α 2 G α(x) 2 2. Exercise: Why this inequality suffices to show convergence. Use z = x in corollary to obtain progress in terms of iterates: φ(x ) φ G α (x), x x α 2 G α(x) 2 2 = 1 [ 2 αgα (x), x x αg α (x) 2 2α 2] = 1 2α = 1 2α [ x x 2 2 x x αg α (x) 2 2] [ x x 2 2 x x 2 ] 2. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 18 / 29

51 Convergence rate Set x x k, x x k+1, and α = 1/L. Then add Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 19 / 29

52 Convergence rate Set x x k, x x k+1, and α = 1/L. Then add k+1 i=1 (φ(x i) φ ) L 2 k+1 i=1 [ xi x 2 2 x i+1 x 2 2] Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 19 / 29

53 Convergence rate Set x x k, x x k+1, and α = 1/L. Then add k+1 k+1 [ xi x 2 2 x i+1 x 2 2] (φ(x i) φ ) L i=1 2 i=1 [ x1 x 2 2 x k+1 x 2 2] = L 2 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 19 / 29

54 Convergence rate Set x x k, x x k+1, and α = 1/L. Then add k+1 k+1 [ xi x 2 2 x i+1 x 2 2] (φ(x i) φ ) L i=1 2 i=1 [ x1 x 2 2 x k+1 x 2 2] = L 2 L 2 x 1 x 2 2. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 19 / 29

55 Convergence rate Set x x k, x x k+1, and α = 1/L. Then add k+1 k+1 [ xi x 2 2 x i+1 x 2 2] (φ(x i) φ ) L i=1 2 i=1 [ x1 x 2 2 x k+1 x 2 2] = L 2 L 2 x 1 x 2 2. Since φ(x k ) is a decreasing sequence, it follows that φ(x k+1 ) φ 1 k+1 k + 1 i=1 (φ(x i ) φ ) L 2(k + 1) x 1 x 2 2. This is the well-known O(1/k) rate. But for C 1 L convex functions, optimal rate is O(1/k2 )! Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 19 / 29

56 Accelerated Proximal Gradient min φ(x) = f (x) + h(x) Let x 0 = y 0 dom h. For k 1: x k = prox αk h (yk 1 α k f (y k 1 )) y k = x k + k 1 k + 2 (xk x k 1 ). Framework due to: Nesterov (1983, 2004); also Beck, Teboulle (2009). Simplified analysis: Tseng (2008). Uses extra memory for interpolation Same computational cost as ordinary prox-grad Convergence rate theoretically optimal φ(x k ) φ 2L (k + 1) 2 x0 x 2 2. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 20 / 29

57 Proximal splitting methods l(x) + f (x) + h(x) Direct use of prox-grad not easy Requires computation of: prox λ(f +h) (i.e., (I + λ( f + h)) 1 ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 21 / 29

58 Proximal splitting methods l(x) + f (x) + h(x) Direct use of prox-grad not easy Requires computation of: prox λ(f +h) (i.e., (I + λ( f + h)) 1 ) Example: 1 min 2 x y λ x 2 + µ n 1 }{{} x i+1 x i. i=1 }{{} f (x) h(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 21 / 29

59 Proximal splitting methods l(x) + f (x) + h(x) Direct use of prox-grad not easy Requires computation of: prox λ(f +h) (i.e., (I + λ( f + h)) 1 ) Example: 1 min 2 x y λ x 2 + µ n 1 }{{} x i+1 x i. i=1 }{{} f (x) h(x) But good feature: prox f and prox h separately easier Can we exploit that? Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 21 / 29

60 Proximal splitting operator notation If (I + f + h) 1 hard, but (I + f ) 1 and (I + h) 1 easy Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 22 / 29

61 Proximal splitting operator notation If (I + f + h) 1 hard, but (I + f ) 1 and (I + h) 1 easy Let us derive a fixed-point equation that splits the operators Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 22 / 29

62 Proximal splitting operator notation If (I + f + h) 1 hard, but (I + f ) 1 and (I + h) 1 easy Let us derive a fixed-point equation that splits the operators Assume we are solving min f (x) + h(x), where both f and h are convex but potentially nondifferentiable. Notice: We implicitly assumed: (f + h) = f + h. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 22 / 29

63 Proximal splitting 0 f (x) + h(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

64 Proximal splitting 0 f (x) + h(x) 2x (I + f )(x) + (I + h)(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

65 Proximal splitting 0 f (x) + h(x) 2x (I + f )(x) + (I + h)(x) Key idea of splitting: new variable! z (I + h)(x) = x = prox h (z) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

66 Proximal splitting 0 f (x) + h(x) 2x (I + f )(x) + (I + h)(x) Key idea of splitting: new variable! z (I + h)(x) = x = prox h (z) 2x z (I + f )(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

67 Proximal splitting 0 f (x) + h(x) 2x (I + f )(x) + (I + h)(x) Key idea of splitting: new variable! z (I + h)(x) = x = prox h (z) 2x z (I + f )(x) = x (I + f ) 1 (2x z) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

68 Proximal splitting 0 f (x) + h(x) 2x (I + f )(x) + (I + h)(x) Key idea of splitting: new variable! z (I + h)(x) = x = prox h (z) 2x z (I + f )(x) = x (I + f ) 1 (2x z) Not a fixed-point equation yet Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

69 Proximal splitting 0 f (x) + h(x) 2x (I + f )(x) + (I + h)(x) Key idea of splitting: new variable! z (I + h)(x) = x = prox h (z) 2x z (I + f )(x) = x (I + f ) 1 (2x z) Not a fixed-point equation yet We need one more idea Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 23 / 29

70 Douglas-Rachford splitting Reflection operator R h (z) := 2 prox h (z) z Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

71 Douglas-Rachford splitting Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method z (I + h)(x), x = prox h (z) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

72 Douglas-Rachford splitting Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method z (I + h)(x), x = prox h (z) = R h (z) = 2x z Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

73 Douglas-Rachford splitting z (I + h)(x), Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method x = prox h (z) = R h (z) = 2x z 0 f (x) + g(x) 2x (I + f )(x) + (I + g)(x) 2x z (I + f )(x) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

74 Douglas-Rachford splitting z (I + h)(x), Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method x = prox h (z) = R h (z) = 2x z 0 f (x) + g(x) 2x (I + f )(x) + (I + g)(x) 2x z (I + f )(x) x = prox f (R h (z)) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

75 Douglas-Rachford splitting z (I + h)(x), Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method x = prox h (z) = R h (z) = 2x z 0 f (x) + g(x) 2x (I + f )(x) + (I + g)(x) 2x z (I + f )(x) x = prox f (R h (z)) but R h (z) = 2x z = z = 2x R h (z) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

76 Douglas-Rachford splitting z (I + h)(x), Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method x = prox h (z) = R h (z) = 2x z 0 f (x) + g(x) 2x (I + f )(x) + (I + g)(x) 2x z (I + f )(x) x = prox f (R h (z)) but R h (z) = 2x z = z = 2x R h (z) z = 2 prox f (R h (z)) R h (z) = Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

77 Douglas-Rachford splitting z (I + h)(x), Reflection operator R h (z) := 2 prox h (z) z Douglas-Rachford method x = prox h (z) = R h (z) = 2x z 0 f (x) + g(x) 2x (I + f )(x) + (I + g)(x) 2x z (I + f )(x) x = prox f (R h (z)) but R h (z) = 2x z = z = 2x R h (z) z = 2 prox f (R h (z)) R h (z) = R f (R h (z)) Finally, z is on both sides of the eqn Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 24 / 29

78 Douglas-Rachford method 0 f (x) + h(x) DR method: given z 0, iterate for k 0 x k = prox h (z k ) { x = prox h (z) z = R f (R h (z)) v k = prox f (2x k z k ) z k+1 = z k + γ k (v k x k ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 25 / 29

79 Douglas-Rachford method 0 f (x) + h(x) DR method: given z 0, iterate for k 0 x k = prox h (z k ) { x = prox h (z) z = R f (R h (z)) v k = prox f (2x k z k ) z k+1 = z k + γ k (v k x k ) Theorem. If f + h admits minimizers, and (γ k ) satisfy γ k [0, 2], γ k(2 γ k ) =, k then the DR-iterates v k and x k converge to a minimizer. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 25 / 29

80 Douglas-Rachford method For γ k = 1, we have z k+1 = z k + v k x k z k+1 = z k + prox f (2 prox h (z k ) z k ) prox h (z k ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 26 / 29

81 Douglas-Rachford method For γ k = 1, we have z k+1 = z k + v k x k z k+1 = z k + prox f (2 prox h (z k ) z k ) prox h (z k ) Dropping superscripts, writing P prox, we have z Tz T = I + P f (2P h I) P h Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 26 / 29

82 Douglas-Rachford method For γ k = 1, we have z k+1 = z k + v k x k z k+1 = z k + prox f (2 prox h (z k ) z k ) prox h (z k ) Dropping superscripts, writing P prox, we have z Tz T = I + P f (2P h I) P h Lemma DR can be written as: z 1 2 (R f R h + I)z, where R f denotes the reflection operator 2P f I (similarly R h ). Exercise: Prove this claim. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 26 / 29

83 Proximal methods cornucopia Douglas Rachford splitting ADMM (special case of DR on dual) Proximal-Dykstra Proximal methods for f 1 + f f n Peaceman-Rachford Proximal quasi-newton, Newton Many other variation... Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 27 / 29

84 Best approximation problem min δ A (x) + δ B (x) where A B =. Can we use DR? Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 28 / 29

85 Best approximation problem min δ A (x) + δ B (x) where A B =. Can we use DR? Using a clever analysis of Bauschke & Combettes (2004), DR can still be applied! However, it generates diverging iterates which can be projected back to obtain a solution to min a b 2 a A, b B. See: Jegelka, Bach, Sra (NIPS 2013) for an example. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 28 / 29

86 ADMM Let us see separable objective with constraints Suvrit Sra Optimization for Machine Learning 29 / 29

87 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29

88 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Objective function separated into x and z variables The constraint prevents a trivial decoupling Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29

89 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Objective function separated into x and z variables The constraint prevents a trivial decoupling Introduce augmented lagrangian (AL) L ρ (x, z, y) := f (x) + g(z) + y T (Ax + Bz c) + ρ 2 Ax + Bz c 2 2 Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29

90 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Objective function separated into x and z variables The constraint prevents a trivial decoupling Introduce augmented lagrangian (AL) L ρ (x, z, y) := f (x) + g(z) + y T (Ax + Bz c) + ρ 2 Ax + Bz c 2 2 Now, a Gauss-Seidel style update to the AL Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29

91 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Objective function separated into x and z variables The constraint prevents a trivial decoupling Introduce augmented lagrangian (AL) L ρ (x, z, y) := f (x) + g(z) + y T (Ax + Bz c) + ρ 2 Ax + Bz c 2 2 Now, a Gauss-Seidel style update to the AL x k+1 = argmin x L ρ (x, z k, y k ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29

92 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Objective function separated into x and z variables The constraint prevents a trivial decoupling Introduce augmented lagrangian (AL) L ρ (x, z, y) := f (x) + g(z) + y T (Ax + Bz c) + ρ 2 Ax + Bz c 2 2 Now, a Gauss-Seidel style update to the AL x k+1 = argmin x L ρ (x, z k, y k ) z k+1 = argmin z L ρ (x k+1, z, y k ) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29

93 ADMM Let us see separable objective with constraints min f (x) + g(z) s.t. Ax + Bz = c. Objective function separated into x and z variables The constraint prevents a trivial decoupling Introduce augmented lagrangian (AL) L ρ (x, z, y) := f (x) + g(z) + y T (Ax + Bz c) + ρ 2 Ax + Bz c 2 2 Now, a Gauss-Seidel style update to the AL x k+1 = argmin x L ρ (x, z k, y k ) z k+1 = argmin z L ρ (x k+1, z, y k ) y k+1 = y k + ρ(ax k+1 + Bz k+1 c) Suvrit Sra (suvrit@mit.edu) Optimization for Machine Learning 29 / 29