1. Peter Richtárik Parallel coordinate NIPS 2013, Lake Tahoe descent methods

2. I.Introduction II.Regularized Convex Optimization III.Parallel CD (PCDM) IV.Accelerated Parallel CD (APCDM) V.ESO? Good ESO? VI.Experiments in 10^9 dim VII.Distributed CD (Hydra) VIII.Nonuniform Parallel CD (‘NSync) IX.Mini-batch SDCA for SVMs OUTLINE POSTER

3. Part I. Introduction

4. What is Randomized Coordinate Descent ?

5. Find the minimizer of 2D Optimization Contours of a function Goal:

6. Randomized Coordinate Descent in 2D N S E W

7. 1 N S E W

8. 1 N S E W 2

9. 1 2 3 N S E W

10. 1 2 3 4 N S E W

11. 1 2 3 4 N S E W 5

12. 1 2 3 4 5 6 N S E W

13. 1 2 3 4 5 N S E W 6 7 S O L V E D !

14. Convergence of Randomized Coordinate Descent Strongly convex F Smooth or ‘simple’ nonsmooth F ‘difficult’ nonsmooth F Focus on n (big data = big n)

15. Parallelization Dream Depends on to what extent we can add up individual updates, which depends on the properties of F and the way coordinates are chosen at each iteration SerialParallel What do we actually get? WANT

16. How (not) to Parallelize Coordinate Descent

17. “Naive” parallelization Do the same thing as before, but for MORE or ALL coordinates & ADD UP the updates

18. Failure of naive parallelization 1a 1b 0

19. Failure of naive parallelization 1 1a 1b 0

20. Failure of naive parallelization 1 2a 2b

21. Failure of naive parallelization 1 2a 2b 2

22. Failure of naive parallelization 2 O O P S !

23. 1 1a 1b 0 Idea: averaging updates may help S O L V E D !

24. Averaging can be too conservative 1a 1b 0 1 2a 2b 2 a n d s o o n...

25. Averaging may be too conservative 2 WANT BAD!!! But we wanted:

26. What to do? Averaging: Summation: Update to coordinate i i-th unit coordinate vector Figure out when one can safely use:

27. Part II. Regularized Convex Optimization

28. Problem Convex (smooth or nonsmooth) Convex (smooth or nonsmooth) - separable - allow Loss Regularizer

29. Regularizer: examples No regularizerWeighted L1 norm Weighted L2 norm Box constraints e.g., SVM dual e.g., LASSO

30. Loss: examples Quadratic loss L-infinity L1 regression Exponential loss Logistic loss Square hinge loss BKBG’11 RT’11b TBRS’13 RT ’13a FR’13a

31. 3 models for f with small 1 2 3 Smooth partially separable f [RT’11b ] Nonsmooth max-type f [FR’13] f with ‘bounded Hessian’ [BKBG’11, RT’13a ]

32. Part III. Parallel Coordinate Descent P.R. and Martin Takáč Parallel Coordinate Descent Methods for Big Data Optimization arXiv:1212.0873, 2012 [IMA Leslie Fox Prize 2013] P.R. and Martin Takáč Parallel Coordinate Descent Methods for Big Data Optimization arXiv:1212.0873, 2012 [IMA Leslie Fox Prize 2013]

33. Randomized Parallel Coordinate Descent Method Random set of coordinates (sampling) Current iterateNew iteratei-th unit coordinate vector Update to i-th coordinate

34. ESO: Expected Separable Overapproximation Definition [RT’11b] 1. Separable in h 2. Can minimize in parallel 3. Can compute updates for only Shorthand: Minimize in h

35. Convergence rate: convex f average # updated coordinates per iteration # coordinatesstepsize parameter error tolerance # iterations implies Theorem [RT’11b]

36. Convergence rate: strongly convex f implies Strong convexity constant of the regularizer Strong convexity constant of the loss f Theorem [RT’11b]

37. Part IV. Accelerated Parallel CD Olivier Fercoq and P.R. Accelerated, Parallel and Proximal Coordinate Descent Manuscript, 2013 Olivier Fercoq and P.R. Accelerated, Parallel and Proximal Coordinate Descent Manuscript, 2013

38. 3 x YES

39. The Algorithm Parallel CD step ESO parameters

40. Complexity average # updated coordinates per iteration # coordinates error tolerance # iterations implies Theorem [FR’13b]

41. Part V. ESO? Good ESO? (e.g., partially separable f & doubly uniform S)

42. Serial uniform sampling Probability law:

43. -nice sampling Probability law: Good for shared memory systems

44. Doubly uniform sampling Probability law: Can model unreliable processors / machines

45. ESO for partially separable functions and doubly uniform samplings Theorem [RT’11b] 1 Smooth partially separable f [RT’11b ]

46. Theoretical speedup Much of Big Data is here! degree of partial separability # coordinates # coordinate updates / iter WEAK OR NO SPEEDUP: Non-separable (dense) problems LINEAR OR GOOD SPEEDUP: Nearly separable (sparse) problems

48. n = 1000 (# coordinates) Theory

49. Practice n = 1000 (# coordinates)

50. Part VI. Experiment with a 1 billion-by-2 billion LASSO problem

51. Optimization with Big Data * in a billion dimensional space on a foggy day Extreme* Mountain Climbing =

52. Coordinate Updates

53. Iterations

54. Wall Time

55. L2-reg logistic loss on rcv1.binary

56. Part VII. Distributed CD P.R. and Martin Takáč Distributed Coordinate Descent Method for Learning with Big Data arXiv:1310.2059, 2013 P.R. and Martin Takáč Distributed Coordinate Descent Method for Learning with Big Data arXiv:1310.2059, 2013

57. Distributed -nice sampling Probability law: Machine 2Machine 1Machine 3 Good for a distributed version of coordinate descent

58. ESO: Distributed setting Theorem [RT’13b] 3 f with ‘bounded Hessian’ [BKBG’11, RT’13a ] spectral norm of the data

59. Bad partitioning at most doubles # of iterations spectral norm of the partitioning Theorem [RT’13b] # nodes # iterations = implies # updates/node

61. LASSO with a 3TB data matrix 128 Cray XE6 nodes with 4 MPI processes (c = 512) Each node: 2 x 16-cores with 32GB RAM = # coordinates

62. References

63. Shai Shalev-Shwartz and Ambuj Tewari, Stochastic methods for L1-regularized loss minimization. JMLR 2011. Yurii Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM Journal on Optimization, 22(2):341-362, 2012. [RT’11b] P.R. and Martin Takáč, Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Mathematical Prog., 2012. Rachael Tappenden, P.R. and Jacek Gondzio, Inexact coordinate descent: complexity and preconditioning, arXiv: 1304.5530, 2013. Ion Necoara, Yurii Nesterov, and Francois Glineur. Efficiency of randomized coordinate descent methods on optimization problems with linearly coupled constraints. Technical report, Politehnica University of Bucharest, 2012. Zhaosong Lu and Lin Xiao. On the complexity analysis of randomized block- coordinate descent methods. Technical report, Microsoft Research, 2013. References: serial coordinate descent

64. [BKBG’11] Joseph Bradley, Aapo Kyrola, Danny Bickson and Carlos Guestrin, Parallel Coordinate Descent for L1-Regularized Loss Minimization. ICML 2011 [RT’12] P.R. and Martin Takáč, Parallel coordinate descen methods for big data optimization. arXiv:1212.0873, 2012 Martin Takáč, Avleen Bijral, P.R., and Nathan Srebro. Mini-batch primal and dual methods for SVMs. ICML 2013 [FR’13a] Olivier Fercoq and P.R., Smooth minimization of nonsmooth functions with parallel coordinate descent methods. arXiv:1309.5885, 2013 [RT’13a] P.R. and Martin Takáč, Distributed coordinate descent method for big data learning. arXiv:1310.2059, 2013 [RT’13b] P.R. and Martin Takáč, On optimal probabilities in stochastic coordinate descent methods. arXiv:1310.3438, 2013 References: parallel coordinate descent Good entry point to the topic (4p paper)

65. P.R. and Martin Takáč, Efficient serial and parallel coordinate descent methods for huge-scale truss topology design. Operations Research Proceedings 2012. [FR’13b] Olivier Fercoq and P.R., Accelerated, Parallel and Proximal Coordinate Descent. Manuscript, 2013. Rachael Tappenden, P.R. and Burak Buke, Separable approximations and decomposition methods for the augmented Lagrangian. arXiv:1308.6774, 2013. Indranil Palit and Chandan K. Reddy. Scalable and parallel boosting with MapReduce. IEEE Transactions on Knowledge and Data Engineering, 24(10):1904-1916, 2012. Shai Shalev-Shwartz and Tong Zhang, Accelerated mini-batch stochastic dual coordinate ascent. NIPS 2013. References: parallel coordinate descent