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Peter Richtárik Parallel coordinate Simons Institute for the Theory of Computing, Berkeley Parallel and Distributed Algorithms for Inference and Optimization,

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Presentation on theme: "Peter Richtárik Parallel coordinate Simons Institute for the Theory of Computing, Berkeley Parallel and Distributed Algorithms for Inference and Optimization,"— Presentation transcript:

1 Peter Richtárik Parallel coordinate Simons Institute for the Theory of Computing, Berkeley Parallel and Distributed Algorithms for Inference and Optimization, October 23, 2013 descent methods

2

3 Randomized Coordinate Descent in 2D

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

5 Randomized Coordinate Descent in 2D N S E W

6 1 N S E W

7 1 N S E W 2

8 1 2 3 N S E W

9 N S E W

10 N S E W 5

11 N S E W

12 N S E W 6 7 S O L V E D !

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

14 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

15 How (not) to Parallelize Coordinate Descent

16 Naive parallelization Do the same thing as before, but for MORE or ALL coordinates & ADD UP the updates

17 Failure of naive parallelization 1a 1b 0

18 Failure of naive parallelization 1 1a 1b 0

19 Failure of naive parallelization 1 2a 2b

20 Failure of naive parallelization 1 2a 2b 2

21 Failure of naive parallelization 2 O O P S !

22 1 1a 1b 0 Idea: averaging updates may help S O L V E D !

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

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

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

26 Optimization Problems

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

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

29 Loss: examples Quadratic loss L-infinity L1 regression Exponential loss Logistic loss Square hinge loss BKBG11 RT11b TBRS13 RT 13a FR13

30 3 models for f with small Smooth partially separable f [RT11b ] Nonsmooth max-type f [FR13] f with bounded Hessian [BKBG11, RT13a ]

31 General Theory

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

33 ESO: Expected Separable Overapproximation Definition [RT11b] 1. Separable in h 2. Can minimize in parallel 3. Can compute updates for only Shorthand: Minimize in h

34 Convergence rate: convex f average # updated coordinates per iteration # coordinatesstepsize parameter error tolerance # iterations implies Theorem [RT11b]

35 Convergence rate: strongly convex f implies Strong convexity constant of the regularizer Strong convexity constant of the loss f Theorem [RT11b]

36 Partial Separability and Doubly Uniform Samplings

37 Serial uniform sampling Probability law:

38 -nice sampling Probability law: Good for shared memory systems

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

40 ESO for partially separable functions and doubly uniform samplings Theorem [RT11b] 1 Smooth partially separable f [RT11b ]

41 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

42

43 n = 1000 (# coordinates) Theory

44 Practice n = 1000 (# coordinates)

45 Experiment with a 1 billion-by-2 billion LASSO problem

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

47 Coordinate Updates

48 Iterations

49 Wall Time

50 Distributed-Memory Coordinate Descent

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

52 ESO: Distributed setting Theorem [RT13b] 3 f with bounded Hessian [BKBG11, RT13a ] spectral norm of the data

53 Bad partitioning at most doubles # of iterations spectral norm of the partitioning Theorem [RT13b] # nodes # iterations = implies # updates/node

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55 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

56 Conclusions Coordinate descent methods scale very well to big data problems of special structure – Partial separability (sparsity) – Small spectral norm of the data – Nesterov separability,... Care is needed when combining updates (add them up? average?)

57 Shai Shalev-Shwartz and Ambuj Tewari, Stochastic methods for L1-regularized loss minimization. JMLR Yurii Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM Journal on Optimization, 22(2): , [RT11b] P.R. and Martin Takáč, Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Mathematical Prog., Rachael Tappenden, P.R. and Jacek Gondzio, Inexact coordinate descent: complexity and preconditioning, arXiv: , 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, Zhaosong Lu and Lin Xiao. On the complexity analysis of randomized block- coordinate descent methods. Technical report, Microsoft Research, References: serial coordinate descent

58 [BKBG11] Joseph Bradley, Aapo Kyrola, Danny Bickson and Carlos Guestrin, Parallel Coordinate Descent for L1-Regularized Loss Minimization. ICML 2011 [RT12] P.R. and Martin Takáč, Parallel coordinate descen methods for big data optimization. arXiv: , 2012 Martin Takáč, Avleen Bijral, P.R., and Nathan Srebro. Mini-batch primal and dual methods for SVMs. ICML 2013 [FR12] Olivier Fercoq and P.R., Smooth minimization of nonsmooth functions with parallel coordinate descent methods. arXiv: , 2013 [RT13a] P.R. and Martin Takáč, Distributed coordinate descent method for big data learning. arXiv: , 2013 [RT13b] P.R. and Martin Takáč, On optimal probabilities in stochastic coordinate descent methods. arXiv: , 2013 References: parallel coordinate descent Good entry point to the topic (4p paper)

59 P.R. and Martin Takáč, Efficient serial and parallel coordinate descent methods for huge-scale truss topology design. Operations Research Proceedings Rachael Tappenden, P.R. and Burak Buke, Separable approximations and decomposition methods for the augmented Lagrangian. arXiv: , Indranil Palit and Chandan K. Reddy. Scalable and parallel boosting with MapReduce. IEEE Transactions on Knowledge and Data Engineering, 24(10): , Shai Shalev-Shwartz and Tong Zhang, Accelerated mini-batch stochastic dual coordinate ascent. NIPS References: parallel coordinate descent TALK TOMORROW


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