Coordinate descent

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Coordinate descent is a non-derivative optimization algorithm. To find a local minimum of a function, one does line search along one coordinate direction at the current point in each iteration. One uses different coordinate directions cyclically throughout the procedure.


Coordinate descent is based on the idea that the minimization of a multivariable function F(\mathbf{x}) can be achieved by minimizing it along one direction at a time, i.e., solving univariate (or at least much simpler) optimization problems in a loop.[1] In the simplest case of cyclic coordinate descent, one cyclically iterates through the directions, one at a time, minimizing the objective function with respect to each coordinate direction at a time. That is, in each iteration, for each variable k of the problem in turn, the algorithm solves the optimization problem

x^{k+1}_i = \underset{y\in\mathbb R}{\operatorname{arg\,min}}\; f(x^{k+1}_1, \dots, x^{k+1}_{i-1}, y, x^k_{i+1}, \dots, x^k_n).

Thus, one begins with an initial guess \mathbf{x}^0 for a local minimum of F, and get a sequence \mathbf{x}^0, \mathbf{x}^1, \mathbf{x}^2, \dots iteratively.

By doing line search in each iteration, one automatically has

F(\mathbf{x}^0)\ge F(\mathbf{x}^1)\ge F(\mathbf{x}^2)\ge \dots.

It can be shown that this sequence has similar convergence properties as steepest descent. No improvement after one cycle of line search along coordinate directions implies a stationary point is reached.

This process is illustrated below.

Coordinate descent.svg

Differentiable case[edit]

In the case of a continuously differentiable function F, a coordinate descent algorithm can be sketched as:[1]

  • Choose an initial parameter vector x.
  • Until convergence is reached, or for some fixed number of iterations:
    • Choose an index i from 1 to n.
    • Choose a step size α.
    • Update xi to xiαF/xi(x).

The step size can be chosen in various ways, e.g., by solving for the exact minimizer of f(xi) = F(x) (i.e., F with all variables but xi fixed), or by traditional line search criteria.[1]


Coordinate descent has problems with non-smooth functions. The following picture shows that coordinate descent iteration may get stuck at a non-stationary point if the level curves of a function are not smooth. Suppose that the algorithm is at the point (-2, -2); then there are two axis-aligned directions it can consider for taking a step, indicated by the red arrows. However, every step along these two directions will increase the objective function's value (assuming a minimization problem), so the algorithm will not take any step, even though both steps together would bring the algorithm closer to the optimum.

Nonsmooth coordinate descent.svg


Coordinate descent algorithms are popular with practitioners owing to their simplicity, but the same property has led optimization researchers to largely ignore them in favor of more interesting (complicated) methods.[1] This situation has changed with the advent of large-scale problems in machine learning, where coordinate descent has been shown competitive to other methods when applied to such problems as training linear support vector machines[2] (see LIBLINEAR) and non-negative matrix factorization.[3] They are attractive for problems where computing gradients is infeasible, perhaps because the data required to do so are distributed across computer networks.[4]

See also[edit]


  1. ^ a b c d Wright, Stephen J. (2015). "Coordinate descent algorithms". Mathematical Programming 151 (1): 3–34. arXiv:1502.04759. doi:10.1007/s10107-015-0892-3. 
  2. ^ Hsieh, C. J.; Chang, K. W.; Lin, C. J.; Keerthi, S. S.; Sundararajan, S. (2008). "A dual coordinate descent method for large-scale linear SVM". Proceedings of the 25th international conference on Machine learning - ICML '08 (PDF). p. 408. doi:10.1145/1390156.1390208. ISBN 9781605582054. 
  3. ^ Hsieh, C. J.; Dhillon, I. S. (2011). Fast coordinate descent methods with variable selection for non-negative matrix factorization (PDF). Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11. p. 1064. doi:10.1145/2020408.2020577. ISBN 9781450308137. 
  4. ^ Nesterov, Yurii (2012). "Efficiency of coordinate descent methods on huge-scale optimization problems" (PDF). SIAM J. Optimization 22 (2): 341–362. doi:10.1137/100802001. 
  • Bezdek, J. C.; Hathaway, R. J.; Howard, R. E.; Wilson, C. A.; Windham, M. P. (1987), "Local convergence analysis of a grouped variable version of coordinate descent", Journal of Optimization theory and applications (Kluwer Academic/Plenum Publishers) 54 (3), pp. 471–477, doi:10.1007/BF00940196 
  • Bertsekas, Dimitri P. (1999). Nonlinear Programming, Second Edition Athena Scientific, Belmont, Massachusetts. ISBN 1-886529-00-0.
  • Canutescu, AA; Dunbrack, RL (2003), "Cyclic coordinate descent: A robotics algorithm for protein loop closure.", Protein science 12 (5), pp. 963–72, doi:10.1110/ps.0242703, PMID 12717019 .
  • Luo, Zhiquan; Tseng, P. (1992), "On the convergence of the coordinate descent method for convex differentiable minimization", Journal of Optimization theory and applications (Kluwer Academic/Plenum Publishers) 72 (1), pp. 7–35, doi:10.1007/BF00939948 .
  • Wu, TongTong; Lange, Kenneth (2008), "Coordinate descent algorithms for Lasso penalized regression", The Annals of Applied Statistics (Institute of Mathematical Statistics) 2 (1), pp. 224–244, doi:10.1214/07-AOAS147 .
  • Richtarik, Peter; Takac, Martin (April 2011), "Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function", Mathematical Programming (Springer), doi:10.1007/s10107-012-0614-z .
  • Richtarik, Peter; Takac, Martin (December 2012), "Parallel coordinate descent methods for big data optimization", arXiv:1212.0873 .