Numerical Optimization Techniques
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1 Numerical Optimization Techniques Léon Bottou NEC Labs America COS 424 3/2/2010
2 Today s Agenda Goals Representation Capacity Control Operational Considerations Computational Considerations Classification, clustering, regression, other. Parametric vs. kernels vs. nonparametric Probabilistic vs. nonprobabilistic Linear vs. nonlinear Deep vs. shallow Explicit: architecture, feature selection Explicit: regularization, priors Implicit: approximate optimization Implicit: bayesian averaging, ensembles Loss functions Budget constraints Online vs. offline Exact algorithms for small datasets. Stochastic algorithms for big datasets. Parallel algorithms. Léon Bottou 2/30 COS 424 3/2/2010
3 Introduction General scheme Set a goal. Define a parametric model. Choose a suitable loss function. Choose suitable capacity control methods. Optimize average loss over the training set. Optimization Sometimes analytic (e.g. linear model with squared loss.) Usually numerical (e.g. everything else.) Léon Bottou 3/30 COS 424 3/2/2010
4 Summary 1. Convex vs. Nonconvex 2. Differentiable vs. Nondifferentiable 3. Constrained vs. Unconstrained 4. Line search 5. Gradient descent 6. Hessian matrix, etc. 7. Stochastic optimization Léon Bottou 4/30 COS 424 3/2/2010
5 Convex Definition x, y, 0 λ 1, f(λx + (1 λ)y) λf(x) + (1 λ)f(y) Property Any local minimum is a global minimum. Conclusion Optimization algorithms are easy to use. They always return the same solution. Example: Linear model with convex loss function. Curve fitting with mean squared error. Linear classification with log-loss or hinge loss. Léon Bottou 5/30 COS 424 3/2/2010
6 Nonconvex Landscape local minima, saddle points. plateaux, ravines, etc. Optimization algorithms Usually find local minima. Good and bad local minima. Result depend on subtle details. Examples Multilayer networks. Clustering algorithms. Learning features. Semi-supervised learning. Mixture models. Hidden Markov Models. Selecting features (some). Transfer learning. Léon Bottou 6/30 COS 424 3/2/2010
7 Differentiable vs. Nondifferentiable No such local cues without derivatives Derivatives may not exist. Derivatives may be too costly to compute. Examples Log loss versus Hinge loss. Léon Bottou 7/30 COS 424 3/2/2010
8 Constrained vs. Unconstrained Compare min w f(w) subject to w 2 < C min w f(w) + λw 2 Constraints Adding constraints lead to very different algorithms. Keywords Lagrange coefficients. Karush-Kuhn-Tucker theorem. Primal optimization, dual optimization. Léon Bottou 8/30 COS 424 3/2/2010
9 Line search - Bracketing a minimum Three points a < b < c such that f(b) < f(a) and f(b) < f(c). Léon Bottou 9/30 COS 424 3/2/2010
10 Line search - Refining the bracket Split the largest half and compute f(x). Léon Bottou 10/30 COS 424 3/2/2010
11 Line search - Refining the bracket Redefine a < b < c. Here a x. Split the largest half and compute f(x). Léon Bottou 11/30 COS 424 3/2/2010
12 Line search - Refining the bracket Redefine a < b < c. Here a b, b x. Split the largest half and compute f(x). Léon Bottou 12/30 COS 424 3/2/2010
13 Line search - Refining the bracket Redefine a < b < c. Here c x. Split the largest half and compute f(x). Léon Bottou 13/30 COS 424 3/2/2010
14 Line search - Golden Section Algorithm Optimal improvement by splitting at the golden ratio. Léon Bottou 14/30 COS 424 3/2/2010
15 Line search - Parabolic Interpolation Fitting a parabola can give much better guess. Léon Bottou 15/30 COS 424 3/2/2010
16 Line search - Parabolic Interpolation Fitting a parabola sometimes gives much better guess. Léon Bottou 16/30 COS 424 3/2/2010
17 Line search - Brent Algorithm Brent Algorithm for line search Alternate golden section and parabolic interpolation. No more than twice slower than golden section. No more than twice slower than parabolic section. In practice, almost as good as the best of the two. Variants with derivatives Improvements if we can compute f(x) and f (x) together. Improvements if we can compute f(x), f (x), f (x) together. Léon Bottou 17/30 COS 424 3/2/2010
18 Coordinate Descent Perform successive line searches along the axes. Tends to zig-zag. Léon Bottou 18/30 COS 424 3/2/2010
19 Gradient The gradient f ( w = f w,..., f ) 1 w d gives the steepest descent direction. Léon Bottou 19/30 COS 424 3/2/2010
20 Steepest Descent Perform successive line searches along the gradient direction. Beneficial if computing the gradients is cheap enough. Line searches can be expensive Léon Bottou 20/30 COS 424 3/2/2010
21 Gradient Descent Repeat w w γ f w (w) Merge gradient and line search. Large gain increase zig-zag tendencies, possibly divergent. High curvature direction limits gain size. Low curvature direction limits speed of approach. Léon Bottou 21/30 COS 424 3/2/2010
22 Hessian matrix Hessian matrix H(w) = 2 f w 1 w 1. 2 f w d w 1 2 f w 1 w d. 2 f w d w d Curvature information Taylor expansion near the optimum w : f(w) f(w ) (w w ) H(w ) (w w ) This paraboloid has ellipsoidal level curves. Principal axes are the eigenvectors of the Hessian. Ratio of curvatures = ratio of eigenvalues of the Hessian. Léon Bottou 22/30 COS 424 3/2/2010
23 Newton method Idea Since Taylor says f w (w) H(w) (w w ) then w w H(w) 1 f w (w). Newton algorithm w w H(w) 1 f w (w) Succession of paraboloidal approximations. Exact when f(w) is a paraboloid, e.g. linear model + squared loss. Very few iterations needed when H(w) is definite positive! Beware when H(w) is not definite positive. Computing and storing H(w) 1 can be too costly. Quasi-Newton methods Methods that avoid the drawbacks of Newton But behave like Newton during the final convergence. Léon Bottou 23/30 COS 424 3/2/2010
24 Conjugate Gradient algorithm Conjugate directions u, v conjugate u H v = 0. Non interacting directions. Conjugate Gradient algorithm Compute g t = f w (w t). Determine a line search direction d t = g t λd t 1 Choose λ such that d t H d t 1 = 0. Since g t g t 1 H (w t w t 1 ) H d t 1, this means λ = g t(g t g t 1 ) d t (g t g t 1 ). Perform a line search in direction d t. Loop. This is a fast and robust quasi-newton algorithm. A solution for all our learning problems? Léon Bottou 24/30 COS 424 3/2/2010
25 Optimization vs. learning Empirical cost Usually f(w) = 1 n ni=1 L(x i, y i, w) The number n of training examples can be large (billions?) Redundant examples Examples are redundant (otherwise there is nothing to learn.) Doubling the number of examples brings a little more information. Do we need it during the first optimization iterations? Examples on-the-fly All examples may not be available simultaneously. Sometimes they come on the fly (e.g. web click stream.) In quantities that are too large to store or retrieve (e.g. click stream.) Léon Bottou 25/30 COS 424 3/2/2010
26 Offline vs. Online Minimize C(w) = λ 2 w n n L(x i, y i, w). i=1 Offline: process all examples together Example: minimization by gradient descent ( ) Repeat: w w γ λw + 1 n L n w (x i, y i, w) i=1 Offline: process examples one by one Example: minimization by stochastic gradient descent Repeat: (a) Pick random example x t, y ( t (b) w w γ t λw + L ) w (x t, y t, w) Léon Bottou 26/30 COS 424 3/2/2010
27 Stochastic Gradient Descent Very noisy estimates of the gradient. Gain γ t controls the size of the cloud. Decreasing gains γ t = γ 0 (1 + λγ 0 t) 1. Why is it attractive? Léon Bottou 27/30 COS 424 3/2/2010
28 Stochastic Gradient Descent Redundant examples Increase the computing cost of offline learning. Do not change the computing cost of online learning. Imagine the dataset contains 10 copies of the same 100 examples. Offline Gradient Descent Computation is 10 times larger than necessary. Stochastic Gradient Descent No difference regardless of the number of copies. Léon Bottou 28/30 COS 424 3/2/2010
29 Practical example Document classification Similar to homework#2 but bigger. 781,264 training examples. 47,152 dimensions. Linear classifier with Hinge Loss Offline dual coordinate descent (svmlight): 6 hours. Offline primal bundle optimizer (svmperf): 66 seconds. Stochastic Gradient Descent: 1.4 seconds. Linear classifier with Log Loss Offline truncated newton (tron): 44 seconds. Offline conjugate gradient descent: 40 seconds. Stochastic Gradient Descent: 2.3 seconds. These are times to reach the same test set error. Léon Bottou 29/30 COS 424 3/2/2010
30 The wall 0.3 Testing cost 0.2 Training time (secs) 100 SGD 50 TRON (LibLinear) e 05 1e 06 1e 07 1e 08 1e 09 Optimization accuracy (trainingcost optimaltrainingcost) Léon Bottou 30/30 COS 424 3/2/2010
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