All-at-once optimization for CP tensor decomposition
We explain how to use cp_opt with the POBLANO toolbox.
Contents
Poblano Optimization Toolbox
Check that you have Poblano 1.1 installed. The output of your 'ver' command should look something like the following.
ver
------------------------------------------------------------------------------------- MATLAB Version 7.13.0.564 (R2011b) MATLAB License Number: 192525 Operating System: Microsoft Windows 7 Version 6.1 (Build 7600) Java VM Version: Java 1.6.0_17-b04 with Sun Microsystems Inc. Java HotSpot(TM) 64-Bit Server VM mixed mode ------------------------------------------------------------------------------------- MATLAB Version 7.13 (R2011b) Poblano Toolbox (Sandia National Labs) Version 1.1 Statistics Toolbox Version 7.6 (R2011b) Tensor Toolbox (Sandia National Labs) Version 2.5
Create an example problem. Here we have 10% noise.
R = 5; info = create_problem('Size', [50 40 30], 'Num_Factors', R, 'Noise', 0.10); X = info.Data; M_true= info.Soln;
Create initial guess using 'nvecs'
M_init = create_guess('Data', X, 'Num_Factors', R, ... 'Factor_Generator', 'nvecs');
Set up the optimization parameters
It's genearlly a good idea to consider the parameters of the optimization method. The default options may be either too stringent or not stringent enough. The most important options to consider are detailed here.
% Get the defaults ncg_opts = ncg('defaults'); % Tighten the stop tolerance (norm of gradient). This is often too large. ncg_opts.StopTol = 1.0e-6; % Tighten relative change in function value tolearnce. This is often too large. ncg_opts.RelFuncTol = 1.0e-20; % Increase the number of iterations. ncg_opts.MaxIters = 10^4; % Only display every 10th iteration ncg_opts.DisplayIters = 10; % Display the final set of options ncg_opts
ncg_opts =
Display: 'iter'
DisplayIters: 10
LineSearch_ftol: 1.0000e-004
LineSearch_gtol: 0.0100
LineSearch_initialstep: 1
LineSearch_maxfev: 20
LineSearch_method: 'more-thuente'
LineSearch_stpmax: 1.0000e+015
LineSearch_stpmin: 1.0000e-015
LineSearch_xtol: 1.0000e-015
MaxFuncEvals: 10000
MaxIters: 10000
RelFuncTol: 1.0000e-020
RestartIters: 20
RestartNW: 0
RestartNWTol: 0.1000
StopTol: 1.0000e-006
TraceFunc: 0
TraceFuncEvals: 0
TraceGrad: 0
TraceGradNorm: 0
TraceRelFunc: 0
TraceX: 0
Update: 'PR'
Call the cp_opt method
Here is an example call to the cp_opt method. By default, each iteration prints the least squares fit function value (being minimized) and the norm of the gradient. The meaning of any line search warnings can be checked via doc cvsrch.
[M,~,output] = cp_opt(X, R, 'init', M_init, ... 'alg', 'ncg', 'alg_options', ncg_opts);
Iter FuncEvals F(X) ||G(X)||/N
------ --------- ---------------- ----------------
0 1 75593.24987686 0.70699321
10 83 833.36966011 0.82018460
20 131 742.99421395 0.01035414
30 158 742.97545770 0.00016140
40 178 742.97544883 0.00000137
42 182 742.97544883 0.00000093
Check the output
It's important to check the output of the optimization method. In particular, it's worthwhile to check the exit flag. A zero (0) indicates successful termination with the gradient smaller than the specified StopTol, and a three (3) indicates a successful termination where the change in function value is less than RelFuncTol. The meaning of any other flags can be checked via doc poblano_params.
exitflag = output.ExitFlag
exitflag =
0
The fit is the percentage of the data that is explained by the model. Because we have noise, we do not expect the fit to be perfect.
fit = output.Fit
fit = 99.0203
Evaluate the output
We can "score" the similarity of the model computed by CP and compare that with the truth. The score function on ktensor's gives a score in [0,1] with 1 indicating a perfect match. Because we have noise, we do not expect the fit to be perfect. See doc score for more details.
scr = score(M,M_true)
scr =
0.9986
Overfitting example
Consider the case where we don't know R in advance. We might guess too high. Here we show a case where we guess R+1 factors rather than R.
% Generate initial guess of the corret size M_plus_init = create_guess('Data', X, 'Num_Factors', R+1, ... 'Factor_Generator', 'nvecs');
% Loosen the stop tolerance (norm of gradient).
ncg_opts.StopTol = 1.0e-2;
% Run the algorithm [M_plus,~,output] = cp_opt(X, R+1, 'init', M_plus_init, ... 'alg', 'ncg', 'alg_options', ncg_opts); exitflag = output.ExitFlag fit = output.Fit
Iter FuncEvals F(X) ||G(X)||/N
------ --------- ---------------- ----------------
0 1 75593.78388667 0.58917501
10 83 833.52870514 0.68349823
20 131 743.08624109 0.00959787
exitflag =
0
fit =
99.0201
% Check the answer (1 is perfect)
scr = score(M_plus, M_true)
scr =
0.9984