Table 1 Running times (in seconds) of different SPCA algorithms

From: Stochastic convex sparse principal component analysis

Sample size	Cvx-SPCA	[9]	[12]	[11]
n=50 k	20.9	207.1	48.7	3002
n=100k	26.2	466.9	78.3	3237.4
n=500 k	35.6	2737.06	2661.7	5276.93
n=1m	35.8	3408.59	3568	5274.26

Since proposed Cvx-SPCA does not depend on eigenvalue decomposition or semi-definite programming, it is more scalable in terms of the sample size. It also requires less iterations to reach a desired sparsity

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