The question arises: what is the scope of compressive sensing? What you show is l1 regularization. This has been around far longer than the term 'compressive se

The question arises: what is the scope of compressive sensing? What you show is l1 regularization. This has been around far longer than the term 'compressive sensing'. Even the theory underlying it pre-dates 'compressive sensing' [1]. I think the 'compressive' 'sensing' aspect comes about due to the possibility of literally, 'compressively sensing', not just in using 'l1-regularization'. Further, an ambiguity arises in the concept of the interactive graphics in the OP website: the graphical explanation shown holds not just for 'l1' regularization, but also for say, 'l2' regularization (and other regularizations). The 'bar' could also represent a scaled version of the l2 norm. It is not easily apparent why it should only be 'l1'. When it comes to graphical intuition for compressive sensing, I find the 'l2-ball' (intersects 'low-dimensional measurements' at dense points ) vs. 'l1-ball' (intersects 'low-dimensional measurement' equations on sparse points in the plane, thus 'promoting sparsity', see for example, [2]) to be more lucid. Since you already have the excellent interactive web-site, can you possibly include the l1-ball vs. l2-ball [2] graphic? [1] R. Tibshirani, "The Lasso page "Regression shrinkage and selection via the lasso". Journal of the Royal Statistical Society, Series B 58 (1): 267–288. [2] R. Baraniuk, "Compressive sensing." IEEE signal processing magazine 24.4 (2007). Edit: Another suggestion: The OP website format is a great way to demonstrate the utility of regularizations in optimization, which has wide applications in machine learning, data interpolation, etc.[3] [3] http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf