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Feng, Jean, and Noah Simon. 2018. “Gradient-Based Regularization Parameter Selection for Problems With Nonsmooth Penalty Functions.” Journal of Computational and Graphical Statistics: A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 27 (2): 426–35.

External packages needed:

  • CVXPY is required for all scripts
  • Install Spearmint
  • Biopython is required to run the colitis data analysis. Also need to download
    • the geneset data from Molecular Signatures Database
    • GDS1615 from the Gene Expression Omnibus database

Create folders:

  • In this directory, create
    • spearmint_descent directory
    • results directory
    • For each example, it will need its own results folder:
      • e.g. results/matrix_completion_groups/tmp
    • To run the Colitis data example, create a realdata directory and put the downloaded data into this directory

Section 3 results were generated as follows:

  • Elastic Net: python
  • Sparse group lasso: python
  • Sparse Additive models: python

Section 4 results were generated as follows: python

By default, the code solves the joint optimization problem via gradient descent. Specify different solvers (grid search, Spearmint, and nelder-mead) and problem settings via command options.


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