Distributed Frank-Wolfe code, described in "Distributing Frank-Wolfe via Map-Reduce", A. Moharrer and S. Ioannidis, ICDM, 2017.
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Distributed Frank-Wolfe via Spark

It is the implementation of the distributed Frank-Wolfe algorithm, described in "Distributing Frank-Wolfe via Map-Reduce", A.Moharrer, S.Ioannidis, ICDM, 2017.


An example execution is as follows:

spark-submit --master <master url> --executor-memory 100g modularDistFW.py --inputfile In1000by100  --outfile test --npartitions 20 --niterations 10  --problem DoptimalDist  --inputP vec100.npy --sampmode "No Drops"   --silent

This solves the D-Optimal Design problem by Parallel FW algorithm. The input dataset is loaded from "In1000by100". Maximum number of iterations is 10. The level of parallelism is set to 20.

Algorithm Overview

This is a generic implementation of distributed FW. Curretnly it solves D-Optimal Design, A-Optimal Design, Convex Approximation, and Adaboost. In order to solve any problem you need to define the gradient function, common information function, and the common information adaptation function, as discussed in the paper.

It reads the dataset from an input file. It is passed through inputfile in the command line to the code.

Command-line arguments

Several program parameters can be controlled from the command line.

usage: modularDistFW.py [-h] [--optgam OPTGAM] [--inputfile INPUTFILE]
                    [--outfile OUTFILE] [--npartitions NPARTITIONS]
                    [--niterations NITERATIONS] [--beta BETA]
                    [--sampmode SAMPMODE] [--ptr PTR]
                    [--randseed RANDSEED] [--problem PROBLEM]
                    [--remmode REMMODE] [--remfiles REMFILES] [--K K]
                    [--inputP INPUTP] [--C C] [--verbose | --silent]

optional arguments:
  -h, --help            show this help message and exit
  --optgam OPTGAM       If this value is 1 the setp size is found from Eq (6),
                    otherwise it is set to a diminshing step size.
  --inputfile INPUTFILE
                    The directory which holds the input files in text
 --outfile OUTFILE     The iutput file, which stires the objective value,
                    duality gap, and running time of the algorithm for
                    each iteration.
 --npartitions NPARTITIONS
                    Number of partitions
 --niterations NITERATIONS
                    Number of iterations
 --beta BETA           beta used in Smoothened FW.
 --sampmode SAMPMODE   It specifies the type of the algorithm. The options
                    are, No Drops, non smooth, smooth, and Lasso which run
                    parallel FW, Sampled FW, Smoothened FW, and parallel
                    FW for LASSO problem, respectively.
 --ptr PTR             Sampling ratio used in Sampled FW and Smoothened FW.
 --randseed RANDSEED   Random seed
 --problem PROBLEM     The type of the problem. Give DoptimalDist,
                    AoptimalDist, CVXapprox, or Adaboost, to solve
                    D-optimal Design, A-optimal Design, Convex
                    Approximation, or AdaBoost, respectively.
 --remmode REMMODE     If it is 0 then the algorithm starts from the
                    beginning. Otherwise it will continue the algorithm
                    from where the algorihtm stopped. It is helpful when
                    the job crashes.
 --remfiles REMFILES   The input file that keeps the RDD, It will continue
                    from this point. Use when remmode is 1.
 --K K                 The budget K for the l1 constraint. Use when sampmode
                    is LASSO.
 --inputP INPUTP       The vector P in Convex Approximation and the vector r
                    in Adaboost. Must be in .npy form.
 --C C                 The parametr C in Adaboost.

Citing This Work

If you intend to use our code in your research, please cite our paper as follows:

     author = {Moharrer, Armin and Ioannidis, Stratis},
     booktitle = {ICDM},
     title = {Distributing Frank-Wolfe via Map-Reduce},
     year = {2017}