readme.md

Hypergradient Descent with Polyak Momentum

This is the official implementation of Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent.

Reproducing the results

To reproduce the experiments in the paper,

1. Ensure that scikit_learn, numpy, scipy, seaborn, and matplotlib are installed

   See requirements.txt for more details

2. Download datasets from LIBSVM. Check the list at the end for benchmark files.

3. Place the datasets in problems directory

4. Execute the following commands

   chmod +x ./*.sh
   ./get_svm_data.sh
   ./get_logistic_data.sh
   

   to reproduce the experiments. The following logs will be printed to the screen

   Running a1a
   ================================================
                 Solver [S]   nFvalCall   nGradCall
                     GD [0]           0        1000
                  GD-HB [0]           0        1000
                AGD-CVX [0]           0        1000
               AGD-SCVX [0]           0        1000
                   Adam [1]           0         566
                AdaGrad [0]           0        1000
                   BFGS [1]          91          91
              L-BFGS-M1 [0]        1124        1124
              L-BFGS-M3 [1]         522         522
              L-BFGS-M5 [1]         531         531
             L-BFGS-M10 [1]         328         328
               HDM-Best [1]         275         276
   ================================================
   

and figures will be saved in figures directory.

Tested LIBSVM instances

a1a, a2a, a3a, a4a, a5a, a6a, a7a, a8a, a9a, australian_scale, fourclass_scale, german, gisette_scale, gisette_scale, heart_scale, ijcnn1, ionosphere_scale, leu, liver-disorders_scale, mushrooms, phishing, skin_nonskin, sonar_scale, splice_scale, svmguide1, svmguide3, w1a, w2a, w3a, w4a, w5a, w6a, w7a, w8a

Code maintainance and contact

gwz@stanford.edu