A Python package of accelerated first-order algorithms for solving relatively-smooth convex optimization problems

minimize { f(x) + P(x) | x in C }

with a reference function h(x), where C is a closed convex set and

• h(x) is convex and essentially smooth on C;
• f(x) is convex and differentiable, and L-smooth relative to h(x), that is, f(x)-L*h(x) is convex;
• P(x) is convex and closed (lower semi-continuous).

• BPG(Bregman proximal gradient) method with line search option
• ABPG (Accelerated BPG) method
• ABPG-expo (ABPG with exponent adaption)
• ABPG-gain (ABPG with gain adaption)
• ABDA (Accelerated Bregman dual averaging) method

Additional algorithms for solving D-Optimal Experiment Design problems:

• D_opt_FW (basic Frank-Wolfe method)
• D_opt_FW_away (Frank-Wolfe method with away steps)

Clone or fork from GitHub. Or install from PyPI:

pip install accbpg

Example: generate a random instance of D-optimal design problem and solve it using two different methods.

import accbpg

# generate a random instance of D-optimal design problem of size 80 by 200
f, h, L, x0 = accbpg.D_opt_design(80, 200)

# solve the problem instance using BPG with line search
x1, F1, G1, T1 = accbpg.BPG(f, h, L, x0, maxitrs=1000, verbskip=100)

# solve it again using ABPG with gamma=2
x2, F2, G2, T2 = accbpg.ABPG(f, h, L, x0, gamma=2, maxitrs=1000, verbskip=100)

# solve it again using adaptive variant of ABPG with gamma=2
x3, F3, G3, _, _, T3 = accbpg.ABPG_gain(f, h, L, x0, gamma=2, maxitrs=1000, verbskip=100)

D-optimal experiment design problems can be constructed from files (LIBSVM format) directly using

f, h, L, X0 = accbpg.D_opt_libsvm(filename)

All algorithms can work with customized functions f(x) and h(x), and an example is given in this Python file.

• A complete example with visualization is given in this Jupyter Notebook.

• All examples in HRX2018 can be found in the ipynb directory.

• Comparisons with the Frank-Wolfe method can be found in ipynb/ABPGvsFW.