MCPy is a python library for automatically calculating convex\concave relaxations and subgradients of factorable nonconvex functions according to McCormick relaxation rules and interval arithmetic. The library could be quite useful for prototyping and testing new global optimization algorithms.

Theory and implementation for the global optimization of a wide class of algorithms is presented via convex/affine relaxations [1]. Similar to the convex/concave relaxation, the subgradient propagation relies on the recursive application of specific rules, which could provide us affine relaxations of McCormick relaxations. This library automatedly implements those theorems based on normal and reverse operator overloading.

Supported rules so far: +, -, /, *, sqrt, log, exp, **(restricted to integer powers).
Upcoming rules: sin, cos.

There are three instance variables in the class, MCPy.IA, MCPy.MC, MCPy.SG.
MCPy.IA
1-D numpy array of two elements [LB, UB].
LB/UB are the lower/upper bound the function calculated by the interval arithmetic.
MCPy.MC
1-D numpy array of two elements [cv, cc].
cv/cc are the convex underestimator/concave overestimators of the function calculated by the McCormick rules.
MCPy.SG
2-D numpy n-by-2 matrix [SG_cv, SG_cc].
SG_cv/SG_cc are n-by-1 column verctors of subgradients for convex/concave relaxations.

(a) Import the MCPy class
(b) Initialize the variables
(c) Do the calculation
See examples file for more details, including the usage, plots, and animations.

See convergence file.

[1] Mitsos, A., B. Chachuat, P.I. Barton, McCormick-based relaxations of algorithms, SIAM Journal on Optimization, 20(2):573-601, 2009
[2] Bompadre, A., A. Mitsos, Convergence rate of McCormick relaxations, Journal of Global Optimization 52(1):1-28, 2012
[3] Y. Shao and J. K. Scott. Convex relaxations for global optimization under uncertainty described by continuous random variables, arXiv preprint. 2017.

Citation of our recent work:
@Article{Shao:2017,
  author = {Shao, Y. and Scott, J. K.},
  title  = {Convex Relaxations for Global Optimization Under Uncertainty Described by Continuous Random Variables (preprint: arXiv:1709.08780)},
  year   = {2017},
  url    = {arXiv:1709.08780},
}