Geometric programming (GP) is popular for use in equation-based optimization of analog circuits thanks to GP-compatible analog performance functions, and its convexity, hence computational tractability. The main challenge in using GP, and thus a roadblock to wider use and adoption, is the mismatch between what GP can accurately fit, and the behavior of many common device/circuit functions.
['Ye Wang', 'Constantine Caramanis', 'Michael Orshansky']
PolyGP: Improving GP-based analog optimization through accurate high-order monomials and semidefinite relaxation