PyOPUS is a library for simulation-based optimization of arbitrary systems. It was developed with circuit optimization in mind. The library is the basis
for the PyOPUS GUI that makes it possible to setup design automation tasks with ease. In the GUI you can also view the the results and plot the waveforms generated by the simulator.
PyOPUS provides several optimization algorithms (Coordinate Search, Hooke-Jeeves, Nelder-Mead Simplex, Successive Approximation Simplex, PSADE (global), MADS, …). Optimization algorithms can be fitted with plugins that are triggered at every function evaluation and have full access to the internals of the optimization algorithm.
PyOPUS has a large library of optimization test functions that can be used for optimization algorithm development. The functions include benchmark sets by Moré-Garbow-Hillstrom, Lukšan-Vlček (nonsmooth problems), Karmitsa (nonsmooth problems), Moré-Wild, global optimization problems by Yao, Hedar, and Yang, problems used in the developement of MADS algorithms, and an interface to thousands of problems in the CUTEr/CUTEst collection. Benchmark results can be converted to data profiles that visualize the relative performance of optimization algorithms.
The “pyopus.simulator“ module currently supports SPICE OPUS, HSPICE, and SPECTRE (supports OP, DC, AC, TRAN, and NOISE analyses, as well as, collecting device properties like Vdsat). The interface is simple can be easily extended to support any simulator.
PyOPUS provides an extensible library of postprocessing functions which enable you to easily extract performance measures like gain, bandwidth, rise time, slew-rate, etc. from simulation results. The collected performance measures can be further post-processed to obtain a user-defined cost function which can be used for guiding the optimization algorithms toward better circuits.
Website: fides.fe.uni-lj.si/pyopus
Support:
Developer: Arpad Buermen
License: GNU General Public License v3.0
PyOPUS is written in C and Python. Learn C with our recommended free books and free tutorials. Learn Python with our recommended free books and free tutorials.
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