Antonio Ribeiro
2017-03-09 22:38:49 UTC
Hello, my name is Antonio and I am a Brazilian electrical engineer currently pursuing my master degree. I have contributed to scipy.optimize and scipy.signal implementing functions "iirnotch", "irrpeak" <https://github.com/scipy/scipy/pull/6404>and the method "trust-region-exact" <https://github.com/scipy/scipy/pull/6919> (under revision). I am interested in applying for the Google Summer of Code 2017 to work with the Scipy optimisation package.
My proposal is to improve scipy.optimize adding optimisation methods that are able to deal with non-linear constraints. Currently the only implemented methods able to deal with non-linear constraints are the FORTRAN wrappers SLSQP and COBYLA.
SLSQP is a sequential quadratic programming method and COBYLA is a derivative-free optimisation method, they both have its limitations: SLSQP is not able to deal with sparse
hessians and jacobians and is unfit for large-scale problems and COBYLA, as other derivative-free methods, is a good choice for optimise noisy objective functions, however usually presents a poorer performance then derivative-based methods when the derivatives are available (or even when they are computed by automatic differentiation or finite differences).
My proposal is to implement in Scipy one or more state-of-the-art solvers (interior point and SQP methods) for constrained optimisation problems. I would like to get some feedback about this, discuss the relevance of it for Scipy and get some suggestions of possible mentors.
My proposal is to improve scipy.optimize adding optimisation methods that are able to deal with non-linear constraints. Currently the only implemented methods able to deal with non-linear constraints are the FORTRAN wrappers SLSQP and COBYLA.
SLSQP is a sequential quadratic programming method and COBYLA is a derivative-free optimisation method, they both have its limitations: SLSQP is not able to deal with sparse
hessians and jacobians and is unfit for large-scale problems and COBYLA, as other derivative-free methods, is a good choice for optimise noisy objective functions, however usually presents a poorer performance then derivative-based methods when the derivatives are available (or even when they are computed by automatic differentiation or finite differences).
My proposal is to implement in Scipy one or more state-of-the-art solvers (interior point and SQP methods) for constrained optimisation problems. I would like to get some feedback about this, discuss the relevance of it for Scipy and get some suggestions of possible mentors.