Projects that are tagged with black box optimization.
Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.
Initial Announcement on mloss.org.
- Operating System:
Mac Os X
- Data Formats:
Black Box Optimization,
Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.
This is an important update:
full support for surrogates, allowing optimization of costly objective functions, ref #57
integrated rankign SVM default surrogate, ref #83
Python bindings for surrogates, ref #75
more informed optimization status and error messages, ref #85
API for computing confidence intervals around optima, ref #30
API for computing 2D contour around optima, ref #31
new 'elitist' scheme for improved restart strategy useful on some rather difficult functions, ref #77
fixed Eigen namespace import, ref #62
fixed and added new parameter vector getter in Candidate, ref #84