mjzhu-p

GLIS

GLIS package

GLIS_MATLAB

bo_pr

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

casadi

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

edbo

Experimental Design via Bayesian Optimization_test

mjzhu-p.github.io

Personal webpage

nlopt

library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

nn-zero-to-hero

Neural Networks: Zero to Hero

PWAS

Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates (PWAS/PWASp)

ExpDesign

Supplementary material for "Discrete and mixed-variable experimental design with surrogate-based approach"

mjzhu-p/PWAS

Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates (PWAS/PWASp)

mjzhu-p/bo_pr

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

mjzhu-p/casadi

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

mjzhu-p/edbo

Experimental Design via Bayesian Optimization_test

mjzhu-p/mjzhu-p.github.io

Personal webpage

mjzhu-p/nlopt

library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

mjzhu-p/nn-zero-to-hero

Neural Networks: Zero to Hero

mjzhu-p/olympus

Olympus: a benchmarking framework for noisy optimization and experiment planning