Algorithms for Optimization Jupyter Notebooks

This repository contains supplemental Jupyter notebooks to accompany Algorithms for Optimization by Mykel Kochenderfer and Tim Wheeler. These notebooks were generated from the Algorithms for Optimization source code. We provide these notebooks to aid with the development of lectures and understanding the material, with the hope that you find it useful.

Installation

All notebooks have Julia 1.0.1 kernels. Julia can be installed here.

Rendering is managed by PGFPlots.jl. Please see their documentation for important installation instructions.

Julia notebooks are supported by IJulia.

Once the repo is cloned, one can set up the required packages from the terminal before launching the jupyter notebook:

export JULIA_PROJECT="@."
julia -e 'using Pkg; pkg"instantiate"'
jupyter notebook

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
LICENSE.md		LICENSE.md
Manifest.toml		Manifest.toml
README.md		README.md
REQUIRE		REQUIRE
bracketing.ipynb		bracketing.ipynb
derivatives.ipynb		derivatives.ipynb
descent.ipynb		descent.ipynb
design-under-uncertainty.ipynb		design-under-uncertainty.ipynb
direct.ipynb		direct.ipynb
discrete.ipynb		discrete.ipynb
expr.ipynb		expr.ipynb
first-order.ipynb		first-order.ipynb
gp.jl		gp.jl
introduction.ipynb		introduction.ipynb
julia.ipynb		julia.ipynb
linear.ipynb		linear.ipynb
math-concepts.ipynb		math-concepts.ipynb
mdo.ipynb		mdo.ipynb
multiobjective.ipynb		multiobjective.ipynb
penalty.ipynb		penalty.ipynb
population.ipynb		population.ipynb
prob-surrogate-models.ipynb		prob-surrogate-models.ipynb
sampling-plans.ipynb		sampling-plans.ipynb
second-order.ipynb		second-order.ipynb
stochastic.ipynb		stochastic.ipynb
support_code.jl		support_code.jl
surrogate-models.ipynb		surrogate-models.ipynb
surrogate-optimization.ipynb		surrogate-optimization.ipynb
test-functions.ipynb		test-functions.ipynb
uncertaintyprop.ipynb		uncertaintyprop.ipynb

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yibit/algforopt-notebooks

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Algorithms for Optimization Jupyter Notebooks

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