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Sleipnir
A linearity-exploiting sparse nonlinear constrained optimization problem solver that uses the interior-point method.
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This document contains educational resources for potential contributors.
Here's an introductory video on autodiff.
Ari Seff. What is Automatic Differentation?. 2020. https://www.youtube.com/watch?v=wG_nF1awSSY
3Blue1Brown's Essence of Linear Algebra video series provides geometric intuition for linear algebra.
3Blue1Brown. Essence of Linear Algebra, 2016. https://www.3blue1brown.com/topics/linear-algebra
Visually Explained's Convex Optimization video series provides geometric intuition for convex optimization and the interior-point method.
Visually Explained. Convex Optimization, 2021. https://www.youtube.com/playlist?list=PLqwozWPBo-FuPu4d9pFOobsCF1vDGdY_I
Sleipnir's authors learned numerical optimization from the following book and relied heavily upon it to implement Sleipnir.
Nocedal, J. and Wright, S. Numerical Optimization, 2nd. ed., Springer, 2006. https://www.math.uci.edu/~qnie/Publications/NumericalOptimization.pdf
Sleipnir's internals occasionally reference the following papers.
Wächter, A. and Biegler, L. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, 2005. http://cepac.cheme.cmu.edu/pasilectures/biegler/ipopt.pdf
Byrd, R. and Nocedal J. and Waltz R. KNITRO: An Integrated Package for Nonlinear Optimization, 2005. https://users.iems.northwestern.edu/~nocedal/PDFfiles/integrated.pdf