Computing and Optimization Fall 2021, Princeton University (undergraduate course)
Useful links
Lectures The notes below summarize most of what I cover during lecture. Please complement them with your own notes. Some lectures take one class session to cover, some others take two.
 Lecture 1: Let's play two games! (Optimization, P and NP.)
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 Lecture 2: What you should remember from linear algebra and multivariate calculus.
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 Lecture 3: Unconstrained optimization, least squares, optimality conditions.
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 Lecture 4: Convex optimization I.
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 Lecture 5: Convex optimization II.
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 CVX: Basic examples.
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 Lecture 6: Applications in statistics and machine learning: LASSO + Support vector machines (SVMs)
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 Lecture 7: Root finding and line search. Bisection, Newton, and secant methods.
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 Lecture 8: Gradient descent methods, analysis of steepest descent, convergence and rates of convergence, Lyapunov functions for proving convergence.
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 Lecture 9: Multivariate Newton, quadratic convergence, Armijo stepsize rule, nonlinear least squares and the GaussNewton algorithm.
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 Lecture 10: Conjugate direction methods, solving linear systems, Leontief economy.
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 Lecture 11: Linear programming: applications, geometry, and the simplex algorithm.
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 Lecture 12: Duality + robust linear programming.
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 Lecture 13: Semidefinite programming + SDP relaxations for nonconvex optimization.
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 Lecture 14: A working knowledge of computational complexity theory for an optimizer.
[pdf]  Lecture 15: Limits of computation + course recap.
