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Lectures on optimization methods
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Optimization methods, Department of Innovation and High Technologies
Lectures on optimization methods and applications
Fall 2021
Fall 2022
Syllabus
Introduction. Convex sets and cones
Dual cone. Automatic differentiation
.
JAX demo
Convex functions
Convex optimization problems
KKT optimality conditions and intro to duality
Conic duality intro
Introduction to numerical optimization. Gradient descent and lower bounds concept
Beyond gradient descent: heavy ball, conjugate gradient and fast gradient methods
Stochastic first-order methods
Newton and quasi-Newton methods
Projected gradient method, Frank-Wolfe method and introduction to proximal methods
Semidefinite programming
Packages for solving convex optimization problems + DCP and ipopt demo
Open Source Agenda is not affiliated with "Optimization Fivt" Project. README Source:
amkatrutsa/optimization-fivt
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Last Commit
1 year ago
Repository
amkatrutsa/optimization-fivt
License
MIT
Tags
Convex Optimization
Lectures
Numerical Optimization
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