Computational methods in optimization David F. Gleich Purdue - PowerPoint PPT Presentation

Dec 03, 2023 •600 likes •829 views

Computational methods in optimization David F. Gleich Purdue University Thanks to Nick Henderson for many slides. 1 Course objectives To understand optimization To be able to optimize a function Course outline Background Software Least

Computational methods in optimization David F. Gleich Purdue University Thanks to Nick Henderson for many slides. 1
Course objectives To understand optimization To be able to optimize a function
Course outline
Background Software Least Squares Matrix calculus
Unconstrained Optimization Non-linear equations Newton methods Line search minimize ƒ ( � ) Trust region Quasi-newton
Constrained Optimization Linear programming minimize ƒ ( � ) Quadratic   � programming A � subject to � ≤  ≤ �    Large-scale c ( � )
Modern Topics Convex Integer Stochastic
Questions about topics?
Your first quiz
Source: http://xkcd.com/135/
Raptors move at 25 m/s You move at 6 m/s
But who cares?
The new model choose direction to run v p [ j ] for j = { 1 , . . . , N } N 3 1 to minimize “likelihood” of X X k p [ j ] � r i [ j ] k 2 dt being eaten j =1 i =1 subject to raptor motion p [ j ] � r i [ j ] r i [ j + 1] = r i [ j ] + hv i k p [ j ] � r i [ j ] k human motion p [ j + 1] = p [ j ] + h v p [ j ] Thanks to Nick Henderson for many slides.
How it’s done modeling solver model environment (SNOPT) (AMPL) web service (NEOS) direct (Matlab, C, Fortran) Thanks to Nick Henderson for many slides.
Solve!
time = 2.65 sec
Source: http://en.wikipedia.org/wiki/Velociraptor
What are your applications?

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