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@ -274,9 +274,11 @@ the directional derivative is zero.
]
= Midterm 2 review
= Speedrun
I literally forgot everything...time to cram.
In this chapter I wrote up notes for the entirety of the course, starting from
week 1, ending at week 8, because I skipped 80% of the classes up to the
midterm.
== Vector review
@ -1078,3 +1080,31 @@ ignore it.
Now we just compare our four candidates and find the greatest (or least) for
optimization!
=== Notes from Week 7 section
We have a function $f : RR^n -> RR$ that is subject to a constraint $g : RR^n -> RR^c$, where $c$ is our number of constraints. It's really a vector of $c$ constraints,
$
g = vec(g_1,g_2,dots.v,g_c)
$
Idea: define the so-called *Lagrangian* $cal(L) = f + (g,lambda)$.
#theorem[
If $f$ and $g$ are "nice" (partials continuous), there are no redundant constraints, and it's not overconstrained ($"Rank" Dif g = c < n$). Then any optimal solution that respects $g = 0$ solves $gradient f = lambda dot Dif g$.
]
= Lecture #datetime(day: 27, year: 2025, month:2).display()
== Volume
Any 3D shape can be built recursively of atomic objects.
#exercise[
Derive formulae for the volume of a pyramid and cone.
]
Schley what are you doing???
== Signed area and volume