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@ -274,9 +274,11 @@ the directional derivative is zero.
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= Midterm 2 review
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= Speedrun
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I literally forgot everything...time to cram.
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In this chapter I wrote up notes for the entirety of the course, starting from
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week 1, ending at week 8, because I skipped 80% of the classes up to the
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midterm.
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== Vector review
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@ -1078,3 +1080,31 @@ ignore it.
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Now we just compare our four candidates and find the greatest (or least) for
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optimization!
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=== Notes from Week 7 section
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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,
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$
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g = vec(g_1,g_2,dots.v,g_c)
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$
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Idea: define the so-called *Lagrangian* $cal(L) = f + (g,lambda)$.
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#theorem[
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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 $gradient f = lambda dot Dif g$.
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]
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= Lecture #datetime(day: 27, year: 2025, month:2).display()
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== Volume
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Any 3D shape can be built recursively of atomic objects.
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#exercise[
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Derive formulae for the volume of a pyramid and cone.
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]
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Schley what are you doing???
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== Signed area and volume
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