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Read Section 1.2 for next class.
Lecture notes (this page) available from course web page by clicking on our class times.
Office hour: today, 12:30-1:30, MC103B.
Vector addition: $[u_1, \ldots, u_n] + [v_1, \ldots, v_n] :=
[u_1 + v_1, \ldots, u_n + v_n]$.
E.g. $[3, 2, 1] + [1, 0, -1] = [4, 2, 0]$.
Scalar multiplication: $c [u_1, \ldots, u_n] := [c u_1, \ldots, c u_n]$.
E.g. $2 [ 1 , 2, 3, 4, 5] = [2, 4, 6, 8, 10]$.
Zero vector: $\vec{0} := [0, 0, ..., 0]$.
The picture above shows geometrically that vector addition is commutative: $\vec{u} + \vec{v} = \vec{v} + \vec{u}$.
In this true in $\R^n$? Let's check: $$ \begin{aligned} \vec{u} + \vec{v} &= [u_1 + v_1, \ldots, u_n + v_n] \\ &= [v_1 + u_1, \ldots, v_n + u_n] \\ &= \vec{v} + \vec{u}. \end{aligned} $$
Many other properties that hold for real numbers also hold for vectors: Theorem 1.1. But we'll see differences later.
Example: Simplification of an expression: $$ \begin{aligned} &3 \vec{b} + 2 (\vec{a} - 4 \vec{b})\\ = &3 \vec{b} + 2 \vec{a} - 8 \vec{b}\\ = &2 \vec{a} - 5 \vec{b} \end{aligned} $$
An important real-world application:
Derive an equation for Inky's target on whiteboard.
Continue with Section 1.1 on whiteboard: linear combinations, modular arithmetic.
Next: lecture 3.