09/29 - Analogy to Vector Spaces

Analogy to Vector Spaces

Let the vector ${\vec {v}}$ be defined as:

• ${\vec {v}}=a_{1}\cdot {\hat {v}}_{1}+a_{2}\cdot {\hat {v}}_{2}+a_{3}\cdot {\hat {v}}_{3}=\sum _{j=1}^{3}v_{j}\cdot {\hat {a}}_{j}$
  • $a_{1},a_{2},a_{3}\,\!$ are the coefficients
  • ${\hat {v}}_{1},{\hat {v}}_{2},{\hat {v}}_{3}$ are the basis vectors
  • A vector basis is a set of n linearly independent vectors capable of generating an n-dimensional subspace of $\Re ^{n}$
    • Generating: using a linear combination of n vectors to be able to uniquely identify any part of the n-dimensional space

Dot Product & Inner Product

Dot Product

The dot (scalar) product takes two vectors over the real numbers and returns a real-valued scalar quantity. Geometrically, it will show the projection of one vector onto another.

The dot product of two vectors ${\vec {a}}={a_{1},a_{2},...,a_{n}}\,\!$ and ${\vec {b}}={b_{1},b_{2},...,b_{n}}\,\!$ is defined as ${\vec {a}}\cdot {\vec {b}}=\sum _{i=1}^{n}a_{i}\cdot b_{i}=a_{1}b_{1}+a_{2}b_{2}+\cdots +a_{n}b_{n}$

Inner Product

Since we will be dealing with complex numbers, we need to use the inner product instead of the dot product

The inner product of two vectors ${\vec {a}}={a_{1}+b_{1}j,a_{2}+b_{2}j,...,a_{n}+b_{n}j}$ and ${\vec {b}}={c_{1}+d_{1}j,c_{2}+d_{2}j,...,c_{n}+d_{n}j}$ is defined as ${\vec {a}}\cdot {\vec {b}}=\sum _{i=1}^{n}a_{i}\cdot b_{i}^{*}$

• Where ${\vec {b}}_{i}^{*}={c_{1}-d_{1}j,c_{2}-d_{2}j,...,c_{n}-d_{n}j}$
• Where is this info on Wikipedia? http://en.wikipedia.org/wiki/Inner_product_space