Product of Vector Space
Last edited: August 8, 2025A product of vector spaces is a vector space formed by putting an element from each space into an element of the vector.
constituents
Suppose \(V_1 \dots V_{m}\) are vector spaces over the same field \(\mathbb{F}\)
requirements
Product between \(V_1 \dots V_{m}\) is defined:
\begin{equation} V_1 \times \dots \times V_{m} = \{(v_1, \dots, v_{m}): v_1 \in V_1 \dots v_{m} \in V_{m}\} \end{equation}
“chain an element from each space into another vector”
additional information
operations on Product of Vector Spaces
The operations on the product of vector spaces are defined in the usual way.
product summation map
Last edited: August 8, 2025Let \(U_1, \dots, U_{m}\) be subspaces of \(V\); we define a linear
We define \(\Gamma\) to be a map \(U_1 \times \dots U_{m} \to U_1 + \dots + U_{m}\) such that:
\begin{equation} \Gamma (u_1, \dots, u_{m}) = u_1 + \dots + u_{m} \end{equation}
Essentially, \(\Gamma\) is the sum operation of the elements of the tuple made by the Product of Vector Spaces.
\(U_1 + \dots + U_{m}\) is a direct sum IFF \(\Gamma\) is injective
Proof:
Production Index
Last edited: August 8, 2025This is a work-in-progress page listing all of my production projects.
Fireside: Blog
20MinuteRants: Blog
https://medium.com/20minuterants
(finished) Project80: Podcast
See .
(finished) Yappin: Podcast
Productivity Starter Pack
Last edited: August 8, 2025So you wanted to be productive?
Go do stuff. Stop reading. Get crap done.
… … …
Wait, you are still here? Well, given that you are sticking around, we might as well discuss some tooling that may help you in organizing your work. By all means I don’t think this is a complete list, many of these have intersecting features; think of this as more a survey of the field—
products and quotients, the intuition
Last edited: August 8, 2025We mentioned this in class, and I figured we should write it down.
So, if you think about the Product of Vector Space:
\begin{equation} \mathbb{R} \times \mathbb{R} \end{equation}
you are essentially taking the \(x\) axis straight line and “duplicating” it along the \(y\) axis.
Now, the opposite of this is the quotient space:
\begin{equation} \mathbb{R}^{2} / \left\{\mqty(a \\ 0): a \in \mathbb{R} \right\} \end{equation}
Where, we are essentially taking the line in the \(x\) axis and squish it down, leaving us only the \(y\) component freedom to play with (as each element is \(v +\left\{\mqty(a \\ 0): a \in \mathbb{R} \right\}\)).