MathIsimple
Part II

Vector Spaces

Vector spaces are the central objects of linear algebra. We define them abstractly over arbitrary fields, study their structure through subspaces and bases, and discover that dimension is the fundamental invariant.

5
Courses
14-18
Hours Total
Core
Level
Key Insights

Abstraction is Power

By working abstractly, theorems apply to ℝⁿ, function spaces, polynomial spaces, and more—all at once

Dimension is Fundamental

Two finite-dimensional spaces over the same field are isomorphic iff they have the same dimension

Coordinates are a Choice

A basis gives coordinates, but there are infinitely many bases—coordinate-free thinking is often cleaner

Building New Spaces

Direct sums combine spaces, quotients collapse subspaces—these constructions are everywhere in algebra

Courses in This Part

LA-2.1
Vector Space Definition
Available
Abstract definition of vector spaces over a field, the eight axioms, and fundamental examples
3-4 hours
Vector Space Axioms
Examples over ℝ and ℂ
Function Spaces
Polynomial Spaces
Basic Properties
LA-2.2
Subspaces
Available
Subspace definition and criteria, intersection and sum of subspaces, and linear span
2-3 hours
Subspace Criterion
Intersection & Sum
Linear Span
Generating Sets
LA-2.3
Linear Independence
Available
Linear dependence and independence, Steinitz exchange lemma, and maximal independent sets
3-4 hours
Dependence & Independence
Testing Independence
Steinitz Exchange
Extending Independent Sets
LA-2.4
Basis & Dimension
Available
Basis definition, existence and uniqueness of dimension, coordinate systems
3-4 hours
Basis Definition
Dimension
Coordinates
Finite vs Infinite Dimensional
LA-2.5
Direct Sums & Quotients
Available
Internal and external direct sums, complementary subspaces, quotient spaces
3-4 hours
Internal Direct Sum
External Direct Sum
Complements
Quotient Spaces V/W