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Linear Algebra Done Right
Vector Spaces
01Complex Numbers as Pairs of Real Numbers02Lists and Fⁿ: Coordinate Addition and a First Proof03Vectors in R², Scalar Multiplication, and FieldsProblem setProblem set 2
01Definition of a Vector Space: Fⁿ, F^∞ and Function Spaces02Unique Zero and Inverses, 0v = 0, and the Subspace Test03Sums of Subspaces: The Smallest Subspace Containing Them04Direct Sums: Only One Way to Write ZeroProblem setProblem set 2
Finite-Dimensional Spaces
01Linear Combinations, Span, and Finite-Dimensional Spaces02Linear Independence and the Linear Dependence Lemma03Independent Lists Are No Longer Than Spanning ListsProblem setProblem set 2
01Bases: Reduce a Spanning List, Extend an Independent List02Dimension: Why Every Basis Has the Same Length03The Dimension of a Sum of Two SubspacesProblem setProblem set 2
Linear Maps
01Linear Maps and the Linear Map Lemma: Values on a Basis02Sums, Scalar Multiples and Products of Linear Maps03Null Space, Range, Injectivity and Surjectivity04The Fundamental Theorem of Linear Maps and Linear SystemsProblem setProblem set 2
01The Matrix of a Linear Map: Each Column Is Tvₖ02Matrix Operations and Why Multiplication Is Row Times Column03Rows, Columns and Linear Combinations in Matrix Products04Column Rank Equals Row Rank: Column-Row FactorizationProblem setProblem set 2
01Invertible Linear Maps: Injective and Surjective02Equal Dimensions: Injective or Surjective Means Invertible03Isomorphic Spaces and the Dimension of L(V, W)04The Matrix of a Vector: M(Tv) = M(T)M(v)05Change of Basis for an Operator: A = C⁻¹BCProblem setProblem set 2
01Products of Vector Spaces: When Dimensions Add02Quotient Spaces V/U: Parallel Lines as Vectors03The Quotient Map and V/null T ≅ range TProblem setProblem set 2
01The Dual Basis Reads Coefficients and T′ Reverses Arrows02The Annihilator U⁰ and dim U⁰ = dim V − dim U03Null T′ and Range T′ as Annihilators; M(T′) Is the TransposeProblem setProblem set 2
Polynomials and Eigenvalues
01Conjugates, |z| and the Complex Triangle Inequality02Zeros, Factors and the Division Algorithm via a Basis03Fundamental Theorem of Algebra and Complete Factorization04Real Polynomials: Linear and Irreducible Quadratic FactorsProblem setProblem set 2
01Invariant Subspaces and Eigenvalues: Tv = λv02Eigenvectors, T − λI and At Most dim V Eigenvalues03p(T) and Why Every Complex Operator Has an Eigenvalue04The Minimal Polynomial: Existence, Uniqueness and Computation05Eigenvalues Are the Zeros of the Minimal Polynomial06Why Odd Dimension Forces a Real EigenvalueProblem setProblem set 2
01Upper-Triangular Matrices and Invariant Spans02Eigenvalues Sit on the Upper-Triangular Diagonal03The Minimal Polynomial Decides Upper-TriangularityProblem setProblem set 2
01Diagonalizable Operators and Direct Sums of Eigenspaces02Four Equivalent Tests for Diagonalizability03Distinct Linear Factors Decide Diagonalizability04The Gershgorin Disk Theorem: Where Eigenvalues Live05Commuting Operators Share a Diagonalizing Basis06Commuting Operators Over C: One Upper-Triangular BasisProblem setProblem set 2
Inner Product Spaces
01Inner Products: Why Complex Spaces Need a Conjugate02Norms, Orthogonality and the Pythagorean Theorem03Cauchy-Schwarz and the Triangle InequalityProblem setProblem set 2
01Orthonormal Bases: Bessel's Inequality and Parseval02Gram-Schmidt, Orthonormal Bases and Schur's Theorem03Linear Functionals and the Riesz Representation TheoremProblem setProblem set 2
01Orthogonal Complements and the Direct Sum V = U ⊕ U⊥02Orthogonal Projection onto U and Why U⊥⊥ = U03Riesz Representation Without a Basis: v ⊥ null φProblem setProblem set 2
01The Closest Point Is the Projection: Fitting Sine on [−π, π]02The Pseudoinverse T† and Its Three Inverse-Like Properties03T†w: The Best Approximate Solution of Smallest NormProblem setProblem set 2
Self-Adjoint, Normal and Unitary Operators
01The Adjoint T*: Moving T into the Second Slot of ⟨Tv, w⟩02Null T* = (range T)⊥ and M(T*) as the Conjugate Transpose03Self-Adjoint Operators: Real Eigenvalues and Real ⟨Tv, v⟩04Normal Operators: ‖Tv‖ = ‖T*v‖ and Orthogonal EigenvectorsProblem setProblem set 2
01Real Spectral Theorem: Self-Adjoint ⇔ Orthonormal Eigenbasis02The Complex Spectral Theorem: Normal Is Enough03Positive Operators: Six Equivalent Characterizations04The Unique Positive Square Root √TProblem setProblem set 2
01Isometries: Five Equivalent Ways to Preserve Length02Unitary Operators: S⁻¹ = S*, Eigenvalues on |λ| = 103QR Factorization: Gram–Schmidt Recorded as A = QR04Cholesky Factorization: Positive Definite B = R*RProblem setProblem set 2
The Singular Value Decomposition
01Singular Values: Square Roots of the Eigenvalues of T*T02Singular Value Decomposition: Rank, Isometries, Proof03Diagonal Matrices, Adjoints and Pseudoinverses via SVD04Matrix SVD: Computing It and Factoring A = BDC*Problem setProblem set 2
01The Norm of a Linear Map Is Its Largest Singular Value02Computing the Norm of T: Three Formulas and Examples03Best Low-Rank Approximation: Truncate the SVD at k04Polar Decomposition: T = S√(T*T)Problem setProblem set 2
01Invertible Operators Turn Balls into Ellipsoids02Boxes to Boxes: Parallelepipeds and the SVD03Volume Scales by the Product of the Singular ValuesProblem setProblem set 2
Operators on Complex Vector Spaces
01Null Spaces of Powers Stabilize and Split the Space02Generalized Eigenvectors Span Every Complex Space03Linear Independence of Generalized Eigenvectors04Nilpotent Operators: Zero Is the Only EigenvalueProblem setProblem set 2
01Generalized Eigenspaces Split Every Complex Space02Multiplicity Without Determinants: Algebraic vs Geometric03Cayley–Hamilton Without Determinants04Upper-Triangular Diagonals Count Multiplicities05Block Diagonal Form: One Block per EigenvalueProblem setProblem set 2
01Every Invertible Complex Operator Has a Square Root02Jordan Bases: Strings and Ones Above the Diagonal03Proving Jordan Form via an Invariant Complement04The Trace Equals the Sum of the Eigenvalues05Why ST − TS Can Never Be the IdentityProblem setProblem set 2
Multilinear Algebra and Determinants
01Bilinear Forms: Linear in Each Slot, a Matrix per Basis02Change of Basis for Bilinear Forms: A = CᵀBC03Symmetric Bilinear Forms Are Diagonal in Some Basis04Every Bilinear Form Splits: Symmetric + Alternating05Quadratic Forms: Polarization and Sums of SquaresProblem setProblem set 2
01Alternating Multilinear Forms: Dependent Lists Give Zero02Swaps, Inversions and the Sign of a Permutation03Top-Degree Alternating Forms: A One-Dimensional Space04The Determinant of an Operator: αT = (det T)α05The Determinant Formula: A Signed Sum over PermutationsProblem setProblem set 2
01det(ST) = det S · det T: Invertibility and Eigenvalues02Similarity Invariance: One Determinant in Every Basis03det Aᵀ = det A: Row Operations and Gaussian Elimination04|det T| Is Volume: Unitary, Positive and Singular Values05The Characteristic Polynomial det(zI − T) over R and C06Hadamard's Inequality: Column Lengths Cap |det A|07The Vandermonde Determinant via Polynomial EvaluationProblem setProblem set 2
01Why V × W Fails: Bilinear Functionals and dim B(V, W)02Defining V ⊗ W as Bilinear Functionals on V′ × W′03A Basis of V ⊗ W, and v ⊗ w as a Grid of Products04The Universal Property: Bilinear Maps Become Linear05The Inner Product on V ⊗ W: Multiply the Factors06Tensor Products of m Spaces and m-Linear MapsProblem setProblem set 2

The Universal Property: Bilinear Maps Become Linear

How does every bilinear map on V × W become one linear map on V ⊗ W, and why does the proof need a basis?


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Questions

Work each one out before you check it. Every question has a scratch pad and a calculator.

  1. Question 1

    Let γ ⁣:F2×F2→F\gamma \colon \mathbf{F}^2 \times \mathbf{F}^2 \to \mathbf{F}γ:F2×F2→F, γ(v,w)=v1w1+2v2w1−v2w2\gamma(v, w) = v_1 w_1 + 2 v_2 w_1 - v_2 w_2γ(v,w)=v1​w1​+2v2​w1​−v2​w2​, with standard bases e1,e2e_1, e_2e1​,e2​ and f1,f2f_1, f_2f1​,f2​. Find γ^(3 e1⊗f1−e2⊗f2+2 e2⊗f1)\hat{\gamma}(3\, e_1 \otimes f_1 - e_2 \otimes f_2 + 2\, e_2 \otimes f_1)γ^​(3e1​⊗f1​−e2​⊗f2​+2e2​⊗f1​).

    γ^=\hat{\gamma} =γ^​=
  2. Question 2

    A linear map T ⁣:F2⊗F2→FT \colon \mathbf{F}^2 \otimes \mathbf{F}^2 \to \mathbf{F}T:F2⊗F2→F has values T(e1⊗f1)=1T(e_1 \otimes f_1) = 1T(e1​⊗f1​)=1, T(e1⊗f2)=−2T(e_1 \otimes f_2) = -2T(e1​⊗f2​)=−2, T(e2⊗f1)=0T(e_2 \otimes f_1) = 0T(e2​⊗f1​)=0, T(e2⊗f2)=3T(e_2 \otimes f_2) = 3T(e2​⊗f2​)=3 on the standard basis tensors. Find T♯((2,1),(1,1))T^\sharp\big((2, 1), (1, 1)\big)T♯((2,1),(1,1)).

    T♯=T^\sharp =T♯=
  3. Question 3

    On F2\mathbf{F}^2F2, let S=(0100)S = \begin{pmatrix} 0 & 1 \\ 0 & 0 \end{pmatrix}S=(00​10​) and T=(1002)T = \begin{pmatrix} 1 & 0 \\ 0 & 2 \end{pmatrix}T=(10​02​) (matrices in the standard basis). Let Φ ⁣:L(F2)⊗L(F2)→L(F2)\Phi \colon \mathcal{L}(\mathbf{F}^2) \otimes \mathcal{L}(\mathbf{F}^2) \to \mathcal{L}(\mathbf{F}^2)Φ:L(F2)⊗L(F2)→L(F2) be the linear map with Φ(A⊗B)=AB\Phi(A \otimes B) = ABΦ(A⊗B)=AB. Find the entry in row 111, column 222 of the matrix of Φ(S⊗T+T⊗S)\Phi(S \otimes T + T \otimes S)Φ(S⊗T+T⊗S).

  4. Question 4

    Let Φ ⁣:F2⊗F2,2→F2\Phi \colon \mathbf{F}^2 \otimes \mathbf{F}^{2,2} \to \mathbf{F}^2Φ:F2⊗F2,2→F2 be the linear map with Φ(v⊗A)=Av\Phi(v \otimes A) = AvΦ(v⊗A)=Av. For A=(1234)A = \begin{pmatrix} 1 & 2 \\ 3 & 4 \end{pmatrix}A=(13​24​) and B=(01−12)B = \begin{pmatrix} 0 & 1 \\ -1 & 2 \end{pmatrix}B=(0−1​12​), find Φ(e1⊗A+e2⊗B)\Phi(e_1 \otimes A + e_2 \otimes B)Φ(e1​⊗A+e2​⊗B), where e1,e2e_1, e_2e1​,e2​ is the standard basis of F2\mathbf{F}^2F2.

    first entry
    second entry
  5. Question 5

    Let γ(v,w)=v1w1+v2w2+v1w2\gamma(v, w) = v_1 w_1 + v_2 w_2 + v_1 w_2γ(v,w)=v1​w1​+v2​w2​+v1​w2​ on F2×F2\mathbf{F}^2 \times \mathbf{F}^2F2×F2 and let t=(1,2)⊗(3,0)+(0,1)⊗(1,1)t = (1, 2) \otimes (3, 0) + (0, 1) \otimes (1, 1)t=(1,2)⊗(3,0)+(0,1)⊗(1,1). Find γ^(t)\hat{\gamma}(t)γ^​(t).

    γ^(t)=\hat{\gamma}(t) =γ^​(t)=
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