Ludium
Sign In
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

Defining V ⊗ W as Bilinear Functionals on V′ × W′

How can v ⊗ w be defined without choosing any basis, by letting it act on pairs of functionals (φ, τ)?


Loading…

Questions

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

  1. Question 1

    In F3⊗F2\mathbf{F}^3 \otimes \mathbf{F}^2F3⊗F2, let v=(1,0,2)v = (1, 0, 2)v=(1,0,2), w=(3,−1)w = (3, -1)w=(3,−1), φ(x)=2x1+x3\varphi(x) = 2x_1 + x_3φ(x)=2x1​+x3​ and τ(y)=y1+2y2\tau(y) = y_1 + 2y_2τ(y)=y1​+2y2​. Find (v⊗w)(φ,τ)(v \otimes w)(\varphi, \tau)(v⊗w)(φ,τ).

    (v⊗w)(φ,τ)=(v \otimes w)(\varphi, \tau) =(v⊗w)(φ,τ)=
  2. Question 2

    Let V=L(F2,F3)V = \mathcal{L}(\mathbf{F}^2, \mathbf{F}^3)V=L(F2,F3) and W=P1(F)W = \mathcal{P}_1(\mathbf{F})W=P1​(F). Find dim⁡(V⊗W)\dim(V \otimes W)dim(V⊗W), where V⊗W=B(V′,W′)V \otimes W = \mathcal{B}(V', W')V⊗W=B(V′,W′).

    dim⁡(V⊗W)=\dim(V \otimes W) =dim(V⊗W)=
  3. Question 3

    Suppose (v⊗w)(φ,τ)=7(v \otimes w)(\varphi, \tau) = 7(v⊗w)(φ,τ)=7. Find ((3v)⊗(−2w)+v⊗(4w))(φ,τ)\big((3v) \otimes (-2w) + v \otimes (4w)\big)(\varphi, \tau)((3v)⊗(−2w)+v⊗(4w))(φ,τ).

  4. Question 4

    For v1,v2∈Vv_1, v_2 \in Vv1​,v2​∈V and w1,w2∈Ww_1, w_2 \in Ww1​,w2​∈W, which element of V⊗WV \otimes WV⊗W equals (v1+v2)⊗(w1−w2)(v_1 + v_2) \otimes (w_1 - w_2)(v1​+v2​)⊗(w1​−w2​)?

  5. Question 5

    In F2⊗F2\mathbf{F}^2 \otimes \mathbf{F}^2F2⊗F2, let t=(1,2)⊗(0,1)+(3,−1)⊗(2,2)t = (1, 2) \otimes (0, 1) + (3, -1) \otimes (2, 2)t=(1,2)⊗(0,1)+(3,−1)⊗(2,2), φ(x)=x1+x2\varphi(x) = x_1 + x_2φ(x)=x1​+x2​ and τ(y)=2y1+y2\tau(y) = 2y_1 + y_2τ(y)=2y1​+y2​. Find t(φ,τ)t(\varphi, \tau)t(φ,τ).

    t(φ,τ)=t(\varphi, \tau) =t(φ,τ)=
←Previous Why V × W Fails: Bilinear Functionals and dim B(V, W)Next A Basis of V ⊗ W, and v ⊗ w as a Grid of Products →