Linear algerba II 2018
Please use "LA2018" as part of subject of your emails.
Office hours
Office hours: by appointment (prefferably Tuesday or Wednesday). S322 (Malostranské nám. 25, 3rd floor).
Lectures
 Feb 22
 Definition of norm (Section 7.2 of Poole) and inner product for real values (Section 7.1 of Poole) and complex values. Examples and main properties (Theorem 7.1 of Poole), Definition of length, distance and orhogonality. Pythagoras theorem (Theorem 7.2 of Poole), Orthogonal projection, Cauchy–Schwarz inequality.
 Mar 2
 Norm induced by the inner product satisfies triangle inequality (Theorem 7.4 of Poole), parallelogram law. Definition of an orthogonal and orthonormal set/base (Section 5.1 of poole but for inner products), Fourier coefficients (example in Poole). Gram–Schmidt orthogonalization (Poole sections 5.3 and 7.1).
 Mar 9
 Brief intro to Fourier transform, discrete Fourier transform and discrete cosine transform and applications (will not be on exam).
Corollaries of Gram–Schmidt orthogonalization: every finitely generated inner produce space has orthonormal base; every set of orthonormal vectors extends to orthonormal base.
Orthogonal complement in inner product space (Teorem 5.2 of poole for inner products rather than dot products). Properties of complement of sets and of complements of subspace (Thm 5.9 of Poole for inner produce spaces). Orthogonal projection: definition and orthogonal projection theorem.
 Mar 16

Fundamental spaces of matrix: Row(A) is orthogonal to Null(A) (Thm 5.10 of Poole), corollary about fundamental spaces of A^{T}T. Least square approximation (Section 7.3 of Poole). Review of linear transformations: Kernel and Range (Section 6.3 of Poole). Matrix of linear transormation. Composition of linear transformations is a matrix product. Inverse of linear transformation is inverse of a matrix. Definition of orthogonal and unitary matrix. Characterisaiton, examples.
 Mar 23
 This class will be replaced at Tuesday May 22.
 Mar 30
 Orthogonal matrices and linear transformations. Determinants  definition and basic properties. Effect of row operations on determinant. Computing determinant using REF. Matrix is regular iff its determinent is nonzero. Laplace method.
 April 5
 Cramer rule. Adjoint matirx. Computing inverses by determinnat. Geometric interpretation of determinants (volume and orientation)
 Eigenvalues, eigenvectors  definition, geometric interpretation. Characterisation of eigenvalues. Characteristic polynomial. Eigenvalues are roots of the characteristic polynomial. Geometric and algebraic multiplicity of eigenvalue. Produc and sum of eigenvalues. Spectrum and dominating eigenvalue.
 April 12
 Properties of eigenvalues. Companion matrix (not needed for exam): finding eigenvalues of n x n matrix is as hard as finding roots of polynomials of degree n. Similar metrices. Diagonalization. Matrix is diagonalizable iff it has n linearly independent eigenvectors. Eigenvalues of AB and BA are the same. Examples: power of matrices, recurences.
 April 19
 Jordan normal form and properties: number of blocks in jordan form is number of eigenvectors (without proof). Corollary: algebraic muliplicity of eigenvalue is greater or equal the geometric multiplicity. Harmitian tranposition and hermitian matrix. Eigenvalues of symmetric real (and complex hermitian) are real. Spectral decomposition (orthogonal diagonalization)
 April 27
 CourantFisher minmax theorem as an application of spectral decomposition (not necessary for exam). Gershgorin circle theorem. Power method and its convergence (Section 4.5 of Poole's book). Google pagerank.
 May 3
 More on google pagerank (will not be part of an eam). Deflation of eigenvalues. Symmetric positivesemidefinite (PSD) and positivedefinite matrices (PSD). Characterisation of positivedefinite matrices. Properties of positivedefinite matrices. Characterisation of positive semidefinite matrices.
 May 10
 Recurrence for testing postiviedefiniteness. Cholesky decomposition. Algorithm for cholesky decomposition. Sylvester's criterrion for PSD. The correspondence between SPD and inner product.
 Plan

Squareroot of a matrix. Sylverster's critterion for PSD.
Bilinear and quadratic forms: definition, matrix form, change of basis. Sylvester's law of inertia. Diagonalization of quadratic forms. Matrix decompositions.
Sample exam
Resources
 Linear Algebra I by Andrew Godall
 David Poole, Linear Algebra, A Modern Introduction, 3rd Int. Ed., Brooks Cole, 2011.
 Essence of Linear Algebra (3Blue1Brown YouTube series)
 C. D. Meyer. Matrix analysis and applied linear algebra. SIAM, Philadelphia, PA, 2000.
 J. Hefferon: Linear algebra
 Robert Beezer, A First Course in Linear Algebra  a free online textbook.
 MIT's website includes video lectures and supplementary material to Gilbert Strang's Introduction to Linear Algebra. Strang's own sample material etc.
 Pavel Klavík's orgpad map of Linear Algebra
 Jiri Matousek's preliminary version of the recommended book Thirtythree miniatures: mathematical and algorithmic applications of linear algebra.
 M. Anderson and T. Feil, A First Course in Abstract Algebra: Rings, Groups, and Fields, CRC, 2015 (chapters 1719 for groups).
 F. Goodman, Algebra: Abstract and Concrete, edition 2.6
 Permutations (CuttheKnot.org)