Linear Algebra

Learning Outcomes

  1. describe and apply linear algebra basic concepts and methods
  2. demonstrate fundamental skills of matrix calculus and solving linear systems of equations
  3. apply fundamental knowledge of vector analysis and space analytic geometry
  4. demonstrate basic knowledge of vector spaces and linear operators
  5. demonstrate an ability to express mathematical ideas and abstract thinking in linear algebra
  6. demonstrate an ability to basic problem solving and reaching conclusions in linear algebra
  7. use methods of linear algebra in engineering

Forms of Teaching

Lectures

Lectures are held in two cycles, 4 hours per week

Exercises

Exercises are held in two cycles, 4 hours per week

Partial e-learning

Homework is accessible on course web-page.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Homeworks 50 % 0 % 0 % 0 %
Attendance 50 % 0 % 0 % 0 %
Mid Term Exam: Written 0 % 50 % 0 %
Final Exam: Written 0 % 50 %
Exam: Written 0 % 100 %
Comment:

Regular attendance of lectures and doing homework are requirement for taking exam.

Week by Week Schedule

  1. Matrices. Basic types. Operations with matrices. Inverse matrix. Regular and singular matrices
  2. Determinants. Basic properties, computation of determinants. Laplace's rule, Elementary transformations, rank of a matrix, linear independence and rank. Characterization of regular matrices
  3. Elementary transformations, rank of a matrix, linear independence and rank. Characterization of regular matrices, Characterization of regular matrices using determinant. Computation of the inverse matrix
  4. Gaussian elimination method. Homogeneous and nonhomogeneous systems, Rank of a system and rank of extended matrix
  5. Rank of a system and rank of extended matrix, Cramer's rule. Comparison of computational complexity of the Gauss and Cramer algorithms
  6. Operations with vectors. Linear dependence and independence in V^2 and V^3. Bases in V^2 and V^3. Canonical base. Vectors in coordinate systems, Dot product. Norm, ortogonality. Vector and scalar projections of one vector on another
  7. Cross product of two vectors and mixed product of three vectors, Radius-vector. Coordinates of the midpoint of a segment and of the barycenter of a triangle. Convex combinations of vectors. Convex hull. Convex set
  8. Midterm exam
  9. Equations of plane. Mutual position of two planes. Distance between a point and a plane
  10. Line, parametric and canonical equations. Mutual position of a line and a plane
  11. Vector spaces and their subspaces. Linear hull (span). The space R^n, Linear independence and dependence. Basis and dimension
  12. Coordinate system. Basis change. Transition matrix, Inner product. Inner product space. Orthogonal basis. Fourier coefficient. Orthogonal projection. Normed vector space
  13. Linear operators and their matrix representation. Basis change. Similar matrices, Examples of linear operators in V^2 and V^3. Linear functional. Hyperspace, half-space
  14. Eigenvalues and eigenvectors. Eigenspaces. Characteristic polynomial. Hamilton-Cayley's theorem, Schur's theorem. Matrix functions. Spectral mapping theorem
  15. Final exam

Study Programmes

University undergraduate
Electrical Engineering and Information Technology and Computing (study)
(1. semester)

Literature

Neven Elezović (2016.), Linearna algebra, Element, Zagreb
N. Elezović, A. Aglić Aljinović (2006.), Linearna algebra: zbirka zadataka, Element, Zagreb
Gilbert Strang (2006.), Linear Algebra and its Applications, Brooks Cole
Damir Bakić (2008.), Linearna algebra, Školska knjiga, Zagreb
David S. Watkins (2002.), Fundamentals of Matrix Computations, Wiley

Associate Lecturers

Exercises

General

ID 183355
  Winter semester
5 ECTS
L1 English Level
L2 e-Learning
60 Lectures
15 Exercises
0 Laboratory exercises
0 Project laboratory

Grading System

85 Excellent
70 Very Good
55 Good
45 Acceptable