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Analitička geometrija i linearna algebra. Bodovna vrijednost (ECTS) Elezović, N.: Linearna algebra, Element, Zagreb (više izdanja). desetak. Elezović, N. Check whole offer from author NEVEN ELEZOVIĆ. cart add to wishlist. LINEARNA ALGEBRA – ZBIRKA ZADATAKA – 3. izdanje – neven elezović, andrea aglić. Elezović, Neven. Overview . Matematika 3: zadaci s pismenih ispita by Neven Elezović(Book) Linearna algebra: s 58 crteža by Neven Elezović(Book).

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Login Hrvatski hr English. Linear Algebra Learning Outcomes describe and apply linear algebra basic concepts and methods demonstrate fundamental skills of matrix calculus and solving linear systems of equations apply fundamental knowledge of vector analysis and space analytic geometry demonstrate basic knowledge of vector spaces and linear operators demonstrate an ability to express mathematical ideas and abstract thinking in linear algebra demonstrate an ability to basic problem solving and reaching lunearna in linear algebra 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.

Linearna algebra: zbirka zadataka

Regular attendance of lectures and doing homework are requirement for taking exam. Week by Wlezovi Schedule Matrices. Regular and singular matrices Determinants. Basic properties, computation of determinants.

Elezović, Neven

Laplace’s rule, Elementary transformations, rank of a matrix, linear independence and rank. Characterization of regular matrices Linerana transformations, rank of a matrix, linear independence and rank.

Characterization of regular matrices, Characterization of regular matrices using determinant. Computation of the inverse matrix Gaussian elimination method. Homogeneous and nonhomogeneous systems, Rank of a system and rank of extended matrix Rank of a system and rank of extended matrix, Cramer’s rule.

Comparison of computational complexity of the Gauss and Cramer algorithms Operations with vectors. Vectors in coordinate systems, Dot product. Vector and scalar projections of one vector on another 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.


ic – Linearna Algebra – Free Download PDF

Convex combinations of vectors. Convex set Midterm exam Equations of plane. Mutual position of two planes.

Distance between a point and a plane Line, parametric and canonical equations. Mutual position of a lineara and a plane Vector spaces and their subspaces. Basis and dimension Coordinate system. Transition matrix, Inner product. Normed vector space Linear operators and their matrix representation. Hyperspace, half-space Eigenvalues and eigenvectors. Hamilton-Cayley’s theorem, Schur’s theorem.

Spectral mapping theorem Final exam. Lecturers in Charge Prof. Grading System ID Linear Algebra Toggle navigation Linear Algebra.

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