Lectures related to Norme, Cauchy-Schwarz Inequality

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Explores weak derivatives in Sobolev spaces, discussing their properties and uniqueness.

Covers vector spaces, scalar products, norms, and forms of polarization in standard properties.

Explores orthogonality, scalar product, and orthonormal bases in vector spaces.

Explores orthogonality, triangle inequality, and the Pythagorean theorem in vector spaces.

Explores curves in the oriented plane, discussing orientation, vector spaces, equivalence relations, and curvature of regular curves.

Covers the standard properties of distance and norm in vector spaces.

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

Covers the concept of 3D vector space, scalar product, bases, orthogonality, and projections.

Covers vector spaces, topology, and proof methods like the pigeonhole principle in R^n.

Explores pre-Hilbertian spaces, focusing on dot product properties and algebraic bases.

Covers essential concepts of vectors, norms, and their properties in linear algebra.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Covers the representation of signals in vector spaces and inner product spaces, including the Projection Theorem.

Explores equivalence in vector spaces, covering conditions for statements to be considered equivalent and properties of algebraic bases.

Covers the properties of Euclidean spaces, focusing on R^n and its applications in analysis.

Explores the properties and applications of the dot product in vector spaces.