Separation axioms | Properties of topological spaces
In mathematics, a topological space is called collectionwise normal if for every discrete family Fi (i ∈ I) of closed subsets of there exists a pairwise disjoint family of open sets Ui (i ∈ I), such that Fi ⊆ Ui. A family of subsets of is called discrete when every point of has a neighbourhood that intersects at most one of the sets from .An equivalent definition of collectionwise normal demands that the above Ui (i ∈ I) are themselves a discrete family, which is stronger than pairwise disjoint. Some authors assume that is also a T1 space as part of the definition. The property is intermediate in strength between paracompactness and normality, and occurs in metrization theorems. (Wikipedia).
This video explains the definition of a vector space and provides examples of vector spaces.
From playlist Vector Spaces
We have already looked at the column view of a matrix. In this video lecture I want to expand on this topic to show you that each matrix has a column space. If a matrix is part of a linear system then a linear combination of the columns creates a column space. The vector created by the
From playlist Introducing linear algebra
From playlist Unlisted LA Videos
The Largest and Smallest Values for the Rank and Nullity of a Matrix (5 x 3)
This video explains how to determine the largest and smallest possible values for the rank and nullity of a 5 by 3 matrix.
From playlist Column and Null Space
The Largest and Smallest Values for the Rank and Nullity of a Matrix (3 x 5)
This video explains how to determine the largest and smallest possible values for the rank and nullity of a 3 by 5 matrix.
From playlist Column and Null Space
Null Space: Is a Vector in a Null Space? Find a Basis for a Null Space
This video explains how to determine if a vector is in a null space and how to find a basis for a null space.
From playlist Column and Null Space
After our introduction to matrices and vectors and our first deeper dive into matrices, it is time for us to start the deeper dive into vectors. Vector spaces can be vectors, matrices, and even function. In this video I talk about vector spaces, subspaces, and the porperties of vector sp
From playlist Introducing linear algebra
From playlist Unlisted LA Videos
Column Space: Is a Vector in a Column Space? Find a Basis for a Column Space
This video explains how to determine if a vector is in a null space and how to find a basis for a null space.
From playlist Column and Null Space
Code samples derived from work by Joey de Vries, @joeydevries, author of https://learnopengl.com/ All code samples, unless explicitly stated otherwise, are licensed under the terms of the CC BY-NC 4.0 license as published by Creative Commons, either version 4 of the License, or (at your o
From playlist OpenGL
Tensor Calculus Lecture 8b: The Surface Derivative of the Normal
This course will eventually continue on Patreon at http://bit.ly/PavelPatreon Textbook: http://bit.ly/ITCYTNew Errata: http://bit.ly/ITAErrata McConnell's classic: http://bit.ly/MCTensors Table of Contents of http://bit.ly/ITCYTNew Rules of the Game Coordinate Systems and the Role of Te
From playlist Introduction to Tensor Calculus
Alex Margolis: Quasi-actions and almost normal subgroups
CIRM VIRTUAL EVENT Recorded during the meeting"Virtual Geometric Group Theory conference " the May 27, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM
From playlist Virtual Conference
Alex Margolis: Quasi-actions and almost normal subgroups
CIRM VIRTUAL EVENT Recorded during the meeting"Virtual Geometric Group Theory conference " the May 27, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM
From playlist VIRTUAL EVENT GEOMETRIC GROUP THEORY CONFERENCE
Tensor Calculus Lecture 14a: Non-hypersurfaces
This course will eventually continue on Patreon at http://bit.ly/PavelPatreon Textbook: http://bit.ly/ITCYTNew Errata: http://bit.ly/ITAErrata McConnell's classic: http://bit.ly/MCTensors Table of Contents of http://bit.ly/ITCYTNew Rules of the Game Coordinate Systems and the Role of Te
From playlist Introduction to Tensor Calculus
Transversality and super-rigidity in Gromov-Witten Theory (Lecture - 03) by Chris Wendl
J-Holomorphic Curves and Gromov-Witten Invariants DATE:25 December 2017 to 04 January 2018 VENUE:Madhava Lecture Hall, ICTS, Bangalore Holomorphic curves are a central object of study in complex algebraic geometry. Such curves are meaningful even when the target has an almost complex stru
From playlist J-Holomorphic Curves and Gromov-Witten Invariants
Thomas Baumgarte (2) - Numerical relativity: Mathematical formulation
PROGRAM: NUMERICAL RELATIVITY DATES: Monday 10 Jun, 2013 - Friday 05 Jul, 2013 VENUE: ICTS-TIFR, IISc Campus, Bangalore DETAL Numerical relativity deals with solving Einstein's field equations using supercomputers. Numerical relativity is an essential tool for the accurate modeling of a wi
From playlist Numerical Relativity
Introduction to a Unified Model of Cellular Automata
This is an introduction to a unified model of Cellular Automata in which a rule is represented not by a single function but by a vector of functions we call genes. These functions can be ordered so that they maintain the same order regardless of the rule space where they are realized. This
From playlist Wolfram Technology Conference 2022
PMSP - Random-like behavior in deterministic systems - Benjamin Weiss
Benjamin Weiss Einstein Institute of Math, Hebrew University June 16, 2010 For more videos, visit http://video.ias.edu
From playlist Mathematics
7 juillet 2015
From playlist 2015 Summer School on Moduli Problems in Symplectic Geometry
Computing Dimension of Null Space & Column Space
The dimension of a subspace is the number of basis vectors. For the two canonical subspaces associated to any matrix - the Null Space and the Column Space - we repeat quickly the computation of basis vectors for them and thus are able to compute their dimensions. *************************
From playlist Linear Algebra (Full Course)