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For #matrices , the max number of linearly independent rows (i.e., row rank) equals the max number of linearly independent columns (i.e., column rank) and is called the #rankofamatrix. But why are these two numbers always the same? We give a proof.#linearalgebra Subscribe @Shahriari for more undergraduate math videos. 00:00 Introduction 00:17 Statement of the Problem: row rank = column rank 01:11 Example of row rank and column rank 02:35 Outline of the Proof 05:06 Review: Row & column space, row & column rank 08:35 Review: Elementary Row Operations and Row & Column space 12:05 Basis for row & column spaces of rref(A) 15:03 Proof that row rank = column rank for all matrices This is a video in a series of lectures on linear algebra. The series is a rigorous treatment meant for students with no prior exposure to linear algebra. In this full undergraduate course in linear algebra, general vector spaces and linear transformations are emphasized. This is also a lecture in a series of lectures on Matrices and their properties. Matrices play an important role in Linear Algebra. We use them in the study of vector spaces and, in return, the theory of vector spaces is used to understand matrices. For an annotated list of available Linear Algebra videos see https://pomona.box.com/s/oahafa797tj5... Linear Algebra YouTube Playlist: • Linear Algebra, a Retro introduction Shahriar Shahriari is the William Polk Russell Professor of Mathematics at Pomona College in Claremont, CA USA Shahriari is a 2015 winner of the Mathematical Association of America's Haimo Award for Distinguished Teaching of Mathematics, and five time winner of Pomona College's Wig teaching award.