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  • Linear systems of equations

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  • Basic terminology
  • Parametric form of the solution set of a system of linear equations
  • Parametric vector form of the solution set of a system of linear equations
  • Gaussian elimination as a method to solve a linear system
  • Definition of echelon form of a linear system
  • All echelon forms of a linear system have the same free variables
  • Definition of equation operations on a linear system
  • Equation operations on a linear system give an equivalent system.
  • Definition of equivalent systems of linear equations
  • The geometry of linear systems
  • Definition of consistent linear system
  • Definition of inconsistent linear system
  • Definition of homogeneous linear system of equations
  • Homogeneous linear systems are consistent.
  • Definition of nontrivial solution to a homogeneous linear system of equations
  • Definition of trivial solution to a homogeneous linear system of equations
  • A homogeneous system has a nontrivial solution if and only if it has a free variable.
  • The number of solutions to a linear system
  • Definition of basic/dependent/leading variable in a linear system
  • Definition of free/independent variable in a linear system
  • Theorem describing the vector form of sulutions to a linear system.
  • The solutions to a nonhomogeneous system are given by a particular solution plus the solutions to the homogeneous system.
  • Definition of ill-conditioned linear system
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  1. The solutions to a nonhomogeneous system are given by a particular solution plus the solutions to the homogeneous system.

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  • Equivalence theorem for nonsingular matrices: the matrix A is a change-of-basis matrix.#23670
  • Equivalence theorem for nonsingular matrices: the matrix A represents the identity map with respect to some pair of bases.#23671
  • Equivalence theorem for nonsingular matrices: the matrix A row-reduces to the identity matrix.#23656
  • Equivalence theorem for nonsingular matrices: the nullity of the matrix A is 0.#23668
  • Equivalence theorem for nonsingular matrices: the null space of the matrix A is {0}.#23667
  • Equivalence theorem for nonsingular matrices: there is a pivot position in every row of A.#23655
  • Equivalence theorem for nonsingular matrices: the rows of A are a basis for R^n (or C^n).#23652
  • Equivalence theorem for nonsingular matrices: the rows of A are linearly independent.#23650
  • Equivalence theorem for nonsingular matrices: the rows of A span R^n (or C^n).#23648
  • Equivalence theorem for nonsingular matrices: the transpose of the matrix A has an inverse.#23660
  • Equivalence theorems for injective transformations#24077
  • Equivalent matrices represent the same linear transformation with resect to appropriate bases.#23605
  • Every basis for a vector space contains the same number of elements#24008
  • Every finite dimensional vector space over R (or C) is isomorphic to R^n (or C^n) for some n.#24006
  • Every matrix has an eigenvalue over the complex numbers.#23687
  • Every matrix is row-equivalent to a matrix in reduced row echelon form.#23548
  • Every matrix is row-equivalent to only one matrix in reduced row echelon form.#23549
  • Every nilpotent matrix is similar to one with 1 on subdiagonal blocks and all other entries 0.#23582
  • Every square matrix is conjugate#23566
  • Every square matrix is similar the sum of a diagonal and a nilpotent matrix.#23585
  • Every square matrix is similar to one in Jordan form.#23639
  • Example of a linear transformation on R^2: generic#23888
  • Example of a linear transformation on R^2: projection#23891
  • Example of a linear transformation on R^2: rotation#23890
  • Example of a linear transformation on R^2: shear#23889
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  • Aspen Summer 2016 - Entanglement Matters#1213
  • Math#23168

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