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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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  • The solutions to a homogeneous linear differential equation is a vector space.#24102
  • The solutions to a homogeneous system of linear equations is a vector space.#24101
  • The solutions to a nonhomogeneous system are given by a particular solution plus the solutions to the homogeneous system.#23445
  • The span of a set of vectors is a subspace#23977
  • The spectral theorem for symmetric matrices#23575
  • The standard inner product of a vector with itself is 0 only for the 0 vector#23903
  • The standard inner product of a vector with itself is non-negative#23902
  • The standard inner product on C^n can be written as the product of a vector and the adjoint of a vector.#23905
  • The standard inner product on C^n commutes/anticommutes with scalar multiplication.#23900
  • The standard inner product on C^n is anticommutative.#23898
  • The standard inner product on R^n can be written as the product of a vector and the transpose of a vector.#23904
  • The standard inner product on R^n commutes with (real) scalar multiplication.#23899
  • The standard inner product on R^n is commutative.#23897
  • The standard inner product on R^n (or C^n) distributes over addition.#23901
  • The standard/natural basis of R^n (or C^n) is a basis.#23831
  • The sum of linear transformations is a linear transformation#24016
  • The sum of subspaces is a subspace#23969
  • The the image of a spanning set is a spanning set for the range space#24037
  • The transpose of a product of matrices is the product of the transposes in reverse order.#23512
  • The transpose of a sum of matrices is the sum of the transposes.#23510
  • The triangle inequality#23912
  • The union of bases from independent subspaces is a basis for the space.#23974
  • TODO: Linear Algebra Content Progress#29496
  • Transpose and adjoint#23502
  • Transpose commutes with scalar multiplication.#23511
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