Try the Free Math Solver or Scroll down to Tutorials!

 Depdendent Variable

 Number of equations to solve: 23456789
 Equ. #1:
 Equ. #2:

 Equ. #3:

 Equ. #4:

 Equ. #5:

 Equ. #6:

 Equ. #7:

 Equ. #8:

 Equ. #9:

 Solve for:

 Dependent Variable

 Number of inequalities to solve: 23456789
 Ineq. #1:
 Ineq. #2:

 Ineq. #3:

 Ineq. #4:

 Ineq. #5:

 Ineq. #6:

 Ineq. #7:

 Ineq. #8:

 Ineq. #9:

 Solve for:

 Please use this form if you would like to have this math solver on your website, free of charge. Name: Email: Your Website: Msg:

# Math 2700 Key Concepts

• systems of linear equations and writing them in matrix form
• augmented matrix
• Elementary Row Operations
• Existence and Uniqueness Questions
• Row Reduction, Echelon, and Reduced Echelon form
• Pivot positons
• Vector notation and equations
• Homogeneous and Inhomogeneous equations and systems
• Linear combinations and span
• Linear independence
• Connections between span, linear independence, existence questions, uniqueness questions, and pivots
• Linear transformations
– Testing if a transformation is linear
– writing a linear transformation as a matrix
– basic geometric examples: rotation, dilation, shear
– one-to-one (also called injective) and connections to linear independence and pivots
– onto (also called surjective) and connections to span of the columns and pivots
– invertible = one-to-one and onto (also called bijective)
– kernel
– range or image

• matrix addition, multiplication, and transpose
• How to invert a matrix
– short-cut for 2 × 2
– general procedure for 3 × 3 and larger

• Invertible Matrix Theorem
• null space
• column space
• determinants
– calculating by expanding by cofactors
– calculating by row operations
– Cramer’s Rule
– Connections between determinant of a linear transformation and volume
• Vector spaces and subspaces: definition and how to decide if a set is a vector space or subspace
• bases and dimension
• rank
• relationship between rank, dimension of null space, dimension of column space, and number of columns
• change of basis and coordinates
• Eigenvalues and eigenvectors
– characteristic polynomial
– imaginary eigenvalues
– complex eigenvalues
– Using eigenvalues and eigenvectors to help understand a linear transformation
• Connections between eigenvalues, eigenvectors, and differential equations as in the Romeo and Juliet examples
• Diagonalization and its relationship to eigenvalues and eigenvectors
• Dot products
– angle between vectors
– length of vectors
– orthogonal
• orthogonal projections
• orthonormal bases and Gram-Schmidt orthogonalization
• least squares problems