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Linear Algebra Final
UAHuntsville Linear Algebara - Nazir Abudiab
| Question | Answer |
|---|---|
| Matrix that is both upper and lower triangular | Diagonal Matrix |
| When one matrix is obtained from the other by elementary column operations they are | Column-equivalent matrices |
| Operations performed on the column of a matrix | Elementary Column Operations |
| Set of all polynomials of degree ≤ n | P_n |
| Set of all continuous functions defined on the real number line. | C(-inf,inf) |
| Simplest subspace of a vector space V that is consisting of only the zero vector W = {0} | Zero subspace |
| Defined as v = C1u1+C2u2+C3u3+...+Cnun where C1,C2,C3,Cn are scalars. | Linear Combination of Vectors |
| Subset of a Vector space such as V where every vector in V can be written as a linear combination of the vector found in the subset. | Spanning Set |
| Vector space that has a basis consisting of a finite number of vector. | Finite Dimensional Vector |
| Number of vectors found in the set which makes up the basis of a vector space | Vector Space Dimension |
| (T/F) A vector space consists of four entities: a set of vectors, a set of scalars, and two operations. | True |
| (T/F) If W is a subspace of R^2, then W must contain the vector (0,0). | True |
| (T/F) Every vector space V contains two proper subspaces that are the zero subspace and itself. | False |
| (T/F) If a subset S spans a vector space V, then every vector in V can be written as a linear combination of the vector in S. | True |
| (T/F) If dim(V) = n, then there exists a set of n-1 vectors in V that will span V. | False |
| (T/F) If dim(V) = n, then any set of n+1 vectors must be linearly dependent. | True |
| (T/F) If A is an invertible n x n matrix, then Ax=b, has a unique solution for every b. | True |
| (T/F) If the determinant of n x n matrix A is nonzero, then Ax=0 has only the trivial solution. | True |
| (T/F) If two rows of a square matrix are equal then the determinant of the matrix is not zero. | False |
| (T/F) The determinant of a matrix order 1 is the entry of the matrix. | True |
| has the form a1x1+a2x2+a3x3+...+anxn = b, where the coefficients a1,a2,a3,...,an are real numbers, and the constant term b is a real number. | Linear Equation in n variables |
| is a set of m equations, each of which is linear in the same n variables. | System of Linear Equations |
| is a system of linear equations that has at least one solution. | Consistent System |
| is a system of linear equations that have no solution. | Inconsistent System |
| is a process of rewriting a system of linear equations in row-echelon form. | Gaussian Elimination |
| is a matrix derived from the coefficients and constant terms of a system of linear equations. | Augmented Matrix |
| are matrices where one is derived from the other by a finite sequence of elementary row operations. | Row equivalent matrices |
| is a rectangular array in which each entry, a_ij, is a number. Its size is determined by the number of its columns and rows. | Matrix |
| is the Zero matrix 0_mn | Additive inverse matrix |
| is a matrix that satisfies the condition A = A^T | Symmetric Matrix |
| is an n x n matrix when there exists matrix B whose size is n x n such that AB = BA = I_n. | Nonsingular Matrix |
| is the process of writing the n x 2n matrix that consists of a given matrix A on the left and the n x n identity matrix I on the right to obtain [A,I]. | Adjoining Matrix I to A |
| is an n x n matrix which is obtained from the identity matrix I_n | Elementary Matrix |
| is the process of writing a squared matrix A as the product of a lower triangular matrix L and an upper triangular matrix U. | LU Factorization |
| is a squared matrix in which all the entries below the main diagonal are zero. | Upper Triangular Matrix |
| (T/F) The Identity Matrix is an Elementary Matrix | True |
| (T/F) The inverse of an Elementary matrix is an elementary matrix. | True |
| (T/F) The Zero Matrix is an elementary matrix. | True |
| (T/F) If matrix A can be reduced to an identity matrix, then A is nonsingular matrix | True |
| (T/F) If A is a square matrix, then the system of linear equations Ax=b has a unique solution. | False |
| (T/F) Every matrix has an Additive Inverse | True |
| (T/F) Matrix Multiplication is commutative | False |
| (T/F) A 4 x 7 matrix has 4 columns | False |
| (T/F) A homogeneous system of four linear equations in four variables is always consistent. | True |
| (T/F) The product of a 2 x 3 matrix and a 3 x 5 matrix is a 5 x 2 matrix. | False |
| If A is an nxn matrix with eigenvalue lambda, then the set of all eigenvectors of lambda, together with the zero vector is a subspace R^n. The subspace is called ______ of lambda. | Eigenspace |
| The Equation |lambdaI-A|=0 is called the | Characteristic Equation |
| Matrices that are similar diagonal matrices are called | Diagonalizable |
| A vector Space V with an inner product is called | Inner product space. |
| The Dot Product is also called the | Euclidean Inner Product. |
| A procedure in which you are given the coordinates of a vector relative to basis B and you are asked to find another basis B' | Change of Basis |
| Given [x]B = P[x]B', the matrix P is called | Transition Matrix from B to B' |
| The dimension of the nullspace of a matrix A of size m x n is called | Nullity |
| Let B = {c1,v2,v3,...,vn} be an ordered basis for a vector space V and let x be a vector in V such that x = c1v1+c2v2+c3v3+...+cnvn, the scalars c,c2,c3,cb are called | Coordinates of x relative to basis |
| If A is an m x n matrix, then the set of all solutions of the homogeneous system of linear equations Ax=0 is a subspace of R^n called | Nullspace |
| (T/F) The column space of matrix A is equal to the row space of matrix A^T | True |
| (T/F) If a is an mxn matrix of rank r then the dimension of the solution space of Ax=0 is m-r | False |
| (T/F) To perform the change of basis from nonstandard basis B' to the standard basis B the transition matrix P^-1 is simply B' | True |
| (T/F) Elementary row operations preserve the column space of the matrix A. | False |
| (T/F) If u*v< 0 the angle is acute | False |
| (T/F) Geometrically if lambda is an eigenvalue of a matrix A and x is an eigenvector of A corresponding to lambda, then multiplying x by A produces a vector labmda(x) parallel to x. | True |
| (T/F) If A is an nxn matrix with eigenvalue lambda then the set of all eigenvectors lambda is a subspace of R^n | False |
| (T/F) If A and B are nxn similar matrices, then they always have the same characteristic polynomial equation. | True |
| (T/F) The fat that an nxn matrix A has n distinct eigenvalues does not guarantee that A is diagonalizable. | False |
| (T/F) If A is diagonalizable, then it has n linearly independent eigenvectors. | True |
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evinsmc
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