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Textbooks / Math / Mathematical Structures for Computer Science 7

# Mathematical Structures for Computer Science 7th Edition - Solutions by Chapter

## Full solutions for Mathematical Structures for Computer Science | 7th Edition

ISBN: 9781429215107

Mathematical Structures for Computer Science | 7th Edition - Solutions by Chapter

Solutions by Chapter
4 5 0 345 Reviews
##### ISBN: 9781429215107

The full step-by-step solution to problem in Mathematical Structures for Computer Science were answered by , our top Math solution expert on 01/18/18, 05:04PM. Since problems from 41 chapters in Mathematical Structures for Computer Science have been answered, more than 37248 students have viewed full step-by-step answer. Mathematical Structures for Computer Science was written by and is associated to the ISBN: 9781429215107. This expansive textbook survival guide covers the following chapters: 41. This textbook survival guide was created for the textbook: Mathematical Structures for Computer Science, edition: 7.

Key Math Terms and definitions covered in this textbook
• Characteristic equation det(A - AI) = O.

The n roots are the eigenvalues of A.

• Complex conjugate

z = a - ib for any complex number z = a + ib. Then zz = Iz12.

• Cramer's Rule for Ax = b.

B j has b replacing column j of A; x j = det B j I det A

• Exponential eAt = I + At + (At)2 12! + ...

has derivative AeAt; eAt u(O) solves u' = Au.

• Four Fundamental Subspaces C (A), N (A), C (AT), N (AT).

Use AT for complex A.

• Fundamental Theorem.

The nullspace N (A) and row space C (AT) are orthogonal complements in Rn(perpendicular from Ax = 0 with dimensions rand n - r). Applied to AT, the column space C(A) is the orthogonal complement of N(AT) in Rm.

• Gram-Schmidt orthogonalization A = QR.

Independent columns in A, orthonormal columns in Q. Each column q j of Q is a combination of the first j columns of A (and conversely, so R is upper triangular). Convention: diag(R) > o.

• Indefinite matrix.

A symmetric matrix with eigenvalues of both signs (+ and - ).

• Inverse matrix A-I.

Square matrix with A-I A = I and AA-l = I. No inverse if det A = 0 and rank(A) < n and Ax = 0 for a nonzero vector x. The inverses of AB and AT are B-1 A-I and (A-I)T. Cofactor formula (A-l)ij = Cji! detA.

• Least squares solution X.

The vector x that minimizes the error lie 112 solves AT Ax = ATb. Then e = b - Ax is orthogonal to all columns of A.

• Left nullspace N (AT).

Nullspace of AT = "left nullspace" of A because y T A = OT.

• Linear combination cv + d w or L C jV j.

• Multiplier eij.

The pivot row j is multiplied by eij and subtracted from row i to eliminate the i, j entry: eij = (entry to eliminate) / (jth pivot).

• Orthogonal subspaces.

Every v in V is orthogonal to every w in W.

• Right inverse A+.

If A has full row rank m, then A+ = AT(AAT)-l has AA+ = 1m.

• Saddle point of I(x}, ... ,xn ).

A point where the first derivatives of I are zero and the second derivative matrix (a2 II aXi ax j = Hessian matrix) is indefinite.

• Schur complement S, D - C A -} B.

Appears in block elimination on [~ g ].

• Singular matrix A.

A square matrix that has no inverse: det(A) = o.

• Symmetric factorizations A = LDLT and A = QAQT.

Signs in A = signs in D.

• Toeplitz matrix.

Constant down each diagonal = time-invariant (shift-invariant) filter.