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Textbooks / Math / Enhanced Web Assign Precalculus and College Algebra

Enhanced Web Assign Precalculus and College Algebra Solutions

Do I need to buy Enhanced Web Assign Precalculus and College Algebra to pass the class?

ISBN: 9781285858333

Enhanced Web Assign Precalculus and College Algebra - Solutions by Chapter

Do I need to buy this book?
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79% of students who have bought this book said that they did not need the hard copy to pass the class. Were they right? Add what you think:

Enhanced Web Assign Precalculus and College Algebra Student Assesment

Rosy from Arizona School of Acupuncture and Oriental Medicine said

"If I knew then what I knew now I would not have bought the book. It was over priced and My professor only used it a few times."

Textbook: Enhanced Web Assign Precalculus and College Algebra
Edition:
Author: CENGAGE Learning (Author)
ISBN: 9781285858333

Since problems from 0 chapters in Enhanced Web Assign Precalculus and College Algebra have been answered, more than 200 students have viewed full step-by-step answer. This textbook survival guide was created for the textbook: Enhanced Web Assign Precalculus and College Algebra, edition: . The full step-by-step solution to problem in Enhanced Web Assign Precalculus and College Algebra were answered by , our top Math solution expert on 10/03/18, 03:08PM. This expansive textbook survival guide covers the following chapters: 0. Enhanced Web Assign Precalculus and College Algebra was written by and is associated to the ISBN: 9781285858333.

Key Math Terms and definitions covered in this textbook
  • Adjacency matrix of a graph.

    Square matrix with aij = 1 when there is an edge from node i to node j; otherwise aij = O. A = AT when edges go both ways (undirected). Adjacency matrix of a graph. Square matrix with aij = 1 when there is an edge from node i to node j; otherwise aij = O. A = AT when edges go both ways (undirected).

  • Basis for V.

    Independent vectors VI, ... , v d whose linear combinations give each vector in V as v = CIVI + ... + CdVd. V has many bases, each basis gives unique c's. A vector space has many bases!

  • Cayley-Hamilton Theorem.

    peA) = det(A - AI) has peA) = zero matrix.

  • Complex conjugate

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

  • Elimination matrix = Elementary matrix Eij.

    The identity matrix with an extra -eij in the i, j entry (i #- j). Then Eij A subtracts eij times row j of A from row i.

  • Fast Fourier Transform (FFT).

    A factorization of the Fourier matrix Fn into e = log2 n matrices Si times a permutation. Each Si needs only nl2 multiplications, so Fnx and Fn-1c can be computed with ne/2 multiplications. Revolutionary.

  • Free variable Xi.

    Column i has no pivot in elimination. We can give the n - r free variables any values, then Ax = b determines the r pivot variables (if solvable!).

  • Gauss-Jordan method.

    Invert A by row operations on [A I] to reach [I A-I].

  • Hypercube matrix pl.

    Row n + 1 counts corners, edges, faces, ... of a cube in Rn.

  • Identity matrix I (or In).

    Diagonal entries = 1, off-diagonal entries = 0.

  • Jordan form 1 = M- 1 AM.

    If A has s independent eigenvectors, its "generalized" eigenvector matrix M gives 1 = diag(lt, ... , 1s). The block his Akh +Nk where Nk has 1 's on diagonall. Each block has one eigenvalue Ak and one eigenvector.

  • Left nullspace N (AT).

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

  • Orthogonal subspaces.

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

  • Outer product uv T

    = column times row = rank one matrix.

  • Pascal matrix

    Ps = pascal(n) = the symmetric matrix with binomial entries (i1~;2). Ps = PL Pu all contain Pascal's triangle with det = 1 (see Pascal in the index).

  • Schwarz inequality

    Iv·wl < IIvll IIwll.Then IvTAwl2 < (vT Av)(wT Aw) for pos def A.

  • Simplex method for linear programming.

    The minimum cost vector x * is found by moving from comer to lower cost comer along the edges of the feasible set (where the constraints Ax = b and x > 0 are satisfied). Minimum cost at a comer!

  • Skew-symmetric matrix K.

    The transpose is -K, since Kij = -Kji. Eigenvalues are pure imaginary, eigenvectors are orthogonal, eKt is an orthogonal matrix.

  • Subspace S of V.

    Any vector space inside V, including V and Z = {zero vector only}.

  • Vector space V.

    Set of vectors such that all combinations cv + d w remain within V. Eight required rules are given in Section 3.1 for scalars c, d and vectors v, w.

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