## 69 linearalgebra

### 69.1 Introduction to linearalgebra

`linearalgebra` is a collection of functions for linear algebra.

Example:

    (%i1) M : matrix ([1, 2], [1, 2]);
                                [ 1  2 ]
    (%o1)                       [      ]
                                [ 1  2 ]
    (%i2) nullspace (M);
                                   [  1  ]
                                   [     ]
    (%o2)                     span([   1 ])
                                   [ - - ]
                                   [   2 ]
    (%i3) columnspace (M);
                                    [ 1 ]
    (%o3)                      span([   ])
                                    [ 1 ]
    (%i4) ptriangularize (M - z*ident(2), z);
                             [ 1   2 - z   ]
    (%o4)                    [             ]
                             [           2 ]
                             [ 0  3 z - z  ]
    (%i5) M : matrix ([1, 2, 3], [4, 5, 6], [7, 8, 9]) - z*ident(3);
                         [ 1 - z    2      3   ]
                         [                     ]
    (%o5)                [   4    5 - z    6   ]
                         [                     ]
                         [   7      8    9 - z ]
    (%i6) MM : ptriangularize (M, z);
                  [ 4  5 - z            6            ]
                  [                                  ]
                  [                2                 ]
                  [     66        z    102 z   132   ]
                  [ 0   --      - -- + ----- + ---   ]
    (%o6)         [     49        7     49     49    ]
                  [                                  ]
                  [               3        2         ]
                  [           49 z    245 z    147 z ]
                  [ 0    0    ----- - ------ - ----- ]
                  [            264      88      44   ]
    (%i7) algebraic : true;
    (%o7)                         true
    (%i8) tellrat (MM [3, 3]);
                             3       2
    (%o8)                  [z  - 15 z  - 18 z]
    (%i9) MM : ratsimp (MM);
                   [ 4  5 - z           6           ]
                   [                                ]
                   [                2               ]
    (%o9)          [     66      7 z  - 102 z - 132 ]
                   [ 0   --    - ------------------ ]
                   [     49              49         ]
                   [                                ]
                   [ 0    0             0           ]
    (%i10) nullspace (MM);
                            [        1         ]
                            [                  ]
                            [   2              ]
                            [  z  - 14 z - 16  ]
                            [  --------------  ]
    (%o10)             span([        8         ])
                            [                  ]
                            [    2             ]
                            [   z  - 18 z - 12 ]
                            [ - -------------- ]
                            [         12       ]
    (%i11) M : matrix ([1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12],
                       [13, 14, 15, 16]);
                           [ 1   2   3   4  ]
                           [                ]
                           [ 5   6   7   8  ]
    (%o11)                 [                ]
                           [ 9   10  11  12 ]
                           [                ]
                           [ 13  14  15  16 ]
    (%i12) columnspace (M);
                               [ 1  ]  [ 2  ]
                               [    ]  [    ]
                               [ 5  ]  [ 6  ]
    (%o12)                span([    ], [    ])
                               [ 9  ]  [ 10 ]
                               [    ]  [    ]
                               [ 13 ]  [ 14 ]
    (%i13) apply ('orthogonal_complement, args (nullspace (transpose (M))));
                               [ 0 ]  [  1  ]
                               [   ]  [     ]
                               [ 1 ]  [  0  ]
    (%o13)                span([   ], [     ])
                               [ 2 ]  [ - 1 ]
                               [   ]  [     ]
                               [ 3 ]  [ - 2 ]

·

Categories: Linear algebra ·Share packages ·Package linearalgebra

### 69.2 Functions and Variables for linearalgebra

Function: addmatrices (f, M_1, …, M_n)

  Using the function f as the addition function, return the sum of the matrices M_1, …, M_n. The function f must accept any number of arguments (a Maxima nary function).

  Examples:

      (%i1) m1 : matrix([1,2],[3,4])$
      (%i2) m2 : matrix([7,8],[9,10])$
      (%i3) addmatrices('max,m1,m2);
      (%o3) matrix([7,8],[9,10])
      (%i4) addmatrices('max,m1,m2,5*m1);
      (%o4) matrix([7,10],[15,20])

  ·

  Categories: Package linearalgebra

Function: blockmatrixp (M)

  Return true if and only if M is a matrix and every entry of M is a matrix.

  ·

  Categories: Package linearalgebra ·Predicate functions

Function: columnop (M, i, j, theta)

  If M is a matrix, return the matrix that results from doing the column operation `C_i <- C_i - theta * C_j`. If M doesn’t have a row i or j, signal an error.

  ·

  Categories: Package linearalgebra

Function: columnswap (M, i, j)

  If M is a matrix, swap columns i and j. If M doesn’t have a column i or j, signal an error.

  ·

  Categories: Package linearalgebra

Function: columnspace (M)

  If M is a matrix, return `span (v_1, ..., v_n)`, where the set `{v_1, ..., v_n}` is a basis for the column space of M. The span of the empty set is `{0}`. Thus, when the column space has only one member, return `span ()`.

  ·

  Categories: Package linearalgebra

Function: cholesky
`cholesky` (M)
`cholesky` (M, field)

  Return the Cholesky factorization of the matrix selfadjoint (or hermitian) matrix M. The second argument defaults to ’generalring.’ For a description of the possible values for field, see `lu_factor`.

  ·

  Categories: Matrix decompositions ·Package linearalgebra

Function: ctranspose (M)

  Return the complex conjugate transpose of the matrix M. The function `ctranspose` uses `matrix_element_transpose` to transpose each matrix element.

  ·

  Categories: Package linearalgebra

Function: diag_matrix (d_1, d_2, …, d_n)

  Return a diagonal matrix with diagonal entries d_1, d_2, …, d_n. When the diagonal entries are matrices, the zero entries of the returned matrix are zero matrices of the appropriate size; for example:

      (%i1) diag_matrix(diag_matrix(1,2),diag_matrix(3,4));

                                  [ [ 1  0 ]  [ 0  0 ] ]
                                  [ [      ]  [      ] ]
                                  [ [ 0  2 ]  [ 0  0 ] ]
      (%o1)                       [                    ]
                                  [ [ 0  0 ]  [ 3  0 ] ]
                                  [ [      ]  [      ] ]
                                  [ [ 0  0 ]  [ 0  4 ] ]
      (%i2) diag_matrix(p,q);

                                         [ p  0 ]
      (%o2)                              [      ]
                                         [ 0  q ]

  ·

  Categories: Package linearalgebra

Function: dotproduct (u, v)

  Return the dotproduct of vectors u and v. This is the same as `conjugate (transpose (u)) . v`. The arguments u and v must be column vectors.

  ·

  Categories: Package linearalgebra

Function: eigens_by_jacobi
`eigens_by_jacobi` (A)
`eigens_by_jacobi` (A, field_type)

  Computes the eigenvalues and eigenvectors of A by the method of Jacobi rotations. A must be a symmetric matrix (but it need not be positive definite nor positive semidefinite). field_type indicates the computational field, either `floatfield` or `bigfloatfield`. If field_type is not specified, it defaults to `floatfield`.

  The elements of A must be numbers or expressions which evaluate to numbers via `float` or `bfloat` (depending on field_type).

  Examples:

      (%i1) S: matrix([1/sqrt(2), 1/sqrt(2)],[-1/sqrt(2), 1/sqrt(2)]);
                           [     1         1    ]
                           [  -------   ------- ]
                           [  sqrt(2)   sqrt(2) ]
      (%o1)                [                    ]
                           [      1        1    ]
                           [ - -------  ------- ]
                           [   sqrt(2)  sqrt(2) ]
      (%i2) L : matrix ([sqrt(3), 0], [0, sqrt(5)]);
                            [ sqrt(3)     0    ]
      (%o2)                 [                  ]
                            [    0     sqrt(5) ]
      (%i3) M : S . L . transpose (S);
                  [ sqrt(5)   sqrt(3)  sqrt(5)   sqrt(3) ]
                  [ ------- + -------  ------- - ------- ]
                  [    2         2        2         2    ]
      (%o3)       [                                      ]
                  [ sqrt(5)   sqrt(3)  sqrt(5)   sqrt(3) ]
                  [ ------- - -------  ------- + ------- ]
                  [    2         2        2         2    ]
      (%i4) eigens_by_jacobi (M);
      The largest percent change was 0.1454972243679
      The largest percent change was 0.0
      number of sweeps: 2
      number of rotations: 1
      (%o4) [[1.732050807568877, 2.23606797749979],
                              [  0.70710678118655   0.70710678118655 ]
                              [                                      ]]
                              [ - 0.70710678118655  0.70710678118655 ]
      (%i5) float ([[sqrt(3), sqrt(5)], S]);
      (%o5) [[1.732050807568877, 2.23606797749979],
                              [  0.70710678118655   0.70710678118655 ]
                              [                                      ]]
                              [ - 0.70710678118655  0.70710678118655 ]
      (%i6) eigens_by_jacobi (M, bigfloatfield);
      The largest percent change was 1.454972243679028b-1
      The largest percent change was 0.0b0
      number of sweeps: 2
      number of rotations: 1
      (%o6) [[1.732050807568877b0, 2.23606797749979b0],
                      [  7.071067811865475b-1   7.071067811865475b-1 ]
                      [                                              ]]
                      [ - 7.071067811865475b-1  7.071067811865475b-1 ]

  ·

  Categories: Matrix decompositions ·Package linearalgebra

Function: get_lu_factors (x)

  When `x = lu_factor (A)`, then `get_lu_factors` returns a list of the form `[P, L, U]`, where P is a permutation matrix, L is lower triangular with ones on the diagonal, and U is upper triangular, and `A = P L U`.

  ·

  Categories: Package linearalgebra

Function: hankel
`hankel` (col)
`hankel` (col, row)

  Return a Hankel matrix H. The first column of H is col; except for the first entry, the last row of H is row. The default for row is the zero vector with the same length as col.

  ·

  Categories: Package linearalgebra

Function: hessian (f, x)

  Returns the Hessian matrix of f with respect to the list of variables x. The `(i, j)`-th element of the Hessian matrix is `diff(f, x[i], 1, x[j], 1)`.

  Examples:

      (%i1) hessian (x * sin (y), [x, y]);
                           [   0       cos(y)   ]
      (%o1)                [                    ]
                           [ cos(y)  - x sin(y) ]
      (%i2) depends (F, [a, b]);
      (%o2)                       [F(a, b)]
      (%i3) hessian (F, [a, b]);
                              [   2      2   ]
                              [  d F    d F  ]
                              [  ---   ----- ]
                              [    2   da db ]
                              [  da          ]
      (%o3)                   [              ]
                              [   2      2   ]
                              [  d F    d F  ]
                              [ -----   ---  ]
                              [ da db     2  ]
                              [         db   ]

  ·

  Categories: Differential calculus ·Package linearalgebra

Function: hilbert_matrix (n)

  Return the n by n Hilbert matrix. When n isn’t a positive integer, signal an error.

  ·

  Categories: Package linearalgebra

Function: identfor
`identfor` (M)
`identfor` (M, fld)

  Return an identity matrix that has the same shape as the matrix M. The diagonal entries of the identity matrix are the multiplicative identity of the field fld; the default for fld is generalring.

  The first argument M should be a square matrix or a non-matrix. When M is a matrix, each entry of M can be a square matrix – thus M can be a blocked Maxima matrix. The matrix can be blocked to any (finite) depth.

  See also `zerofor`

  ·

  Categories: Package linearalgebra

Function: invert_by_lu (M, (rng generalring))

  Invert a matrix M by using the LU factorization. The LU factorization is done using the ring rng.

  ·

  Categories: Package linearalgebra

Function: jacobian (f, x)

  Returns the Jacobian matrix of the list of functions f with respect to the list of variables x. The `(i, j)`-th element of the Jacobian matrix is `diff(f[i], x[j])`.

  Examples:

      (%i1) jacobian ([sin (u - v), sin (u * v)], [u, v]);
                        [ cos(v - u)  - cos(v - u) ]
      (%o1)             [                          ]
                        [ v cos(u v)   u cos(u v)  ]
      (%i2) depends ([F, G], [y, z]);
      (%o2)                  [F(y, z), G(y, z)]
      (%i3) jacobian ([F, G], [y, z]);
                                 [ dF  dF ]
                                 [ --  -- ]
                                 [ dy  dz ]
      (%o3)                      [        ]
                                 [ dG  dG ]
                                 [ --  -- ]
                                 [ dy  dz ]

  ·

  Categories: Differential calculus ·Package linearalgebra

Function: kronecker_product (A, B)

  Return the Kronecker product of the matrices A and B.

  ·

  Categories: Package linearalgebra

Function: listp
`listp` (e, p)
`listp` (e)

  Given an optional argument p, return `true` if e is a Maxima list and p evaluates to `true` for every list element. When `listp` is not given the optional argument, return `true` if e is a Maxima list. In all other cases, return `false`.

  ·

  Categories: Package linearalgebra ·Predicate functions

Function: locate_matrix_entry (M, r_1, c_1, r_2, c_2, f, rel)

  The first argument must be a matrix; the arguments r_1 through c_2 determine a sub-matrix of M that consists of rows r_1 through r_2 and columns c_1 through c_2.

  Find a entry in the sub-matrix M that satisfies some property. Three cases:

  (1) `rel = 'bool` and f a predicate:

  Scan the sub-matrix from left to right then top to bottom, and return the index of the first entry that satisfies the predicate f. If no matrix entry satisfies f, return `false`.

  (2) `rel = 'max` and f real-valued:

  Scan the sub-matrix looking for an entry that maximizes f. Return the index of a maximizing entry.

  (3) `rel = 'min` and f real-valued:

  Scan the sub-matrix looking for an entry that minimizes f. Return the index of a minimizing entry.

  ·

  Categories: Package linearalgebra

Function: lu_backsub (M, b)

  When `M = lu_factor (A, field)`, then `lu_backsub (M, b)` solves the linear system `A x = b`.

  The n by m matrix `b`, with n the number of rows of the matrix `A`, contains one right hand side per column. If there is only one right hand side then `b` must be a n by 1 matrix.

  Each column of the matrix `x=lu_backsub (M, b)` is the solution corresponding to the respective column of `b`.

  Examples:

      (%i1) A : matrix ([1 - z, 3], [3, 8 - z]);
                                     [ 1 - z    3   ]
      (%o1)                          [              ]
                                     [   3    8 - z ]
      (%i2) M : lu_factor (A,generalring);
                     [ 1 - z          3         ]
                     [                          ]
      (%o2)         [[   3              9       ], [1, 2], generalring]
                     [ -----  (- z) - ----- + 8 ]
                     [ 1 - z          1 - z     ]
      (%i3) b : matrix([a],[c]);
                                           [ a ]
      (%o3)                                [   ]
                                           [ c ]
      (%i4) x : lu_backsub(M,b);
                                 [               3 a     ]
                                 [       3 (c - -----)   ]
                                 [              1 - z    ]
                                 [ a - ----------------- ]
                                 [               9       ]
                                 [     (- z) - ----- + 8 ]
                                 [             1 - z     ]
                                 [ --------------------- ]
      (%o4)                      [         1 - z         ]
                                 [                       ]
                                 [            3 a        ]
                                 [       c - -----       ]
                                 [           1 - z       ]
                                 [   -----------------   ]
                                 [             9         ]
                                 [   (- z) - ----- + 8   ]
                                 [           1 - z       ]
      (%i5) ratsimp(A . x - b);
                                           [ 0 ]
      (%o5)                                [   ]
                                           [ 0 ]
      (%i6) B : matrix([a,d],[c,f]);
                                         [ a  d ]
      (%o6)                              [      ]
                                         [ c  f ]
      (%i7) x : lu_backsub(M,B);
                     [               3 a                    3 d     ]
                     [       3 (c - -----)          3 (f - -----)   ]
                     [              1 - z                  1 - z    ]
                     [ a - -----------------  d - ----------------- ]
                     [               9                      9       ]
                     [     (- z) - ----- + 8      (- z) - ----- + 8 ]
                     [             1 - z                  1 - z     ]
                     [ ---------------------  --------------------- ]
      (%o7)          [         1 - z                  1 - z         ]
                     [                                              ]
                     [            3 a                    3 d        ]
                     [       c - -----              f - -----       ]
                     [           1 - z                  1 - z       ]
                     [   -----------------      -----------------   ]
                     [             9                      9         ]
                     [   (- z) - ----- + 8      (- z) - ----- + 8   ]
                     [           1 - z                  1 - z       ]
      (%i8) ratsimp(A . x - B);
                                         [ 0  0 ]
      (%o8)                              [      ]
                                         [ 0  0 ]

  ·

  Categories: Package linearalgebra

Function: lu_factor (M, field)

  Return a list of the form `[LU, perm, fld]`, or `[LU, perm, fld, lower-cnd upper-cnd]`, where

  (1) The matrix LU contains the factorization of M in a packed form. Packed form means three things: First, the rows of LU are permuted according to the list perm. If, for example, perm is the list `[3,2,1]`, the actual first row of the LU factorization is the third row of the matrix LU. Second, the lower triangular factor of m is the lower triangular part of LU with the diagonal entries replaced by all ones. Third, the upper triangular factor of M is the upper triangular part of LU.

  (2) When the field is either `floatfield` or `complexfield`, the numbers lower-cnd and upper-cnd are lower and upper bounds for the infinity norm condition number of M. For all fields, the condition number might not be estimated; for such fields, `lu_factor` returns a two item list. Both the lower and upper bounds can differ from their true values by arbitrarily large factors. (See also `mat_cond`.)

  The argument M must be a square matrix.

  The optional argument fld must be a symbol that determines a ring or field. The pre-defined fields and rings are:

  (a) `generalring` – the ring of Maxima expressions,

  (b) `floatfield` – the field of floating point numbers of the type double,

  (c) `complexfield` – the field of complex floating point numbers of the type double,

  (d) `crering` – the ring of Maxima CRE expressions,

  (e) `rationalfield` – the field of rational numbers,

  (f) `runningerror` – track the all floating point rounding errors,

  (g) `noncommutingring` – the ring of Maxima expressions where multiplication is the non-commutative dot operator.

  When the field is `floatfield`, `complexfield`, or `runningerror`, the algorithm uses partial pivoting; for all other fields, rows are switched only when needed to avoid a zero pivot.

  Floating point addition arithmetic isn’t associative, so the meaning of ’field’ differs from the mathematical definition.

  A member of the field `runningerror` is a two member Maxima list of the form `[x,n]`,where x is a floating point number and `n` is an integer. The relative difference between the ’true’ value of `x` and `x` is approximately bounded by the machine epsilon times `n`. The running error bound drops some terms that of the order the square of the machine epsilon.

  There is no user-interface for defining a new field. A user that is familiar with Common Lisp should be able to define a new field. To do this, a user must define functions for the arithmetic operations and functions for converting from the field representation to Maxima and back. Additionally, for ordered fields (where partial pivoting will be used), a user must define functions for the magnitude and for comparing field members. After that all that remains is to define a Common Lisp structure `mring`. The file `mring` has many examples.

  To compute the factorization, the first task is to convert each matrix entry to a member of the indicated field. When conversion isn’t possible, the factorization halts with an error message. Members of the field needn’t be Maxima expressions. Members of the `complexfield`, for example, are Common Lisp complex numbers. Thus after computing the factorization, the matrix entries must be converted to Maxima expressions.

  See also `get_lu_factors`.

  Examples:

      (%i1) w[i,j] := random (1.0) + %i * random (1.0);
      (%o1)          w     := random(1.) + %i random(1.)
                      i, j
      (%i2) showtime : true$
      Evaluation took 0.00 seconds (0.00 elapsed)
      (%i3) M : genmatrix (w, 100, 100)$
      Evaluation took 7.40 seconds (8.23 elapsed)
      (%i4) lu_factor (M, complexfield)$
      Evaluation took 28.71 seconds (35.00 elapsed)
      (%i5) lu_factor (M, generalring)$
      Evaluation took 109.24 seconds (152.10 elapsed)
      (%i6) showtime : false$

      (%i7) M : matrix ([1 - z, 3], [3, 8 - z]);
                              [ 1 - z    3   ]
      (%o7)                   [              ]
                              [   3    8 - z ]
      (%i8) lu_factor (M, generalring);
                [ 1 - z         3        ]
                [                        ]
      (%o8)    [[   3            9       ], [1, 2], generalring]
                [ -----  - z - ----- + 8 ]
                [ 1 - z        1 - z     ]
      (%i9) get_lu_factors (%);
                        [   1    0 ]  [ 1 - z         3        ]
              [ 1  0 ]  [          ]  [                        ]
      (%o9)  [[      ], [   3      ], [                9       ]]
              [ 0  1 ]  [ -----  1 ]  [   0    - z - ----- + 8 ]
                        [ 1 - z    ]  [              1 - z     ]
      (%i10) %[1] . %[2] . %[3];
                              [ 1 - z    3   ]
      (%o10)                  [              ]
                              [   3    8 - z ]

  ·

  Categories: Matrix decompositions ·Package linearalgebra

Function: mat_cond
`mat_cond` (M, 1)
`mat_cond` (M, inf)

  Return the p-norm matrix condition number of the matrix m. The allowed values for p are 1 and inf. This function uses the LU factorization to invert the matrix m. Thus the running time for `mat_cond` is proportional to the cube of the matrix size; `lu_factor` determines lower and upper bounds for the infinity norm condition number in time proportional to the square of the matrix size.

  ·

  Categories: Package linearalgebra

Function: mat_norm
`mat_norm` (M, 1)
`mat_norm` (M, inf)
`mat_norm` (M, frobenius)

  Return the matrix p-norm of the matrix M. The allowed values for p are 1, `inf`, and `frobenius` (the Frobenius matrix norm). The matrix M should be an unblocked matrix.

  ·

  Categories: Package linearalgebra

Function: matrixp
`matrixp` (e, p)
`matrixp` (e)

  Given an optional argument p, return `true` if e is a matrix and p evaluates to `true` for every matrix element. When `matrixp` is not given an optional argument, return `true` if `e` is a matrix. In all other cases, return `false`.

  See also `blockmatrixp`

  ·

  Categories: Package linearalgebra ·Predicate functions

Function: matrix_size (M)

  Return a two member list that gives the number of rows and columns, respectively of the matrix M.

  ·

  Categories: Package linearalgebra

Function: mat_fullunblocker (M)

  If M is a block matrix, unblock the matrix to all levels. If M is a matrix, return M; otherwise, signal an error.

  ·

  Categories: Package linearalgebra

Function: mat_trace (M)

  Return the trace of the matrix M. If M isn’t a matrix, return a noun form. When M is a block matrix, `mat_trace(M)` returns the same value as does `mat_trace(mat_unblocker(m))`.

  ·

  Categories: Package linearalgebra

Function: mat_unblocker (M)

  If M is a block matrix, unblock M one level. If M is a matrix, `mat_unblocker (M)` returns M; otherwise, signal an error.

  Thus if each entry of M is matrix, `mat_unblocker (M)` returns an unblocked matrix, but if each entry of M is a block matrix, `mat_unblocker (M)` returns a block matrix with one less level of blocking.

  If you use block matrices, most likely you’ll want to set `matrix_element_mult` to `"."` and `matrix_element_transpose` to `'transpose`. See also `mat_fullunblocker`.

  Example:

      (%i1) A : matrix ([1, 2], [3, 4]);
                                  [ 1  2 ]
      (%o1)                       [      ]
                                  [ 3  4 ]
      (%i2) B : matrix ([7, 8], [9, 10]);
                                  [ 7  8  ]
      (%o2)                       [       ]
                                  [ 9  10 ]
      (%i3) matrix ([A, B]);

                           [ [ 1  2 ]  [ 7  8  ] ]
      (%o3)                [ [      ]  [       ] ]
                           [ [ 3  4 ]  [ 9  10 ] ]

      (%i4) mat_unblocker (%);
                               [ 1  2  7  8  ]
      (%o4)                    [             ]
                               [ 3  4  9  10 ]

  ·

  Categories: Package linearalgebra

Function: nullspace (M)

  If M is a matrix, return `span (v_1, ..., v_n)`, where the set `{v_1, ..., v_n}` is a basis for the nullspace of M. The span of the empty set is `{0}`. Thus, when the nullspace has only one member, return `span ()`.

  ·

  Categories: Package linearalgebra

Function: nullity (M)

  If M is a matrix, return the dimension of the nullspace of M.

  ·

  Categories: Package linearalgebra

Function: orthogonal_complement (v_1, …, v_n)

  Return `span (u_1, ..., u_m)`, where the set `{u_1, ..., u_m}` is a basis for the orthogonal complement of the set `(v_1, ..., v_n)`.

  Each vector v_1 through v_n must be a column vector.

  ·

  Categories: Package linearalgebra

Function: polynomialp
`polynomialp` (p, L, coeffp, exponp)
`polynomialp` (p, L, coeffp)
`polynomialp` (p, L)

  Return `true` if p is a polynomial in the variables in the list L. The predicate coeffp must evaluate to `true` for each coefficient, and the predicate exponp must evaluate to `true` for all exponents of the variables in L. If you want to use a non-default value for exponp, you must supply coeffp with a value even if you want to use the default for coeffp.

  The command `polynomialp (p, L, coeffp)` is equivalent to `polynomialp (p, L, coeffp, 'nonnegintegerp)` and the command `polynomialp (p, L)` is equivalent to `polynomialp (p, L, 'constantp, 'nonnegintegerp)`.

  The polynomial needn’t be expanded:

      (%i1) polynomialp ((x + 1)*(x + 2), [x]);
      (%o1)                         true
      (%i2) polynomialp ((x + 1)*(x + 2)^a, [x]);
      (%o2)                         false

  An example using non-default values for coeffp and exponp:

      (%i1) polynomialp ((x + 1)*(x + 2)^(3/2), [x], numberp, numberp);
      (%o1)                         true
      (%i2) polynomialp ((x^(1/2) + 1)*(x + 2)^(3/2), [x], numberp,
                                                              numberp);
      (%o2)                         true

  Polynomials with two variables:

      (%i1) polynomialp (x^2 + 5*x*y + y^2, [x]);
      (%o1)                         false
      (%i2) polynomialp (x^2 + 5*x*y + y^2, [x, y]);
      (%o2)                         true

  ·

  Categories: Package linearalgebra ·Predicate functions

Function: polytocompanion (p, x)

  If p is a polynomial in x, return the companion matrix of p. For a monic polynomial p of degree n, we have `p = (-1)^n charpoly (polytocompanion (p, x))`.

  When p isn’t a polynomial in x, signal an error.

  ·

  Categories: Package linearalgebra

Function: ptriangularize (M, v)

  If M is a matrix with each entry a polynomial in v, return a matrix M2 such that

  (1) M2 is upper triangular,

  (2) `M2 = E_n ... E_1 M`, where E_1 through E_n are elementary matrices whose entries are polynomials in v,

  (3) `|det (M)| = |det (M2)|`,

  Note: This function doesn’t check that every entry is a polynomial in v.

  ·

  Categories: Package linearalgebra

Function: rowop (M, i, j, theta)

  If M is a matrix, return the matrix that results from doing the row operation `R_i <- R_i - theta * R_j`. If M doesn’t have a row i or j, signal an error.

  ·

  Categories: Package linearalgebra

Function: linalg_rank (M)

  Return the rank of the matrix M. This function is equivalent to function `rank`, but it uses a different algorithm: it finds the `columnspace` of the matrix and counts its elements, since the rank of a matrix is the dimension of its column space.

      (%i1) linalg_rank(matrix([1,2],[2,4]));
      (%o1)                           1

      (%i2) linalg_rank(matrix([1,b],[c,d]));
      (%o2)                           2

  ·

  Categories: Package linearalgebra

Function: rowswap (M, i, j)

  If M is a matrix, swap rows i and j. If M doesn’t have a row i or j, signal an error.

  ·

  Categories: Package linearalgebra

Function: toeplitz
`toeplitz` (col)
`toeplitz` (col, row)

  Return a Toeplitz matrix T. The first first column of T is col; except for the first entry, the first row of T is row. The default for row is complex conjugate of col. Example:

      (%i1)  toeplitz([1,2,3],[x,y,z]);

                                        [ 1  y  z ]
                                        [         ]
      (%o1)                             [ 2  1  y ]
                                        [         ]
                                        [ 3  2  1 ]

      (%i2)  toeplitz([1,1+%i]);

                                    [   1     1 - %I ]
      (%o2)                         [                ]
                                    [ %I + 1    1    ]

  ·

  Categories: Package linearalgebra

Function: vandermonde_matrix ([x_1, ..., x_n])

  Return a n by n matrix whose i-th row is `[1, x_i, x_i^2, ... x_i^(n-1)]`.

  ·

  Categories: Package linearalgebra

Function: zerofor
`zerofor` (M)
`zerofor` (M, fld)

  Return a zero matrix that has the same shape as the matrix M. Every entry of the zero matrix is the additive identity of the field fld; the default for fld is generalring.

  The first argument M should be a square matrix or a non-matrix. When M is a matrix, each entry of M can be a square matrix – thus M can be a blocked Maxima matrix. The matrix can be blocked to any (finite) depth.

  See also `identfor`

  ·

  Categories: Package linearalgebra

Function: zeromatrixp (M)

  If M is not a block matrix, return `true` if `is (equal (e, 0))` is true for each element e of the matrix M. If M is a block matrix, return `true` if `zeromatrixp` evaluates to `true` for each element of e.

  ·

  Categories: Package linearalgebra ·Predicate functions