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Matrix functions

9.1 Calculation of the transposed matrix

To compute the transpose of the matrix $A$ must run transpose(A) or $\mathbf {A \widehat { } {T } }$.

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9.2 The calculation of adjoint and inverse matrices

The calculation of inverse matrix

To calculate the inverse matrix for the matrix A, to execute inverse(A) or $\mathbf{A\widehat{ } {(-1)}}$.

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Calculation of adjoint matrix

To calculate the adjoint matrix for a given matrix $A$ execute adjoint(A) or $\mathbf{A\widehat{ }{\backslash star}}$.

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9.3 Calculation of the matrix determinant

To calculate the determinant of $A$, you must run det(A).

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9.4 Calculation of the conjugate matrix

To calculate the conjugate matrix, you must run conjugate(A) or $\mathbf {A\widehat{ } { \backslash ast}}$.

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9.5 Calculation of the generalized inverse matrix

To compute the generalized inverse Murr-Penrose matrix must run genInverse(A) or $\mathbf{A\widehat{ } {+}}$.

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9.6 Computation of the kernel and echelon form

Computation of the echelon form

To compute the echelon form of the matrix $A$, you should run toEchelonForm(A).

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Computation of the kernel

To calculate the kernel of matrix $A$, you should run kernel(A).

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9.7 Calculating the characteristic polynomial of matrix

To calculate the characteristic polynomial of the matrix A with entries in $R[x_1,…,x_m]$, you should give the ring $R[x_1,…,x_m]R[t]$ or $R[t,x_1,…,x_m]$ with some new variable $t$ and run charPolynom(A).

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9.8 Calculating LDU-decomposition of the matrix

To calculate the LDU-decomposition of the matrix A, you must run LDU(A).

The result is a vector of three matrices $[L,D,U]$. Where $L$ is a lower triangular matrix, $U$ — upper triangular matrix, $D$ — permutation matrix, multiplied by the inverse of the diagonal matrix. If the elements of the matrix A are elements of commutative domain R, then elements of matrices $L$, $D^{-1}$, $U$ are elements of the same domain R.

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9.9 Calculating Bruhat decomposition of the matrix

To calculate the Bruhat decomposition of the matrix A, you must run BruhatDecomposition(A).

The result is a vector of three matrices $[V,D,U]$. Where $V$ and $U$ — upper triangular matrices, $D$ — permutation matrix, multiplied by the inverse of the diagonal matrix. If the elements of the matrix $A$ are elements of commutative domain $R$, then elements of matrices $V$, $D^{-1}$, $U$ are elements of the same domain $R$.

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9.10 Linear programming

Let there be given the objective function $\sum_{j = 1}^n c_j x_j$ and conditions $$\sum_{j = 1}^n a_{ij}x_j\leqslant b_i,\text{ here }i = 1,2,…,m,$$ $$x_j\geqslant 0,\text{ here }j = 1,2,…,n.$$

We define $m\times n$-matrix $A = (a_{ij})$, $m$-dimensional vector $b = (b_i)$, $n$-dimensional vector $c = (c_j)$ and $n$-dimensional vector $x = (x_j)$.

Then the objective function can be written as $c^Tx,$ and and conditions can be written as $$Ax \leqslant b,$$ $$x \geqslant 0.$$

For solving linear programming problems, you can use one of the following two commands SimplexMax or SimplexMin. The result is a vector.

Depending on the type of problem you have the following options.

1. To solve the problem $$c^Tx \rightarrow max$$ under conditions $$Ax \leqslant b,$$ $$x \geqslant 0,$$ we use the SimplexMax(A, b, c).

If the objective function needs to be minimized, , i.e. $$c^Tx \rightarrow min,$$ then we use the SimplexMin(A, b, c).

Example.

We need to maximize the $$3x_1 + x_2 + 2x_3$$ under the conditions $$x_1 + x_2 + 3x_3 \leqslant 30, 2x_1 + 2x_2 + 5x_3 \leqslant 24, 4x_1 + x_2 + 2x_3 \leqslant 36, x_1, x_2, x_3 \geqslant 0.$$

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2. To solve the problem $$c^Tx \rightarrow max$$ under the conditions $$A_1 x\leqslant b_1,$$ $$A_2 x= b_2,$$ $$x \geqslant 0,$$ we use the SimplexMax(A_1,A_2, b_1, b_2, c).

If the objective function needs to be minimized, i.e. $$c^Tx \rightarrow min,$$ then we use the SimplexMin(A_1,A_2, b_1, b_2, c).

Example.

We need to maximize the $$7x_1 + x_3 - 4x_4$$

under the conditions $$x_1 - x_2 + 2x_3 - x_4 \leqslant 6, 2x_1 + x_2 - x_3 = -1, x_1, x_2, x_3, x_4 \geqslant 0.$$

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3. To solve the problem $$c^Tx \rightarrow max$$ under the conditions $$A_1 x\leqslant b_1,$$ $$A_2 x= b_2,$$ $$A_3 x\geqslant b_3,$$ we use the SimplexMax(A_1,A_2, A_3,b_1, b_2, b_3,c).

If the objective function needs to be minimized, i.e. $$c^Tx \rightarrow min,$$ then we use the SimplexMin(A_1,A_2, A_3,b_1, b_2, b_3, c).

Example.

$$7x_1 + x_3 - 4x_4$$ We need to maximize the $$x_1 + x_2$$ under the conditions $$4x_1 - x_2 \leqslant 8, 2x_1 + x_2 \leqslant 10, -5x_1 + 2x_2 \geqslant -2, x_1, x_2 \geqslant 0.$$

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4. To solve the problem $$c^Tx \rightarrow max$$ in mixed conditions desired by the matrix $A$ and vector $b$, you can use the command SimplexMax(A,signs,b,c), where an array of integers $signs$ determines the signs of comparison:

-1 means "less than or equal to",

0 means "equal to",

1 means "greater than or equal to".

The array $signs$ must contain the same number of elements as the vector $b$. If the objective function needs to be minimized, i.e. $$c^Tx \rightarrow min,$$ then we use the SimplexMin(A,signs,b,c).

Example.

We need to minimize the $$-2x_1-4x_2-2x_3$$ under the conditions

$$-2x_1 + x_2 + x_3 \leqslant 4, - x_1 + x_2 + 3x_3 \leqslant 6, x_1 - 3x_2 + x_3 \leqslant 2, x_1, x_2, x_3 \geqslant 0.$$ In:

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