Numerical Methods in Computational Finance. Daniel J. Duffy

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x prime element-of upper X period"/>

      The function k left-parenthesis period comma period right-parenthesis is called a kernel.

      We discuss the notion of a matrix for a linear transformation. To this end, consider the linear transformation upper A element-of upper L left-parenthesis upper V semicolon upper W right-parenthesis where V and W are finite-dimensional vector spaces, and let StartSet e 1 comma ellipsis comma e Subscript n Baseline EndSet and StartSet f 1 comma ellipsis comma f Subscript m Baseline EndSet be bases in V and W, respectively. Then:

      for some scalars a Subscript italic i j Baseline comma 1 less-than-or-equal-to i less-than-or-equal-to m comma 1 less-than-or-equal-to j less-than-or-equal-to n. We can represent these scalars in rectangular form which we call a matrix:

      (5.11)upper A equals Start 4 By 4 Matrix 1st Row 1st Column a 11 2nd Column a 12 3rd Column ellipsis 4th Column a Subscript 1 n Baseline 2nd Row 1st Column a 21 2nd Column a 22 3rd Column ellipsis 4th Column a Subscript 2 n Baseline 3rd Row 1st Column vertical-ellipsis 2nd Column Blank 3rd Column Blank 4th Column vertical-ellipsis 4th Row 1st Column a Subscript m Baseline 1 Baseline 2nd Column a Subscript m Baseline 2 Baseline 3rd Column ellipsis 4th Column a Subscript italic m n Baseline EndMatrix period

      (5.12)upper A left-parenthesis x right-parenthesis equals x 1 upper A left-parenthesis e 1 right-parenthesis plus ellipsis plus x Subscript n Baseline upper A left-parenthesis e Subscript n Baseline right-parenthesis where x equals sigma-summation Underscript j equals 1 Overscript n Endscripts x Subscript j Baseline e Subscript j Baseline period

      5.4.1 Some Examples

      We take simple two-dimensional problems to model reflection and (counterclockwise) rotation in the plane.

       Case 1:(5.3)

       Case 2:From (5.10) we getwhich allows us to find the matrix A.

       Case 3:The trick is to build the matrix column by column, from top to bottom.

      We first give a short history of how matrices were discovered.

       The term matrix was introduced by the 19th-century English mathematician James Sylvester, but it was his friend the mathematician Arthur Cayley who developed the algebraic aspect of matrices in two papers in the 1850s. Cayley first applied them to the study of systems of linear equations, where they are still very useful. They are also important because, as Cayley recognised, certain sets of matrices form algebraic systems in which many of the ordinary laws of arithmetic (e.g., the associative and distributive laws) are valid but in which other laws (for example, the commutative law) are not valid. (Wikipedia)

      The notation and operations for vectors are:

      (5.13)StartLayout 1st Row a equals left-parenthesis a 1 comma ellipsis comma a Subscript n Baseline right-parenthesis comma b equals left-parenthesis b 1 comma ellipsis comma b Subscript n Baseline right-parenthesis 2nd Row a plus b equals left-parenthesis a 1 plus b 1 comma ellipsis comma a Subscript n Baseline plus b Subscript n Baseline right-parenthesis 3rd Row normal lamda a equals left-parenthesis normal lamda a 1 comma ellipsis comma normal lamda a Subscript n Baseline right-parenthesis comma normal lamda element-of normal double struck upper R 4th Row a dot b equals sigma-summation Underscript j equals 1 Overscript n Endscripts a Subscript j Baseline b Subscript j Baseline 5th Row double-vertical-bar a double-vertical-bar equals StartRoot a dot a EndRoot equals left-parenthesis sigma-summation Underscript j equals 1 Overscript n Endscripts a Subscript j Superscript 2 Baseline right-parenthesis Superscript 1 slash 2 Baseline equals StartRoot sigma-summation Underscript j equals 1 Overscript n Endscripts a Subscript j Superscript 2 Baseline EndRoot EndLayout

      and for rectangular matrices:

      (5.14)upper A equals left-parenthesis a Subscript italic i j Baseline right-parenthesis equals Start 4 By 4 Matrix 1st Row 1st Column a 11 2nd Column a 12 3rd Column midline-horizontal-ellipsis 4th Column a Subscript 1 n Baseline 2nd Row 1st Column a 21 2nd Column a 22 3rd Column midline-horizontal-ellipsis 4th Column a Subscript 2 n Baseline 3rd Row 1st Column vertical-ellipsis 2nd Column Blank 3rd Column Blank 4th Column Blank 4th Row 1st Column a Subscript m Baseline 1 Baseline 2nd Column a Subscript m Baseline 2 Baseline 3rd Column midline-horizontal-ellipsis 4th Column a Subscript italic m n Baseline EndMatrix period

      Some special cases of matrices are:

       Row matrix: has one row (row vector).

       Column matrix: has one column (column vector).

       Zero matrix: all entries have the value zero.

       Diagonal matrix: all entries zero except those on main diagonal.

       Identity matrix: diagonal matrix all of whose diagonal elements == 1.

      Matrix operations are:

      (5.15)

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