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# FM Square Matrix

## 2.3 Extended Matrix Recurrence Relations

Note: if you cannot remember how to use simple matrix recurrence relations, revise notes for 2.1 Simple Matrix Recurrence Relations.

### The Extended Matrix Recurrence Formula

• The simple matrix recurrence formula we have analysed previously (in 2.1 Simple Matrix Recurrence Relations and 2.2 Applications of Matrix Recurrence Relations) only allows the values for the next state to be calculated based on proportional values of the current state. In some systems, this is not enough to produce an accurate model. The extended matrix recurrence formula allows us to model systems which have fixed values added or removed between states.
• The general form for the extended recurrence formula is:

S_{n+1}=T S_{n}+B

Where S_n is the state matrix for the nth state, T is the transition matrix and B is a matrix containing constant values.

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## 2.2 Applications of Matrix Recurrence Relations

Note: if you cannot remember how to use a matrix recurrence relation, revise notes for 2.1 Simple Matrix Recurrence Relations.

### Transition Diagrams

• Transition diagrams provide a visual method for modelling how a system changes between states.
• They consist of a series of nodes (dots) representing the variables in the system, with arrows drawn between them showing the movement of individuals/objects in the system between variables (e.g. the percentage of people who go from playing soccer one month to playing baseball the next).
• By convention numerical values should be shown as percentages in transition diagrams.
Read More »2.2 Applications of Matrix Recurrence Relations

## 2.1 Simple Matrix Recurrence Relations

### The Simple Matrix Recurrence Relation Formula

• The simplest type of matrix recurrence relation formula we will analyse in Further Maths models a system where the next “state”; S_{n+1}, can be reached by multiplying the current state; S_{n}, by a transition matrix; T, in the form:

S_{n+1}=T S_{n}

• The state matrices; S_{n} (where n is a positive whole number representing the state of the system), are column matrix listing the value of each of the system’s variables in the corresponding state.
• The transition matrix; T, is a square matrix.
• As with a linear recurrence relation, it is important to state the initial state of a system; S_{0}.
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## 1.7 Matrix Applications: Dominance Matrices

### Dominance Matrices

• A dominance matrix is a binary matrix which has individuals listed along the rows and columns. A 1 element indicates the individual corresponding to the row is “dominant to” or “beat” the individual corresponding to the column. A 0 element indicates this is not the case.
• This type of matrix is useful when modelling systems such as round robin competitions, where multiple people play against each other in rounds, with each round producing a winner.
• This type of matrix is also known as a one-step dominance matrix.
Read More »1.7 Matrix Applications: Dominance Matrices

## 1.6 Matrix Applications: Communication Matrices

### Communication Matrices

• Communication matrices provide a numerical method for visualising communication networks. Each individual in the network is listed along the rows and columns of the matrix.
• A “1” element indicates the sender corresponding to the row can communicate directly to the receiver corresponding to the column. A “0” means they cannot.
• By convention all elements on the main diagonal are “0”s, as it is generally redundant to analyse an individual’s ability to communicate with itself.
• A basic communication matrix is also referred to as a one-step communication matrix as it shows the communication allowed via a single “step” (i.e. direct communication).
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## 1.5 Binary and Permutation Matrices

### Applications of Binary Matrices

Note: if you cannot remember what a binary matrix is, revise notes for 1.1 Matrices: Definition and Unique Cases.

• Binary matrices are used to represent correlation between datasets. If two categories are correlated, a 1 is placed in the element corresponding to the two, if they are not, a 0 is placed instead.
• They are also used to represent dominance. If one category is “greater than” another, a 1 is placed in the corresponding element, else a 0 is placed instead.

Note: we will further explore dominance matrices in notes 1.7 Matrix Applications: Dominance Matrices.

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## 1.4 Basic Matrix Applications

### Using Matrices to Present Information

• Matrices can be used to present information in a tabular form.
• Similar to 2-way frequency tables, two categorical datasets are listed as the rows and columns, respectively, and each element represents a numerical value corresponding to the category in its row and column.
• When presenting data in a matrix, the categories should be listed at the top of each column, and to the left of each row.
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## 1.3 The Inverse of a Matrix

### The Determinant of a 2×2 Matrix

• The determinant of a 2 x 2 matrix can be calculated as:

\text { det }=A_{1,1} A_{2,2}-A_{1,2} A_{2,1}

• The determinant of an identity matrix is equal to 1.

Example

A=\left[\begin{array}{ll} 1 & 2 \\ 3 & 4 \end{array}\right]

The determinant of the above matrix is:

\operatorname{det}=1 * 4-2 * 3=-2

### Requirements for a Matrix to be Invertible

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## 1.1 Matrices: Definition and Unique Cases

### Matrices

• Matrices provide an alternative to ordinary linear algebra which allows us to deal with multi-dimensional data in a more concise way.
• They are similar in form to a table, with a number of entries arranged into rows and columns.
• The size of a matrix is expressed in the form rows x columns (i.e. the number of rows followed by the number of columns, with a cross separating them).
• We can refer to a specific element in a matrix using the name of the matrix, with a subscript listing the row and column corresponding to the element in question e.g. for a matrix; A, the element in the 2nd row and 1st column is denoted by A_{2,1}
Read More »1.1 Matrices: Definition and Unique Cases