# sdpvar

sdpvar is used to define YALMIPs symbolic decision variables.

### Syntax

```
x = sdpvar(n)
x = sdpvar(n,m)
x = sdpvar(n,m,'type')
x = sdpvar(n,m,'type','field')
x = sdpvar(dim1,dim2,dim3,...,dimn,'type','field')
sdpvar x
```

### Examples

A square real-valued **symmetric** matrix is obtained with

```
P = sdpvar(n,n) % SYMMETRIC!
```

The command above can be simplified by only giving one argument when defining a symmetric matrix or a scalar.

```
P = sdpvar(n) % SYMMETRIC!
```

We can also define the same matrix using a more verbose notation.

```
P = sdpvar(n,n,'symmetric')
```

A fully parameterized (i.e., not necessarily symmetric) square matrix requires a third argument.

```
P = sdpvar(n,n,'full')
```

A square complex-valued fully parameterized matrix is obtained with

```
P = sdpvar(n,n,'full','complex')
```

YALMIP tries to complete the third and fourth argument, so an equivalent command is

```
P = sdpvar(n,n,'sy','co')
```

Variables can alternatively be defined using command line syntax (although this sometimes can be dangerous inside functions due to a bug in some versions of MATLAB.)

```
sdpvar x y z(1,1) u(2,2) v(2,3,'full','complex')
```

A lot of users seem to get stuck initially on simple things such as defining a diagonal matrix. The most important thing to remember when working with YALMIP is that **almost all MATLAB operators can be applied also on sdpvar objects**. Hence, we create diagonal matrices with

```
X = diag(sdpvar(n,1));
```

Or Hankel…

```
X = hankel(sdpvar(n,1));
```

A typical situation is that several identical variables are needed, and a natural way to implement this is to use a loop.

```
for i = 1:100; X{i} = sdpvar(5,5);end
```

A more convenient way to implement this is to use vector valued dimensions (this currently only works if all matrices are symmetric or full)

```
X = sdpvar(5*ones(1,100),5*ones(1,100));
```

We define a 3D variable, where each slice in the first two dimensions is symmetric.

```
X = sdpvar(3,3,3)
X(:,:,1)
Linear matrix variable 3x3 (symmetric, real, 6 variables)
```

The following command defines a 4D variable, where each slice in the first two dimensions is fully parametrized.

```
X = sdpvar(3,3,3,3,'full')
X(:,:,1,1)
Linear matrix variable 3x3 (full, real, 9 variables)
```

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