# Quick Start Model Building¶

## Variable Types¶

GEKKO has eight types of variables, four of which have extra properties.

Constants, Parameters, Variables and Intermediates are the standard types. Constants and Parameters are fixed by the user, while Variables and Intermediates are degrees of freedom and are changed by the solver. All variable declarations return references to a new object.

Fixed Variables (FV), Manipulated Variables (MV), State Variables (SV) and Controlled Variables (CV) expand parameters and variables with extra attributes and features to facilitate dynamic optimization problem formulation and robustness for online use. These attributes are discussed in Manipulated Variable Options and Controlled Variable Options.

All of these variable types have the optional argument ‘name’. The name is used on the back-end to write the model file and is only useful if the user intends to manually use the model file later. Names are case-insensitive, must begin with a letter, and can only contain alphanumeric characters and underscores. If a name is not provided, one is automatically assigned a unique letter/number (c#/p#/v#/i#).

### Constants¶

Define a Constant. There is no functional difference between using a GEKKO Constant, a python variable or a magic number in the Equations. However, the Constant can be provided a name to make the .apm model more clear:

```
c = m.Const(value, [name]):
```

- Value must be provided and must be a scalar

### Parameters¶

Parameters are capable of becoming MVs and FVs. Since GEKKO defines MVs and FVs directly, parameters just serve as constant values. However, Parameters (unlike Constants) can be (and usually are) arrays.:

```
p = m.Param([value], [name])
```

- The value can be a python scalar, python list of numpy array. If the value is a scalar, it will be used throughout the horizon.

### Variable¶

Calculated by solver to meet constraints (Equations):

```
v = m.Var([value], [lb], [ub], [name]):
```

- ‘lb’ and ‘ub’ provide lower and upper variable bounds, respectively, to the solver.

### Intermediates¶

Intermediates are a unique GEKKO variable type. Intermediates, and their associated equations, are like variables except their values and gradients are evaluated explicitly, rather than being solved implicitly by the optimizer. Intermediate variables essentially blend the benefits of sequential solver approaches into simultaneous methods.

The function creates an intermediate variable i and sets it equal to argument equation:

```
i = m.Intermediate(equation,[name])
```

Equation must be an explicitly equality. Each intermediate equation is solved in order of declaration. All variable values used in the explicit equation come from either the previous iteration or an intermediate variable declared previously.

### Fixed Variable¶

Fixed Variables (FV) inherit Parameters, but potentially add a degree of freedom and are always fixed throughout the horizon (i.e. they are not discretized in dynamic modes).:

```
f = m.FV([value], [lb], [ub], [name])
```

- ‘lb’ and ‘ub’ provide lower and upper variable bounds, respectively, to the solver.

### Manipulated Variable¶

Manipulated Variables (MV) inherit FVs but are discretized throughout the horizon and have time-dependent attributes:

```
m = m.MV([value], [lb], [ub], [name])
```

- ‘lb’ and ‘ub’ provide lower and upper variable bounds, respectively, to the solver.

### State Variable¶

State Variables (SV) inherit Variables with just a couple extra attributes:

```
s = m.SV([value], [lb], [ub], [name])
```

### Controlled Variable¶

Controlled Variables (CV) inherit SVs but potentially add an objective (such as reaching a setpoint in control applications or matching model and measured values in estimation):

```
c = m.CV([value], [lb], [ub], [name])
```

## Equations¶

Equations are defined with the variables defined and python syntax:

```
m.Equation(equation)
```

For example, with variables `x`

, `y`

and `z`

:

```
m.Equation(3*x == (y**2)/z)
```

- Multiple equations can be defined at once if provided in an array or python list::
- m.Equations(eqs)

Equations are all solved implicitly together.

## Objectives¶

Objectives are defined like equations, except they must not be equality or inequality expressions. Objectives are always minimized (maximization is possible by multiplying the objective by -1):

```
m.Obj(obj)
```

## Example¶

Here’s an example script for solving problem HS71

```
from gekko import GEKKO
#Initialize Model
m = GEKKO()
#define parameter
eq = m.Param(value=40)
#initialize variables
x1,x2,x3,x4 = [m.Var(lb=1, ub=5) for i in range(4)]
#initial values
x1.value = 1
x2.value = 5
x3.value = 5
x4.value = 1
#Equations
m.Equation(x1*x2*x3*x4>=25)
m.Equation(x1**2+x2**2+x3**2+x4**2==eq)
#Objective
m.Obj(x1*x4*(x1+x2+x3)+x3)
#Set global options
m.options.IMODE = 3 #steady state optimization
#Solve simulation
m.solve()
#Results
print('')
print('Results')
print('x1: ' + str(x1.value))
print('x2: ' + str(x2.value))
print('x3: ' + str(x3.value))
print('x4: ' + str(x4.value))
```

## Connections¶

Connections are processed after the parameters and variables are parsed, but before the initialization of the values. Connections are the merging of two variables or connecting specific nodes of a discretized variable. Once the variable is connected to another, the variable is only listed as an alias. Any other references to the connected value are referred to the principal variable (var1). The alias variable (var2) can be referenced in other parts of the model, but will not appear in the solution files.

```
m.Connection(var1,var2,pos1=None,pos2=None,node1='end',node2='end')
```

var1 must be a GEKKO variable, but var2 can be a static value. If pos1 or pos2 is not None, the associated var must be a GEKKO variable and the position is the (0-indexed) time-discretized index of the variable.