# 4. Low-level interface¶

FORCES Pro supports designing solvers and controllers via MATLAB and Python scripts. Regardless of the language used, a Simulink block is always created such that you can plug your advanced formulation directly into your simulation models, or download it to a real-time target platform.

The low-level interface gives advanced optimization users the full flexibility when designing custom optimization solvers and MPC controllers based on non-standard formulations.

## 4.1. Supported problem class¶

The FORCES Pro low-level interface supports the class of **convex multistage quadratically constrained programs (QCQPs)** of the form

for \(i=1,...,N\) and \(k=1,...,M\). To obtain a solver for this optimization program using the FORCES Pro client, you need to define all data in the problem, that is the matrices \(H_i\), \(A_i\), \(Q_{i,j}\), \(D_i\), \(C_i\) and the vectors \(\underline{z}_i\), \(\bar{z}_i\), \(b_i\), \(L_{i,k}\), \(r_{i,k}\), \(c_i\), in a MATLAB struct or Python dictionary, along with the corresponding dimensions. The following steps will take you through this process.

Note

FORCES Pro supports all problem data to be parametric, i.e. to be unknown at code generation time. Read Section 7 to learn how to use parameters correctly.

## 4.2. Expressing the optimization problem in MATLAB or Python¶

In the following, we describe how to model a problem of the above form with FORCES Pro. First make sure that the FORCES Pro client is on the MATLAB/Python path. See Section 2 for more details.

Python users first have to import the FORCES Pro module.

```
from forcespro import *
```

## 4.3. Multistage struct¶

First, an empty struct/class has to be initialized, which contains all fields needed and initialises matrices and vectors to empty matrices. The command

```
stages = MultistageProblem(N);
```

creates such an empty structure/class of length \(N\). Once this structure/class has been created, the corresponding matrices, vectors and dimensions can be set for each element of stages.

## 4.4. Dimensions¶

In order to define the dimensions of the stage variables \(zi\), the number of lower and upper bounds, the number of polytopic inequality constraints and the number of quadratic constraints use the following fields:

```
stages(i).dims.n = ...; % length of stage variable zi
stages(i).dims.r = ...; % number of equality constraints
stages(i).dims.l = ...; % number of lower bounds
stages(i).dims.u = ...; % number of upper bounds
stages(i).dims.p = ...; % number of polytopic constraints
stages(i).dims.q = ...; % number of quadratic constraints
```

```
stages.dims[ i ]['n'] = ... # length of stage variable zi
stages.dims[ i ]['r'] = ... # number of equality constraints
stages.dims[ i ]['l'] = ... # number of lower bounds
stages.dims[ i ]['u'] = ... # number of upper bounds
stages.dims[ i ]['p'] = ... # number of polytopic constraints
stages.dims[ i ]['q'] = ... # number of quadratic constraints
```

## 4.5. Cost function¶

The cost function is, for each stage, defined by the matrix \(H_i\) and the vector \(f_i\). These can be set by

```
stages(i).cost.H = ...; % Hessian
stages(i).cost.f = ...; % linear term
```

```
stages.cost[i]['H'] = ... # Hessian
stages.cost[i]['f'] = ... # linear term
```

Note: whenever one of these terms is zero, you have to set them to zero (otherwise the default of an empty matrix is assumed, which is different from a zero matrix).

## 4.6. Equality constraints¶

The equality constraints for each stage, which are given by the matrices \(C_i\), \(D_i\) and the vector \(c_i\), have to be provided in the following form:

```
stages(i).eq.C = ...;
stages(i).eq.c = ...;
stages(i).eq.D = ...;
```

```
stages.eq[ i ]['C'] = ...
stages.eq[ i ]['c'] = ...
stages.eq[ i ]['D'] = ...
```

## 4.7. Lower and upper bounds¶

Lower and upper bounds have to be set in sparse format, i.e. an index vector lbIdx/ubIdx that defines the elements of the stage variable \(z_i\) has to be provided, along with the corresponding upper/lower bound lb/ub:

```
stages(i).ineq.b.lbidx = ...; % index vector for lower bounds
stages(i).ineq.b.lb = ...; % lower bounds
stages(i).ineq.b.ubidx = ...; % index vector for upper bounds
stages(i).ineq.b.ub = ...; % upper bounds
```

```
stages.ineq[ i ]['b']['lbidx'] = ... # index vector for lower bounds
stages.ineq[ i ]['b']['lb'] = ... # lower bounds
stages.ineq[ i ]['b']['ubidx'] = ... # index vector for upper bounds
stages.ineq[ i ]['b']['ub'] = ... # upper bounds
```

Both lb and lbIdx must have length stages(i).dims.l / stages.dims[ i ][‘l’], and both ub and ubIdx must have length stages(i).dims.u / stages.dims[ i ][‘u’].

## 4.8. Polytopic constraints¶

In order to define the inequality \(A_iz_i\leq b_i\), use

```
stages(i).ineq.p.A = ...; % Jacobian of linear inequality
stages(i).ineq.p.b = ...; % RHS of linear inequality
```

```
stages.ineq[ i ]['A'] = ... # Jacobian of linear inequality
stages.ineq[ i ]['b'] = ... # RHS of linear inequality
```

The matrix A must have stages(i).dims.p / stages.dims[ i ][‘p’] rows and stages(i).dims.n / stages.dims[ i ][‘n’] columns. The vector b must have stages(i).dims.p / stages.dims[ i ][‘p’] rows.

## 4.9. Quadratic constraints¶

Similar to lower and upper bounds, quadratic constraints are given in sparse form by means of an index vector, which determines on which variables the corresponding quadratic constraint acts.

```
stages(i).ineq.q.idx = { idx1, idx2, …}; % index vectors
stages(i).ineq.q.Q = { Q1, Q2, …}; % Hessians
stages(i).ineq.q.l = { L1, L2, …}; % linear terms
stages(i).ineq.q.r = [ r1; r2; … ]; % RHSs
```

```
stages.ineq[ i ]['q']['idx'] = ... # index vectors
stages.ineq[ i ]['q']['Q'] = ... # Hessians
stages.ineq[ i ]['q']['l'] = ... # linear terms
stages.ineq[ i ]['q']['r'] = ... # RHSs
```

If the index vector idx1 has length \(m_1\), then the matrix Q must be square and of size \(m_1\times m_1\), the column vector l1 must be of size \(m_1\) and \(r_1\) is a scalar. Of course this dimension rules apply to all further quadratic constraints that might be present. Note that \(L_1\), \(L_2\) etc. are column vectors in MATLAB!

Since multiple quadratic constraints can be present per stage, in MATLAB we make use of the cell notation for the Hessian, linear terms, and index vectors. In Python we make use of Python object arrays for the Hessians, linear terms, and index vectors.

### 4.9.1. Example¶

To express the two quadratic constraints

on the third stage variable, use the code

```
stages(3).ineq.q.idx = { [3 5], [1] } % index vectors
stages(3).ineq.q.Q = { [1 0; 0 2], [5] }; % Hessians
stages(3).ineq.q.l = { [0; -1], [0] }; % linear terms
stages(3).ineq.q.r = [ 3; 1 ]; % RHSs
```

```
stages.ineq[3-1]['q']['idx'] = np.zeros((2,), dtype=object) % index vectors
stages.ineq[3-1]['q']['idx'][0] = np.array([3,5])
stages.ineq[3-1]['q']['idx'][1] = np.array([1])
stages.ineq[3-1]['q']['Q'] = np.zeros((2,), dtype=object) % Hessians
stages.ineq[3-1]['q']['Q'][0] = np.array([1.0 0],[0 2.0])
stages.ineq[3-1]['q']['Q'][1] = np.array([5])
stages.ineq[3-1]['q']['l'] = np.zeros((2,), dtype=object) % linear terms
stages.ineq[3-1]['q']['l'][0] = np.array([0], [-1])
stages.ineq[3-1]['q']['l'][1] = np.array([0])
stages.ineq[3-1]['q']['r'] = np.array([3],[1]) % RHSs
```

## 4.10. Binary constraints¶

To declare binary variables, you can use the bidx field of the stages struct or object. For example, the following code declares variables 3 and 7 of stage 1 to be binary:

```
stages(1).bidx = [3 7]
```

```
stages.bidx[1] = np.array([3, 7])
```

That’s it! You can now generate a solver that will take into account the binary constraints on these variables. If binary variables are declared, FORCES Pro will add a branch-and-bound procedure to the standard convex solver it generates.