# mpcqpsolver

(To be removed) Solve a quadratic programming problem using the KWIK algorithm

`mpcqpsolver`

will be removed in a future release. Use
`mpcActiveSetSolver`

instead. For more information, see Compatibility Considerations.

## Syntax

## Description

`[`

finds an optimal solution, `x`

,`status`

]
= mpcqpsolver(`Linv`

,`f`

,`A`

,`b`

,`Aeq`

,`beq`

,`iA0`

,`options`

)`x`

, to a quadratic programming
problem by minimizing the objective function:

$$J=\frac{1}{2}{x}^{\u22ba}Hx+{f}^{\u22ba}x$$

subject to inequality constraints $$Ax\ge b$$, and equality constraints $${A}_{eq}x={b}_{eq}$$. `status`

indicates the validity of
`x`

.

## Examples

### Solve Quadratic Programming Problem Using Active-Set Solver

Find the values of *x* that
minimize

$$f\left(x\right)=0.5{x}_{1}^{2}+{x}_{2}^{2}-{x}_{1}{x}_{2}-2{x}_{1}-6{x}_{2},$$

subject to the constraints

$$\begin{array}{l}{x}_{1}\ge 0\\ {x}_{2}\ge 0\\ {x}_{1}+{x}_{2}\le 2\\ -{x}_{1}+2{x}_{2}\le 2\\ 2{x}_{1}+{x}_{2}\le 3.\end{array}$$

Specify the Hessian and linear multiplier vector for the objective function.

H = [1 -1; -1 2]; f = [-2; -6];

Specify the inequality constraint parameters.

A = [1 0; 0 1; -1 -1; 1 -2; -2 -1]; b = [0; 0; -2; -2; -3];

Define `Aeq`

and `beq`

to indicate that
there are no equality constraints.

Aeq = []; beq = zeros(0,1);

Find the lower-triangular Cholesky decomposition of
`H`

.

```
[L,p] = chol(H,'lower');
Linv = inv(L);
```

It is good practice to verify that `H`

is positive
definite by checking if `p = 0`

.

p

p = 0

Create a default option set for
`mpcActiveSetSolver`

.

opt = mpcqpsolverOptions;

To cold start the solver, define all inequality constraints as inactive.

iA0 = false(size(b));

Solve the QP problem.

[x,status] = mpcqpsolver(Linv,f,A,b,Aeq,beq,iA0,opt);

Examine the solution, `x`

.

x

`x = `*2×1*
0.6667
1.3333

### Check Active Inequality Constraints for QP Solution

Find the values of *x* that
minimize

$$f\left(x\right)=3{x}_{1}^{2}+0.5{x}_{2}^{2}-2{x}_{1}{x}_{2}-3{x}_{1}+4{x}_{2},$$

subject to the constraints

$$\begin{array}{l}{x}_{1}\ge 0\\ {x}_{1}+{x}_{2}\le 5\\ {x}_{1}+2{x}_{2}\le 7.\end{array}$$

Specify the Hessian and linear multiplier vector for the objective function.

H = [6 -2; -2 1]; f = [-3; 4];

Specify the inequality constraint parameters.

A = [1 0; -1 -1; -1 -2]; b = [0; -5; -7];

Define `Aeq`

and `beq`

to indicate that
there are no equality constraints.

Aeq = []; beq = zeros(0,1);

Find the lower-triangular Cholesky decomposition of
`H`

.

```
[L,p] = chol(H,'lower');
Linv = inv(L);
```

Verify that `H`

is positive definite by checking if
`p = 0`

.

p

p = 0

Create a default option set for `mpcqpsolver`

.

opt = mpcqpsolverOptions;

To cold start the solver, define all inequality constraints as inactive.

iA0 = false(size(b));

Solve the QP problem.

[x,status,iA,lambda] = mpcqpsolver(Linv,f,A,b,Aeq,beq,iA0,opt);

Check the active inequality constraints. An active inequality constraint is at equality for the optimal solution.

iA

`iA = `*3x1 logical array*
1
0
0

There is a single active inequality constraint.

View the Lagrange multiplier for this constraint.

lambda.ineqlin(1)

ans = 5.0000

## Input Arguments

`Linv`

— Inverse of lower-triangular Cholesky decomposition of Hessian matrix

*n*-by-*n* matrix

Inverse of lower-triangular Cholesky decomposition of Hessian matrix,
specified as an *n*-by-*n* matrix, where *n* > 0 is the number of optimization variables. For a given
Hessian matrix, *H*, `Linv`

can be
computed as follows:

```
[L,p] = chol(H,'lower');
Linv = inv(L);
```

*H* is an *n*-by-*n*
matrix, which must be symmetric and positive definite. If *p* = 0, then *H* is positive definite.

**Note**

The KWIK algorithm requires the computation of
`Linv`

instead of using *H*
directly, as in the `quadprog`

(Optimization Toolbox) command.

`f`

— Multiplier of objective function linear term

column vector

Multiplier of objective function linear term, specified as a column vector
of length *n*.

`A`

— Linear inequality constraint coefficients

*m*-by-*n* matrix | `[]`

Linear inequality constraint coefficients, specified as an
*m*-by-*n* matrix, where
*m* is the number of inequality constraints.

If your problem has no inequality constraints, use
`[]`

.

`b`

— Right-hand side of inequality constraints

column vector of length *m*

Right-hand side of inequality constraints, specified as a column vector of
length *m*.

If your problem has no inequality constraints, use
`zeros(0,1)`

.

`Aeq`

— Linear equality constraint coefficients

*q*-by-*n* matrix | `[]`

Linear equality constraint coefficients, specified as a
*q*-by-*n* matrix, where
*q* is the number of equality constraints, and *q* <= *n*. Equality constraints must be linearly independent with
`rank(Aeq) =`

*q*.

If your problem has no equality constraints, use
`[]`

.

`beq`

— Right-hand side of equality constraints

column vector of length *q*

Right-hand side of equality constraints, specified as a column vector of
length *q*.

If your problem has no equality constraints, use
`zeros(0,1)`

.

`iA0`

— Initial active inequalities

logical vector of length *m*

Initial active inequalities, where the equal portion of the inequality is
true, specified as a logical vector of length *m* according
to the following:

If your problem has no inequality constraints, use

`false(0,1)`

.For a

*cold start*,`false(m,1)`

.For a

*warm start*, set`iA0(`

to start the algorithm with the*i*) == true*i*th inequality constraint active. Use the optional output argument`iA`

from a previous solution to specify`iA0`

in this way. If both`iA0(`

and*i*)`iA0(`

are*j*)`true`

, then rows*i*and*j*of`A`

should be linearly independent. Otherwise, the solution can fail with`status = -2`

.

`options`

— Option set for `mpcqpsolver`

structure

Option set for `mpcqpsolver`

, specified as a structure
created using `mpcqpsolverOptions`

.

## Output Arguments

`x`

— Optimal solution to the QP problem

column vector

Optimal solution to the QP problem, returned as a column vector of length
*n*. `mpcqpsolver`

always returns a
value for `x`

. To determine whether the solution is
optimal or feasible, check the solution `status`

.

`status`

— Solution validity indicator

positive integer | `0`

| `-1`

| `-2`

Solution validity indicator, returned as an integer according to the following:

Value | Description |
---|---|

`> 0` | `x` is optimal.
`status` represents the number of
iterations performed during optimization. |

`0` | The maximum number of iterations was reached. The
solution, `x` , may be suboptimal or
infeasible. |

`-1` | The problem appears to be infeasible, that is, the constraint $$Ax\ge b$$ cannot be satisfied. |

`-2` | An unrecoverable numerical error occurred. |

`iA`

— Active inequalities

logical vector of length *m*

Active inequalities, where the equal portion of the inequality is true,
returned as a logical vector of length *m*. If
`iA(`

, then the
*i*) == true*i*th inequality is active for the solution
`x`

.

Use `iA`

to *warm start* a
subsequent `mpcqpsolver`

solution.

`lambda`

— Lagrange multipliers

structure

Lagrange multipliers, returned as a structure with the following fields:

Field | Description |
---|---|

`ineqlin` | Multipliers of the inequality constraints, returned as a
vector of length n. When the solution is
optimal, the elements of `ineqlin` are
nonnegative. |

`eqlin` | Multipliers of the equality constraints, returned as a
vector of length q. There are no sign
restrictions in the optimal solution. |

## Tips

The KWIK algorithm requires that the Hessian matrix,

*H*, be positive definite. When calculating`Linv`

, use:`[L, p] = chol(H,'lower');`

If

*p*= 0, then*H*is positive definite. Otherwise,*p*is a positive integer.`mpcqpsolver`

provides access to the QP solver used by Model Predictive Control Toolbox™ software. Use this command to solve QP problems in your own custom MPC applications.

## Algorithms

`mpcqpsolver`

solves the QP problem using an active-set method, the
KWIK algorithm, based on [1]. For more information, see
QP Solvers.

## References

[1] Schmid, C., and L.T. Biegler.
‘Quadratic Programming Methods for Reduced Hessian SQP’. *Computers & Chemical Engineering* 18, no. 9 (September 1994):
817–32. https://doi.org/10.1016/0098-1354(94)E0001-4.

## Extended Capabilities

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

You can use

`mpcqpsolver`

as a general-purpose QP solver that supports code generation. Create the function`myCode`

that uses`mpcqpsolver`

.function [out1,out2] = myCode(in1,in2) %#codegen ... [x,status] = mpcqpsolver(Linv,f,A,b,Aeq,Beq,iA0,options); ...

Generate C code with MATLAB

^{®}Coder™.func = 'myCode'; cfg = coder.config('mex'); % or 'lib', 'dll' codegen('-config',cfg,func,'-o',func);

For code generation, use the same precision for all real inputs, including options. Configure the precision as

`'double'`

or`'single'`

using`mpcqpsolverOptions`

.

## Version History

**Introduced in R2015b**

### R2020a: `mpcqpsolver`

will be removed

`mpcqpsolver`

will be removed in a future release. Use
`mpcActiveSetSolver`

instead. There are differences between
these functions that require updates to your code.

**Update Code**

The following differences require updates to your code:

For

`mpcActiveSetSolver`

, you define inequality constraints in the form*A**x*≤*b*. Previously, for`mpcqpsolver`

, you defined inequality constraints in the form*A**x*≥*b*For

`mpcActiveSetSolver`

, you specify solver options with a structure created using the`mpcActiveSetOptions`

function. Previously, for`mpcqpsolver`

, you created an option structure using the`mpcqpsolverOptions`

function. These option structures contain the same options, though some option names have changed.By default, you pass the Hessian matrix to

`mpcActiveSetSolver`

. Previously, you passed the inverse of lower-triangular Cholesky decomposition (`Linv`

) of the Hessian matrix to`mpcqpsolver`

. To continue to use`Linv`

, set the`UseHessianAsInput`

field of the structure returned by`mpcActiveSetSolver`

to`false`

.When your QP problem has either no inequality constraints or no equality constraints, the corresponding

`A`

or`Aeq`

input argument to`mpcActiveSetSolver`

must be`zeros(0,n)`

, where`n`

is the number of decision variables. Previously, for`mpcqpsolver`

, you specified these input arguments as`[]`

.

This table shows some typical usages of `mpcqpsolver`

and
how to update your code to use `mpcActiveSetSolver`

instead.

Not Recommended | Recommended |
---|---|

opt = mpcqpsolverOptions; [x,status] = mpcqpsolver(Linv,f,A,b,... Aeq,beq,iA0,opt); | opt = mpcActiveSetOptions; opt.UseHessianAsInput = false; [x,status] = mpcActiveSetSolver(Linv,f,... -A,-b,Aeq,beq,iA0,opt); Alternatively,
you can use the Hessian matrix,
opt = mpcActiveSetOptions; [x,status] = mpcActiveSetSolver(H,f,... -A,-b,Aeq,beq,iA0,opt); |

opt = mpcqpsolverOptions('single'); [x,status] = mpcqpsolver(Linv,f,A,b,... Aeq,beq,iA0,opt); |
opt = mpcActiveSetOptions('single'); opt.UseHessianAsInput = false; [x,status] = mpcActiveSetSolver(Linv,f,... -A,-b,Aeq,beq,iA0,opt); |

opt = mpcqpsolverOptions; opt.MaxIter = 300; opt.FeasibilityTol = 1e-5; [x,status] = mpcqpsolver(Linv,f,A,b,... Aeq,beq,iA0,opt); |
opt = mpcActiveSetOptions; opt.UseHessianAsInput = false; opt.MaxIterations = 300; opt.ContraintTolerance = 1e-5; [x,status] = mpcActiveSetSolver(Linv,f,... -A,-b,Aeq,beq,iA0,opt); |

[x,status] = mpcqpsolver(Linv,f,[],... zeros(0,1),Aeq,beq,iA0,opt); |
n = length(f); opt.UseHessianAsInput = false; [x,status] = mpcActiveSetSolver(Linv,f,... zeros(0,n),zeros(0,1),Aeq,beq,iA0,opt); |

[x,status] = mpcqpsolver(Linv,f,A,b,... [],zeros(0,1),iA0,opt); |
n = length(f); opt.UseHessianAsInput = false; [x,status] = mpcActiveSetSolver(Linv,f,... -A,-b,zeros(0,n),zeros(0,1),iA0,opt); |

## See Also

### Functions

`mpcqpsolverOptions`

|`mpcActiveSetSolver`

|`mpcActiveSetOptions`

|`mpcInteriorPointSolver`

|`mpcInteriorPointOptions`

|`setCustomSolver`

|`quadprog`

(Optimization Toolbox)

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