# Obtain Solution Using Feasibility Mode

This example shows how to use the feasibility mode of the `fmincon`

`'interior-point'`

algorithm to obtain a feasible point. To take advantage of automatic differentiation, the example uses the problem-based approach. The example is taken from Problem 9 of Moré [1].

### Problem Setup

The problem has a 5-D optimization variable `x`

along with five quadratic constraints. The first `x`

component has a lower bound of 0, and the remaining four components have upper bounds of 0.

x = optimvar("x",5,"LowerBound",[0;-Inf;-Inf;-Inf;-Inf],"UpperBound",[Inf;0;0;0;0]);

The problem, which comes from the aircraft industry, uses aeronautical terms for the components of `x`

and has specified values of some parameters.

```
elevator = 0.1; % If elevator were 0, then [0 0 0 0 0] would be a solution
aileron = 0.0;
rudderdf = 0.0;
rollrate = x(1);
pitchrat = x(2);
yawrate = x(3);
attckang = x(4);
sslipang = x(5);
```

Create an optimization problem and the constraints.

prob = optimproblem; prob.Constraints.eq1 = (-3.933*rollrate + 0.107*pitchrat + ... 0.126*yawrate - 9.99*sslipang - 45.83*aileron - 7.64*rudderdf - ... 0.727*pitchrat*yawrate + 8.39*yawrate*attckang - ... 684.4*attckang*sslipang + 63.5*pitchrat*attckang) == 0; prob.Constraints.eq2 = (-0.987*pitchrat - 22.95*attckang - ... 28.37*elevator + 0.949*rollrate*yawrate + 0.173*rollrate*sslipang) == 0; prob.Constraints.eq3 = (0.002*rollrate - 0.235*yawrate + ... 5.67*sslipang - 0.921*aileron - 6.51*rudderdf - ... 0.716*rollrate*pitchrat - 1.578*rollrate*attckang + ... 1.132*pitchrat*attckang) == 0; prob.Constraints.eq4 = (pitchrat - attckang - ... 1.168*elevator - rollrate*sslipang) == 0; prob.Constraints.eq5 = (-yawrate - 0.196*sslipang - ... 0.0071*aileron + rollrate*attckang) == 0;

This problem has no objective function, so do not specify `prob.Objective`

.

### Attempt Solution Without Feasibility Mode

Attempt to solve the problem using the default solver and parameters, starting from the point `[0 0 0 0 0]'`

.

x0.x = zeros(5,1); [sol,~,exitflag,output] = solve(prob,x0)

Solving problem using fmincon. Solver stopped prematurely. fmincon stopped because it exceeded the iteration limit, options.MaxIterations = 1.000000e+03.

`sol = `*struct with fields:*
x: [5x1 double]

exitflag = SolverLimitExceeded

`output = `*struct with fields:*
iterations: 1000
funcCount: 1003
constrviolation: 11.1712
stepsize: 8.2265e-05
algorithm: 'interior-point'
firstorderopt: 0
cgiterations: 0
message: 'Solver stopped prematurely....'
bestfeasible: []
objectivederivative: "closed-form"
constraintderivative: "closed-form"
solver: 'fmincon'

The solver stops prematurely. Increase the iteration limit and function evaluation limit, and then try again.

options = optimoptions("fmincon","MaxIterations",1e4,"MaxFunctionEvaluations",1e4); [sol,~,exitflag,output] = solve(prob,x0,"Options",options)

Solving problem using fmincon. Converged to an infeasible point. fmincon stopped because the size of the current step is less than the value of the step size tolerance but constraints are not satisfied to within the value of the constraint tolerance. Consider enabling the interior point method feasibility mode.

`sol = `*struct with fields:*
x: [5x1 double]

exitflag = NoFeasiblePointFound

`output = `*struct with fields:*
iterations: 4089
funcCount: 4092
constrviolation: 5.0899
stepsize: 5.9783e-11
algorithm: 'interior-point'
firstorderopt: 0
cgiterations: 0
message: 'Converged to an infeasible point....'
bestfeasible: []
objectivederivative: "closed-form"
constraintderivative: "closed-form"
solver: 'fmincon'

The solver converges to an infeasible point.

### Solve Using Feasibility Mode

Try again to solve the problem, this time specifying the `EnableFeasibilityMode`

and `SubproblemAlgorithm`

options. Generally, if you need to use feasibility mode, the best approach is to set the `SubproblemAlgorithm`

option to `'cg'`

.

options = optimoptions(options,"EnableFeasibilityMode",true,... "SubproblemAlgorithm","cg"); [sol,~,exitflag,output] = solve(prob,x0,"Options",options)

Solving problem using fmincon. Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.

`sol = `*struct with fields:*
x: [5x1 double]

exitflag = OptimalSolution

`output = `*struct with fields:*
iterations: 138
funcCount: 139
constrviolation: 2.9070e-04
stepsize: 0.0057
algorithm: 'interior-point'
firstorderopt: 0
cgiterations: 0
message: 'Local minimum found that satisfies the constraints....'
bestfeasible: []
objectivederivative: "closed-form"
constraintderivative: "closed-form"
solver: 'fmincon'

This time, the solver reports that it reaches a feasible solution. However, the constraint violation in `output.constrviolation`

is not very small. Tighten the constraint tolerance and solve again. To speed the solution process, start from the returned feasible solution.

```
options.ConstraintTolerance = 1e-8;
[sol,~,exitflag,output] = solve(prob,sol,"Options",options)
```

Solving problem using fmincon. Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.

`sol = `*struct with fields:*
x: [5x1 double]

exitflag = OptimalSolution

`output = `*struct with fields:*
iterations: 2
funcCount: 3
constrviolation: 2.1164e-16
stepsize: 1.7083e-08
algorithm: 'interior-point'
firstorderopt: 0
cgiterations: 0
message: 'Local minimum found that satisfies the constraints....'
bestfeasible: [1x1 struct]
objectivederivative: "closed-form"
constraintderivative: "closed-form"
solver: 'fmincon'

The constraint violation is now quite small. The solver takes only two iterations to reach this improved solution.

### References

[1] Moré, J. J. *A collection of nonlinear model problems.* Proceedings of the AMS-SIAM Summer Seminar on the Computational Solution of Nonlinear Systems of Equations, Colorado, 1988. Argonne National Laboratory MCS-P60-0289, 1989.