fmincon
Find minimum of constrained nonlinear multivariable function
Syntax
Description
Nonlinear programming solver.
Finds the minimum of a problem specified by
$$\underset{x}{\mathrm{min}}f(x)\text{suchthat}\{\begin{array}{c}c(x)\le 0\\ ceq(x)=0\\ A\cdot x\le b\\ Aeq\cdot x=beq\\ lb\le x\le ub,\end{array}$$
b and beq are vectors, A and Aeq are matrices, c(x) and ceq(x) are functions that return vectors, and f(x) is a function that returns a scalar. f(x), c(x), and ceq(x) can be nonlinear functions.
x, lb, and ub can be passed as vectors or matrices; see Matrix Arguments.
starts
at x
= fmincon(fun
,x0
,A
,b
)x0
and attempts to find a minimizer x
of
the function described in fun
subject to the linear
inequalities A*x ≤ b
. x0
can
be a scalar, vector, or matrix.
Note
Passing Extra Parameters explains how to pass extra parameters to the objective function and nonlinear constraint functions, if necessary.
defines
a set of lower and upper bounds on the design variables in x
= fmincon(fun
,x0
,A
,b
,Aeq
,beq
,lb
,ub
)x
,
so that the solution is always in the range lb
≤ x
≤ ub
.
If no equalities exist, set Aeq = []
and beq
= []
. If x(i)
is unbounded below, set lb(i)
= Inf
, and if x(i)
is unbounded above,
set ub(i) = Inf
.
Note
If the specified input bounds for a problem are inconsistent, fmincon
throws
an error. In this case, output x
is x0
and fval
is []
.
For the default 'interiorpoint'
algorithm, fmincon
sets
components of x0
that violate the bounds lb ≤ x ≤ ub
, or are equal to a bound, to the interior
of the bound region. For the 'trustregionreflective'
algorithm, fmincon
sets
violating components to the interior of the bound region. For other
algorithms, fmincon
sets violating components
to the closest bound. Components that respect the bounds are not changed.
See Iterations Can Violate Constraints.
Examples
Linear Inequality Constraint
Find the minimum value of Rosenbrock's function when there is a linear inequality constraint.
Set the objective function fun
to be Rosenbrock's function. Rosenbrock's function is wellknown to be difficult to minimize. It has its minimum objective value of 0 at the point (1,1). For more information, see Constrained Nonlinear Problem Using Optimize Live Editor Task or Solver.
fun = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2;
Find the minimum value starting from the point [1,2]
, constrained to have $$x(1)+2x(2)\le 1$$. Express this constraint in the form Ax <= b
by taking A = [1,2]
and b = 1
. Notice that this constraint means that the solution will not be at the unconstrained solution (1,1), because at that point $$x(1)+2x(2)=3>1$$.
x0 = [1,2]; A = [1,2]; b = 1; x = fmincon(fun,x0,A,b)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
0.5022 0.2489
Linear Inequality and Equality Constraint
Find the minimum value of Rosenbrock's function when there are both a linear inequality constraint and a linear equality constraint.
Set the objective function fun
to be Rosenbrock's function.
fun = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2;
Find the minimum value starting from the point [0.5,0]
, constrained to have $$x(1)+2x(2)\le 1$$ and $$2x(1)+x(2)=1$$.
Express the linear inequality constraint in the form
A*x <= b
by takingA = [1,2]
andb = 1
.Express the linear equality constraint in the form
Aeq*x = beq
by takingAeq = [2,1]
andbeq = 1
.
x0 = [0.5,0]; A = [1,2]; b = 1; Aeq = [2,1]; beq = 1; x = fmincon(fun,x0,A,b,Aeq,beq)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
0.4149 0.1701
Minimize with Bound Constraints
Find the minimum of an objective function in the presence of bound constraints.
The objective function is a simple algebraic function of two variables.
fun = @(x)1+x(1)/(1+x(2))  3*x(1)*x(2) + x(2)*(1+x(1));
Look in the region where $$x$$ has positive values, $$x(1)\le 1$$, and $$x(2)\le 2$$.
lb = [0,0]; ub = [1,2];
The problem has no linear constraints, so set those arguments to []
.
A = []; b = []; Aeq = []; beq = [];
Try an initial point in the middle of the region.
x0 = (lb + ub)/2;
Solve the problem.
x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
1.0000 2.0000
A different initial point can lead to a different solution.
x0 = x0/5; x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
10^{6} ×
0.4000 0.4000
To determine which solution is better, see Obtain the Objective Function Value.
Nonlinear Constraints
Find the minimum of a function subject to nonlinear constraints
Find the point where Rosenbrock's function is minimized within a circle, also subject to bound constraints.
fun = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2;
Look within the region $$0\le x(1)\le 0.5$$, $$0.2\le x(2)\le 0.8$$.
lb = [0,0.2]; ub = [0.5,0.8];
Also look within the circle centered at [1/3,1/3] with radius 1/3. Use this code for the nonlinear constraint function.
function [c,ceq] = circlecon(x) c = (x(1)1/3)^2 + (x(2)1/3)^2  (1/3)^2; ceq = []; end
There are no linear constraints, so set those arguments to []
.
A = []; b = []; Aeq = []; beq = [];
Choose an initial point satisfying all the constraints.
x0 = [1/4,1/4];
Solve the problem.
nonlcon = @circlecon; x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
0.5000 0.2500
Nondefault Options
Set options to view iterations as they occur and to use a different algorithm.
To observe the fmincon
solution process, set the Display
option to 'iter'
. Also, try the 'sqp'
algorithm, which is sometimes faster or more accurate than the default 'interiorpoint'
algorithm.
options = optimoptions('fmincon','Display','iter','Algorithm','sqp');
Find the minimum of Rosenbrock's function on the unit disk, $$x{}^{2}\le 1$$. First create a function that represents the nonlinear constraint. Save this as a file named unitdisk.m
on your MATLAB® path.
type unitdisk.m
function [c,ceq] = unitdisk(x) c = x(1)^2 + x(2)^2  1; ceq = [];
Create the remaining problem specifications. Then run fmincon
.
fun = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2; A = []; b = []; Aeq = []; beq = []; lb = []; ub = []; nonlcon = @unitdisk; x0 = [0,0]; x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)
Iter Funccount Fval Feasibility Step Length Norm of Firstorder step optimality 0 3 1.000000e+00 0.000e+00 1.000e+00 0.000e+00 2.000e+00 1 12 8.913011e01 0.000e+00 1.176e01 2.353e01 1.107e+01 2 22 8.047847e01 0.000e+00 8.235e02 1.900e01 1.330e+01 3 28 4.197517e01 0.000e+00 3.430e01 1.217e01 6.172e+00 4 31 2.733703e01 0.000e+00 1.000e+00 5.254e02 5.705e01 5 34 2.397111e01 0.000e+00 1.000e+00 7.498e02 3.164e+00 6 37 2.036002e01 0.000e+00 1.000e+00 5.960e02 3.106e+00 7 40 1.164353e01 0.000e+00 1.000e+00 1.459e01 1.059e+00 8 43 1.161753e01 0.000e+00 1.000e+00 1.754e01 7.383e+00 9 46 5.901602e02 0.000e+00 1.000e+00 1.547e02 7.278e01 10 49 4.533081e02 2.898e03 1.000e+00 5.393e02 1.252e01 11 52 4.567454e02 2.225e06 1.000e+00 1.492e03 1.679e03 12 55 4.567481e02 4.386e12 1.000e+00 2.095e06 1.502e05 13 58 4.567481e02 0.000e+00 1.000e+00 2.193e12 1.406e05 Local minimum possible. Constraints satisfied. fmincon stopped because the size of the current step is less than the value of the step size tolerance and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
0.7864 0.6177
For iterative display details, see Iterative Display.
Include Gradient
Include gradient evaluation in the objective function for faster or more reliable computations.
Include the gradient evaluation as a conditionalized output in the objective function file. For details, see Including Gradients and Hessians. The objective function is Rosenbrock's function,
$$f(x)=100{\left({x}_{2}{x}_{1}^{2}\right)}^{2}+(1{x}_{1}{)}^{2},$$
which has gradient
$$\nabla f(x)=\left[\begin{array}{c}400\left({x}_{2}{x}_{1}^{2}\right){x}_{1}2\left(1{x}_{1}\right)\\ 200\left({x}_{2}{x}_{1}^{2}\right)\end{array}\right].$$
This code creates the rosenbrockwithgrad
function, which implements the objective function with gradient..
function [f,g] = rosenbrockwithgrad(x) % Calculate objective f f = 100*(x(2)  x(1)^2)^2 + (1x(1))^2; if nargout > 1 % gradient required g = [400*(x(2)x(1)^2)*x(1)2*(1x(1)); 200*(x(2)x(1)^2)]; end end
Create options to use the objective function gradient.
options = optimoptions('fmincon','SpecifyObjectiveGradient',true);
Create the other inputs for the problem. Then call fmincon
.
fun = @rosenbrockwithgrad; x0 = [1,2]; A = []; b = []; Aeq = []; beq = []; lb = [2,2]; ub = [2,2]; nonlcon = []; x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
1.0000 1.0000
Use a Problem Structure
Solve the same problem as in Nondefault Options using a problem structure instead of separate arguments.
Create the options and a problem structure. See problem for the field names and required fields.
options = optimoptions('fmincon','Display','iter','Algorithm','sqp'); problem.options = options; problem.solver = 'fmincon'; problem.objective = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2; problem.x0 = [0,0];
The nonlinear constraint function unitdisk
appears at the end of this example. Include the nonlinear constraint function in problem
.
problem.nonlcon = @unitdisk;
Solve the problem.
x = fmincon(problem)
Iter Funccount Fval Feasibility Step Length Norm of Firstorder step optimality 0 3 1.000000e+00 0.000e+00 1.000e+00 0.000e+00 2.000e+00 1 12 8.913011e01 0.000e+00 1.176e01 2.353e01 1.107e+01 2 22 8.047847e01 0.000e+00 8.235e02 1.900e01 1.330e+01 3 28 4.197517e01 0.000e+00 3.430e01 1.217e01 6.172e+00 4 31 2.733703e01 0.000e+00 1.000e+00 5.254e02 5.705e01 5 34 2.397111e01 0.000e+00 1.000e+00 7.498e02 3.164e+00 6 37 2.036002e01 0.000e+00 1.000e+00 5.960e02 3.106e+00 7 40 1.164353e01 0.000e+00 1.000e+00 1.459e01 1.059e+00 8 43 1.161753e01 0.000e+00 1.000e+00 1.754e01 7.383e+00 9 46 5.901602e02 0.000e+00 1.000e+00 1.547e02 7.278e01 10 49 4.533081e02 2.898e03 1.000e+00 5.393e02 1.252e01 11 52 4.567454e02 2.225e06 1.000e+00 1.492e03 1.679e03 12 55 4.567481e02 4.386e12 1.000e+00 2.095e06 1.502e05 13 58 4.567481e02 0.000e+00 1.000e+00 2.193e12 1.406e05 Local minimum possible. Constraints satisfied. fmincon stopped because the size of the current step is less than the value of the step size tolerance and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
0.7864 0.6177
The iterative display and solution are the same as in Nondefault Options.
The following code creates the unitdisk
function.
function [c,ceq] = unitdisk(x) c = x(1)^2 + x(2)^2  1; ceq = []; end
Obtain the Objective Function Value
Call fmincon
with the fval
output to obtain the value of the objective function at the solution.
The Minimize with Bound Constraints example shows two solutions. Which is better? Run the example requesting the fval
output as well as the solution.
fun = @(x)1+x(1)./(1+x(2))  3*x(1).*x(2) + x(2).*(1+x(1)); lb = [0,0]; ub = [1,2]; A = []; b = []; Aeq = []; beq = []; x0 = (lb + ub)/2; [x,fval] = fmincon(fun,x0,A,b,Aeq,beq,lb,ub)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2
1.0000 2.0000
fval = 0.6667
Run the problem using a different starting point x0
.
x0 = x0/5; [x2,fval2] = fmincon(fun,x0,A,b,Aeq,beq,lb,ub)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
x2 = 1×2
10^{6} ×
0.4000 0.4000
fval2 = 1.0000
This solution has an objective function value fval2
= 1, which is higher than the first value fval
= –0.6667. The first solution x
has a lower local minimum objective function value.
Examine Solution Using Extra Outputs
To easily examine the quality of a solution, request the exitflag
and output
outputs.
Set up the problem of minimizing Rosenbrock's function on the unit disk, . First create a function that represents the nonlinear constraint. Save this as a file named unitdisk.m
on your MATLAB® path.
function [c,ceq] = unitdisk(x)
c = x(1)^2 + x(2)^2  1;
ceq = [];
Create the remaining problem specifications.
fun = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2; nonlcon = @unitdisk; A = []; b = []; Aeq = []; beq = []; lb = []; ub = []; x0 = [0,0];
Call fmincon
using the fval
, exitflag
, and output
outputs.
[x,fval,exitflag,output] = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. x = 0.7864 0.6177 fval = 0.0457 exitflag = 1 output = struct with fields: iterations: 24 funcCount: 84 constrviolation: 0 stepsize: 6.9162e06 algorithm: 'interiorpoint' firstorderopt: 2.4373e08 cgiterations: 4 message: 'Local minimum found that satisfies the constraints....' bestfeasible: [1x1 struct]
The
exitflag
value1
indicates that the solution is a local minimum.The
output
structure reports several statistics about the solution process. In particular, it gives the number of iterations inoutput.iterations
, number of function evaluations inoutput.funcCount
, and the feasibility inoutput.constrviolation
.
Obtain All Outputs
fmincon
optionally returns several outputs that you can use for analyzing the reported solution.
Set up the problem of minimizing Rosenbrock's function on the unit disk. First create a function that represents the nonlinear constraint. Save this as a file named unitdisk.m
on your MATLAB® path.
function [c,ceq] = unitdisk(x)
c = x(1)^2 + x(2)^2  1;
ceq = [];
Create the remaining problem specifications.
fun = @(x)100*(x(2)x(1)^2)^2 + (1x(1))^2; nonlcon = @unitdisk; A = []; b = []; Aeq = []; beq = []; lb = []; ub = []; x0 = [0,0];
Request all fmincon
outputs.
[x,fval,exitflag,output,lambda,grad,hessian] = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon)
Local minimum found that satisfies the constraints. Optimization completed because the objective function is nondecreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. x = 0.7864 0.6177 fval = 0.0457 exitflag = 1 output = struct with fields: iterations: 24 funcCount: 84 constrviolation: 0 stepsize: 6.9162e06 algorithm: 'interiorpoint' firstorderopt: 2.4373e08 cgiterations: 4 message: 'Local minimum found that satisfies the constraints....' bestfeasible: [1x1 struct] lambda = struct with fields: eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] lower: [2x1 double] upper: [2x1 double] ineqnonlin: 0.1215 grad = 0.1911 0.1501 hessian = 497.2903 314.5589 314.5589 200.2392
The
lambda.ineqnonlin
output shows that the nonlinear constraint is active at the solution, and gives the value of the associated Lagrange multiplier.The
grad
output gives the value of the gradient of the objective function at the solutionx
.The
hessian
output is described in fmincon Hessian.
Input Arguments
fun
— Function to minimize
function handle  function name
Function to minimize, specified as a function handle or function
name. fun
is a function that accepts a vector or
array x
and returns a real scalar f
,
the objective function evaluated at x
.
fmincon
passes x
to
your objective function and any nonlinear constraint functions in the shape of the
x0
argument. For example, if x0
is a 5by3 array,
then fmincon
passes x
to fun
as a
5by3 array. However, fmincon
multiplies linear constraint matrices
A
or Aeq
with x
after
converting x
to the column vector x(:)
.
Specify fun
as a function handle for a file:
x = fmincon(@myfun,x0,A,b)
where myfun
is a MATLAB^{®} function such
as
function f = myfun(x) f = ... % Compute function value at x
You can also specify fun
as a function handle
for an anonymous function:
x = fmincon(@(x)norm(x)^2,x0,A,b);
If you can compute the gradient of fun
and the SpecifyObjectiveGradient
option is set to true
, as set
by
options = optimoptions('fmincon','SpecifyObjectiveGradient',true)
fun
must return the gradient vector
g(x)
in the second output argument.
If you can also compute the Hessian matrix and the HessianFcn
option
is set to 'objective'
via optimoptions
and the Algorithm
option
is 'trustregionreflective'
, fun
must
return the Hessian value H(x)
, a symmetric matrix,
in a third output argument. fun
can give a sparse
Hessian. See Hessian for fminunc trustregion or fmincon trustregionreflective algorithms for
details.
If you can also compute the Hessian matrix and the Algorithm
option
is set to 'interiorpoint'
, there is a different
way to pass the Hessian to fmincon
. For more
information, see Hessian for fmincon interiorpoint algorithm. For an example
using Symbolic Math Toolbox™ to compute the gradient and Hessian,
see Calculate Gradients and Hessians Using Symbolic Math Toolbox.
The interiorpoint
and trustregionreflective
algorithms
allow you to supply a Hessian multiply function. This function gives
the result of a Hessiantimesvector product without computing the
Hessian directly. This can save memory. See Hessian Multiply Function.
Example: fun = @(x)sin(x(1))*cos(x(2))
Data Types: char
 function_handle
 string
x0
— Initial point
real vector  real array
Initial point, specified as a real vector or real array. Solvers use the
number of elements in, and size of, x0
to determine the
number and size of variables that fun
accepts.
'interiorpoint'
algorithm — If theHonorBounds
option istrue
(default),fmincon
resetsx0
components that are on or outside boundslb
orub
to values strictly between the bounds.'trustregionreflective'
algorithm —fmincon
resets infeasiblex0
components to be feasible with respect to bounds or linear equalities.'sqp'
,'sqplegacy'
, or'activeset'
algorithm —fmincon
resetsx0
components that are outside bounds to the values of the corresponding bounds.
Example: x0 = [1,2,3,4]
Data Types: double
A
— Linear inequality constraints
real matrix
Linear inequality constraints, specified as a real matrix. A
is
an M
byN
matrix, where M
is
the number of inequalities, and N
is the number
of variables (number of elements in x0
). For
large problems, pass A
as a sparse matrix.
A
encodes the M
linear
inequalities
A*x <= b
,
where x
is the column vector of N
variables x(:)
,
and b
is a column vector with M
elements.
For example, consider these inequalities:
x_{1} + 2x_{2} ≤
10
3x_{1} +
4x_{2} ≤ 20
5x_{1} +
6x_{2} ≤ 30,
Specify the inequalities by entering the following constraints.
A = [1,2;3,4;5,6]; b = [10;20;30];
Example: To specify that the x components sum to 1 or less, use A =
ones(1,N)
and b = 1
.
Data Types: single
 double
b
— Linear inequality constraints
real vector
Linear inequality constraints, specified as a real vector. b
is
an M
element vector related to the A
matrix.
If you pass b
as a row vector, solvers internally
convert b
to the column vector b(:)
.
For large problems, pass b
as a sparse vector.
b
encodes the M
linear
inequalities
A*x <= b
,
where x
is the column vector of N
variables x(:)
,
and A
is a matrix of size M
byN
.
For example, consider these inequalities:
x_{1}
+ 2x_{2} ≤
10
3x_{1}
+ 4x_{2} ≤
20
5x_{1}
+ 6x_{2} ≤
30.
Specify the inequalities by entering the following constraints.
A = [1,2;3,4;5,6]; b = [10;20;30];
Example: To specify that the x components sum to 1 or less, use A =
ones(1,N)
and b = 1
.
Data Types: single
 double
Aeq
— Linear equality constraints
real matrix
Linear equality constraints, specified as a real matrix. Aeq
is
an Me
byN
matrix, where Me
is
the number of equalities, and N
is the number of
variables (number of elements in x0
). For large
problems, pass Aeq
as a sparse matrix.
Aeq
encodes the Me
linear
equalities
Aeq*x = beq
,
where x
is the column vector of N
variables x(:)
,
and beq
is a column vector with Me
elements.
For example, consider these inequalities:
x_{1} + 2x_{2} +
3x_{3} = 10
2x_{1} +
4x_{2} + x_{3} =
20,
Specify the inequalities by entering the following constraints.
Aeq = [1,2,3;2,4,1]; beq = [10;20];
Example: To specify that the x components sum to 1, use Aeq = ones(1,N)
and
beq = 1
.
Data Types: single
 double
beq
— Linear equality constraints
real vector
Linear equality constraints, specified as a real vector. beq
is
an Me
element vector related to the Aeq
matrix.
If you pass beq
as a row vector, solvers internally
convert beq
to the column vector beq(:)
.
For large problems, pass beq
as a sparse vector.
beq
encodes the Me
linear
equalities
Aeq*x = beq
,
where x
is the column vector of N
variables
x(:)
, and Aeq
is a matrix of size
Me
byN
.
For example, consider these equalities:
x_{1}
+ 2x_{2} +
3x_{3} =
10
2x_{1}
+ 4x_{2} +
x_{3} =
20.
Specify the equalities by entering the following constraints.
Aeq = [1,2,3;2,4,1]; beq = [10;20];
Example: To specify that the x components sum to 1, use Aeq = ones(1,N)
and
beq = 1
.
Data Types: single
 double
lb
— Lower bounds
real vector  real array
Lower bounds, specified as a real vector or real array. If the number of elements in
x0
is equal to the number of elements in lb
,
then lb
specifies that
x(i) >= lb(i)
for all i
.
If numel(lb) < numel(x0)
, then lb
specifies
that
x(i) >= lb(i)
for 1 <=
i <= numel(lb)
.
If lb
has fewer elements than x0
, solvers issue a
warning.
Example: To specify that all x components are positive, use lb =
zeros(size(x0))
.
Data Types: single
 double
ub
— Upper bounds
real vector  real array
Upper bounds, specified as a real vector or real array. If the number of elements in
x0
is equal to the number of elements in ub
,
then ub
specifies that
x(i) <= ub(i)
for all i
.
If numel(ub) < numel(x0)
, then ub
specifies
that
x(i) <= ub(i)
for 1 <=
i <= numel(ub)
.
If ub
has fewer elements than x0
, solvers issue
a warning.
Example: To specify that all x components are less than 1, use ub =
ones(size(x0))
.
Data Types: single
 double
nonlcon
— Nonlinear constraints
function handle  function name
Nonlinear constraints, specified as a function handle or function
name. nonlcon
is a function that accepts a vector
or array x
and returns two arrays, c(x)
and ceq(x)
.
c(x)
is the array of nonlinear inequality constraints atx
.fmincon
attempts to satisfyc(x) <= 0
for all entries ofc
.ceq(x)
is the array of nonlinear equality constraints atx
.fmincon
attempts to satisfyceq(x) = 0
for all entries ofceq
.
For example,
x = fmincon(@myfun,x0,A,b,Aeq,beq,lb,ub,@mycon)
where mycon
is a MATLAB function such
as
function [c,ceq] = mycon(x) c = ... % Compute nonlinear inequalities at x. ceq = ... % Compute nonlinear equalities at x.
SpecifyConstraintGradient
option
is true
, as set byoptions = optimoptions('fmincon','SpecifyConstraintGradient',true)
nonlcon
must
also return, in the third and fourth output arguments, GC
,
the gradient of c(x)
, and GCeq
,
the gradient of ceq(x)
. GC
and GCeq
can
be sparse or dense. If GC
or GCeq
is
large, with relatively few nonzero entries, save running time and
memory in the interiorpoint
algorithm by representing
them as sparse matrices. For more information, see Nonlinear Constraints.
Data Types: char
 function_handle
 string
options
— Optimization options
output of optimoptions
 structure such as optimset
returns
Optimization options, specified as the output of
optimoptions
or a structure such as
optimset
returns.
Some options apply to all algorithms, and others are relevant for particular algorithms. See Optimization Options Reference for detailed information.
Some options are absent from the
optimoptions
display. These options appear in italics in the following
table. For details, see View Optimization Options.
All Algorithms  
Algorithm  Choose the optimization algorithm:
For information on choosing the algorithm, see Choosing the Algorithm. The
If you select the
The  
CheckGradients  Compare usersupplied
derivatives (gradients of objective or constraints) to
finitedifferencing derivatives. Choices are
For
The  
ConstraintTolerance  Tolerance on the constraint
violation, a nonnegative scalar. The default is
For  
Diagnostics  Display diagnostic
information about the function to be minimized or
solved. Choices are  
DiffMaxChange  Maximum change in variables
for finitedifference gradients (a positive scalar). The
default is  
DiffMinChange  Minimum change in variables
for finitedifference gradients (a positive scalar). The
default is  
Display  Level of display (see Iterative Display):
 
FiniteDifferenceStepSize 
Scalar or vector step size factor for finite differences. When
you set
sign′(x) = sign(x) except sign′(0) = 1 .
Central finite differences are
FiniteDifferenceStepSize expands to a vector. The default
is sqrt(eps) for forward finite differences, and eps^(1/3)
for central finite differences.
For  
FiniteDifferenceType  Finite differences, used to
estimate gradients, are either
For
 
FunValCheck  Check whether objective
function values are valid. The default setting,
 
MaxFunctionEvaluations  Maximum number of function
evaluations allowed, a nonnegative integer. The default
value for all algorithms except
For  
MaxIterations  Maximum number of iterations
allowed, a nonnegative integer. The default value for
all algorithms except For  
OptimalityTolerance  Termination tolerance on the firstorder optimality (a
nonnegative scalar). The default is For  
OutputFcn  Specify one or more
userdefined functions that an optimization function
calls at each iteration. Pass a function handle or a
cell array of function handles. The default is none
(  
PlotFcn  Plots various measures of
progress while the algorithm executes; select from
predefined plots or write your own. Pass a builtin plot
function name, a function handle, or a cell array of
builtin plot function names or function handles. For
custom plot functions, pass function handles. The
default is none (
Custom plot functions use the same syntax as output functions. See Output Functions for Optimization Toolbox and Output Function and Plot Function Syntax. For
 
SpecifyConstraintGradient  Gradient for nonlinear
constraint functions defined by the user. When set to
the default, For
 
SpecifyObjectiveGradient  Gradient for the objective
function defined by the user. See the description of
For
 
StepTolerance  Termination tolerance on
For  
TypicalX  Typical The
 
UseParallel  When  
TrustRegionReflective Algorithm  
FunctionTolerance  Termination tolerance on the
function value, a nonnegative scalar. The default is
For  
HessianFcn  If For  
HessianMultiplyFcn  Hessian multiply
function, specified as a function handle. For
largescale structured problems, this function computes
the Hessian matrix product W = hmfun(Hinfo,Y) where
The first
argument is the same as the third argument returned by
the objective function [f,g,Hinfo] = fun(x)
Note To use the See Hessian Multiply Function. See Minimization with Dense Structured Hessian, Linear Equalities for an example. For
 
HessPattern  Sparsity pattern of the
Hessian for finite differencing. Set
Use
When the structure is unknown, do
not set  
MaxPCGIter  Maximum number of
preconditioned conjugate gradient (PCG) iterations, a
positive scalar. The default is
 
PrecondBandWidth  Upper bandwidth of
preconditioner for PCG, a nonnegative integer. By
default, diagonal preconditioning is used (upper
bandwidth of 0). For some problems, increasing the
bandwidth reduces the number of PCG iterations. Setting
 
SubproblemAlgorithm  Determines how the iteration
step is calculated. The default,
For  
TolPCG  Termination tolerance on the
PCG iteration, a positive scalar. The default is
 
ActiveSet Algorithm  
FunctionTolerance  Termination tolerance on the
function value, a nonnegative scalar. The default is
For  
MaxSQPIter  Maximum number of SQP
iterations allowed, a positive integer. The default is
 
RelLineSrchBnd  Relative bound (a real
nonnegative scalar value) on the line search step
length. The total displacement in x
satisfies Δx(i) ≤ relLineSrchBnd·
max(x(i),typicalx(i)). This option provides control over the
magnitude of the displacements in x
for cases in which the solver takes steps that are
considered too large. The default is no bounds
(  
RelLineSrchBndDuration  Number of iterations for
which the bound specified in
 
TolConSQP  Termination tolerance on
inner iteration SQP constraint violation, a positive
scalar. The default is
 
InteriorPoint Algorithm  
BarrierParamUpdate  Specifies how
This option can affect the speed and convergence of the solver, but the effect is not easy to predict.  
EnableFeasibilityMode  When Feasibility
mode usually performs better when
 
HessianApproximation  Specifies how
Note To use For  
HessianFcn  If For  
HessianMultiplyFcn  Usersupplied function that gives a Hessiantimesvector product (see Hessian Multiply Function). Pass a function handle. Note To use the For  
HonorBounds  The default
For
 
InitBarrierParam  Initial barrier value, a
positive scalar. Sometimes it might help to try a value
above the default  
InitTrustRegionRadius  Initial radius of the trust region, a positive scalar. On badly scaled problems it might help to choose a value smaller than the default $$\sqrt{n}$$, where n is the number of variables.  
MaxProjCGIter  A tolerance (stopping
criterion) for the number of projected conjugate
gradient iterations; this is an inner iteration, not the
number of iterations of the algorithm. This positive
integer has a default value of
 
ObjectiveLimit  A tolerance (stopping
criterion) that is a scalar. If the objective function
value goes below  
ScaleProblem 
For
 
SubproblemAlgorithm  Determines how the iteration
step is calculated. The default,
For  
TolProjCG  A relative tolerance
(stopping criterion) for projected conjugate gradient
algorithm; this is for an inner iteration, not the
algorithm iteration. This positive scalar has a default
of  
TolProjCGAbs  Absolute tolerance (stopping
criterion) for projected conjugate gradient algorithm;
this is for an inner iteration, not the algorithm
iteration. This positive scalar has a default of
 
SQP and SQP Legacy Algorithms  
ObjectiveLimit  A tolerance (stopping
criterion) that is a scalar. If the objective function
value goes below  
ScaleProblem 
For
 
SinglePrecision Code Generation  
Algorithm  Must be
 
ConstraintTolerance  Tolerance on the constraint
violation, a nonnegative scalar. The default is
 
FiniteDifferenceStepSize  Scalar or vector step
size factor for finite differences. When you set
sign′(x) = sign(x)
except sign′(0) = 1 . Central finite
differences are
FiniteDifferenceStepSize expands
to a vector. The default is
sqrt(eps('single')) for forward
finite differences, and
eps('single')^(1/3) for central
finite differences.  
FiniteDifferenceType  Finite differences, used to
estimate gradients, are either
 
MaxFunctionEvaluations  Maximum number of function
evaluations allowed, a nonnegative integer. The default
value is  
MaxIterations  Maximum number of iterations
allowed, a nonnegative integer. The default value is
 
ObjectiveLimit  A tolerance (stopping
criterion) that is a scalar. If the objective function
value goes below  
OptimalityTolerance  Termination tolerance
on the firstorder optimality (a nonnegative scalar).
The default is  
ScaleProblem 
 
SpecifyConstraintGradient  Gradient for nonlinear
constraint functions defined by the user. When set to
the default,  
SpecifyObjectiveGradient  Gradient for the objective
function defined by the user. See the description of
 
StepTolerance  Termination tolerance on
 
TypicalX  Typical 
Example: options =
optimoptions('fmincon','SpecifyObjectiveGradient',true,'SpecifyConstraintGradient',true)
problem
— Problem structure
structure
Problem structure, specified as a structure with the following fields:
Field Name  Entry 

 Objective function 
 Initial point for x 
 Matrix for linear inequality constraints 
 Vector for linear inequality constraints 
 Matrix for linear equality constraints 
 Vector for linear equality constraints 
lb  Vector of lower bounds 
ub  Vector of upper bounds 
 Nonlinear constraint function 
 'fmincon' 
 Options created with optimoptions 
You must supply at least the objective
, x0
, solver
,
and options
fields in the problem
structure.
Data Types: struct
Output Arguments
x
— Solution
real vector  real array
Solution, returned as a real vector or real array. The size
of x
is the same as the size of x0
.
Typically, x
is a local solution to the problem
when exitflag
is positive. For information on
the quality of the solution, see When the Solver Succeeds.
fval
— Objective function value at solution
real number
Objective function value at the solution, returned as a real
number. Generally, fval
= fun(x)
.
exitflag
— Reason fmincon
stopped
integer
Reason fmincon
stopped, returned as an
integer.
All Algorithms:  
 Firstorder optimality measure was less than 
 Number of iterations exceeded 
 Stopped by an output function or plot function. 
 No feasible point was found. 
All algorithms except  
 Change in 
 
 Change in the objective function value was less than 
 
 Magnitude of the search direction was less than 2* 
 Magnitude of directional derivative in search direction
was less than 2* 
 
 Objective function at current iteration went below 
output
— Information about the optimization process
structure
Information about the optimization process, returned as a structure with fields:
iterations  Number of iterations taken 
funcCount  Number of function evaluations 
lssteplength  Size of line search step relative to search direction
( 
constrviolation  Maximum of constraint functions 
stepsize  Length of last displacement in 
algorithm  Optimization algorithm used 
cgiterations  Total number of PCG iterations ( 
firstorderopt  Measure of firstorder optimality 
bestfeasible  Best (lowest objective function) feasible point encountered. A structure with these fields:
If no feasible point is found, the
The

message  Exit message 
lambda
— Lagrange multipliers at the solution
structure
grad
— Gradient at the solution
real vector
Gradient at the solution, returned as a real vector. grad
gives
the gradient of fun
at the point x(:)
.
hessian
— Approximate Hessian
real matrix
Approximate Hessian, returned as a real matrix. For the meaning
of hessian
, see Hessian Output.
Limitations
fmincon
is a gradientbased method that is designed to work on problems where the objective and constraint functions are both continuous and have continuous first derivatives.For the
'trustregionreflective'
algorithm, you must provide the gradient infun
and set the'SpecifyObjectiveGradient'
option totrue
.The
'trustregionreflective'
algorithm does not allow equal upper and lower bounds. For example, iflb(2)==ub(2)
,fmincon
gives this error:Equal upper and lower bounds not permitted in trustregionreflective algorithm. Use either interiorpoint or SQP algorithms instead.
There are two different syntaxes for passing a Hessian, and there are two different syntaxes for passing a
HessianMultiplyFcn
function; one fortrustregionreflective
, and another forinteriorpoint
. See Including Hessians.For
trustregionreflective
, the Hessian of the Lagrangian is the same as the Hessian of the objective function. You pass that Hessian as the third output of the objective function.For
interiorpoint
, the Hessian of the Lagrangian involves the Lagrange multipliers and the Hessians of the nonlinear constraint functions. You pass the Hessian as a separate function that takes into account both the current pointx
and the Lagrange multiplier structurelambda
.
When the problem is infeasible,
fmincon
attempts to minimize the maximum constraint value.
More About
Hessian as an Input
fmincon
uses a Hessian
as an optional input. This Hessian is the matrix of second derivatives
of the Lagrangian (see Equation 1), namely,
$${\nabla}_{xx}^{2}L(x,\lambda )={\nabla}^{2}f(x)+{\displaystyle \sum {\lambda}_{i}{\nabla}^{2}{c}_{i}(x)}+{\displaystyle \sum {\lambda}_{i}{\nabla}^{2}ce{q}_{i}(x)}.$$  (3) 
For details of how to supply a Hessian to the trustregionreflective
or interiorpoint
algorithms,
see Including Hessians.
The activeset
and sqp
algorithms
do not accept an input Hessian. They compute a quasiNewton approximation
to the Hessian of the Lagrangian.
The interiorpoint
algorithm has several choices for the
'HessianApproximation'
option; see Choose Input Hessian Approximation for interiorpoint fmincon:
'bfgs'
—fmincon
calculates the Hessian by a dense quasiNewton approximation. This is the default Hessian approximation.'lbfgs'
—fmincon
calculates the Hessian by a limitedmemory, largescale quasiNewton approximation. The default memory, 10 iterations, is used.{'lbfgs',positive integer}
—fmincon
calculates the Hessian by a limitedmemory, largescale quasiNewton approximation. The positive integer specifies how many past iterations should be remembered.'finitedifference'
—fmincon
calculates a Hessiantimesvector product by finite differences of the gradient(s). You must supply the gradient of the objective function, and also gradients of nonlinear constraints (if they exist). Set the'SpecifyObjectiveGradient'
option totrue
and, if applicable, the'SpecifyConstraintGradient'
option totrue
. You must set the'SubproblemAlgorithm'
to'cg'
.
Hessian Multiply Function
The interiorpoint
and trustregionreflective
algorithms
allow you to supply a Hessian multiply function. This function gives
the result of a Hessiantimesvector product, without computing the
Hessian directly. This can save memory. For details, see Hessian Multiply Function.
Enhanced Exit Messages
The next few items list the possible enhanced exit messages from
fmincon
. Enhanced exit messages give a link for more
information as the first sentence of the message.
Local Minimum Found that Satisfies the Constraints
The solver located a point that seems to be a local minimum, since the point is feasible (satisfies constraints within the ConstraintTolerance tolerance) and the firstorder optimality measure is less than the OptimalityTolerance tolerance.
For suggestions on how to proceed, see When the Solver Succeeds.
Initial Point is a Local Minimum that Satisfies the Constraints
The initial point seems to be a local minimum, since the point is feasible (satisfies constraints within the ConstraintTolerance tolerance), and the firstorder optimality measure is less than the OptimalityTolerance tolerance.
For suggestions on how to proceed, see Final Point Equals Initial Point.
Local Minimum Possible, Constraints Satisfied
The solver may have reached a local minimum, but cannot be certain because the firstorder optimality measure is not less than the OptimalityTolerance tolerance. The constraints are satisfied to within the ConstraintTolerance constraint tolerance.
For suggestions on how to proceed, see Local Minimum Possible.
fmincon
Converged to an Infeasible Point
fmincon
converged to a point that does not satisfy all
constraints to within the constraint tolerance called ConstraintTolerance. The reason
fmincon
stopped is that the last step was too small.
When the relative step size goes below the StepTolerance tolerance, then
the iterations end.
For suggestions on how to proceed, see Converged to an Infeasible Point.
Solver Stopped Prematurely
The solver stopped because it reached a limit on the number of iterations or function evaluations before it minimized the objective to the requested tolerance.
For suggestions on how to proceed, see Too Many Iterations or Function Evaluations.
Problem Appears Unbounded
The solver reached a feasible point whose objective function value was less
than or equal to the ObjectiveLimit
tolerance. The problem
is unbounded, or poorly scaled, or the ObjectiveLimit
option
is too high.
For suggestions on how to proceed, see Problem Unbounded.
Feasible Point with Lower Objective Function Value Found
fmincon
encountered a feasible point with a lower objective
value than the final point. This includes the case where the final point is
infeasible, in which case the final objective function value is not relevant.
Feasible means that the maximum infeasibility is less than the
ConstraintTolerance
option.
The best feasible point is in the bestfeasible
field of the
output
structure. For an
example, see Obtain Best Feasible Point.
Definitions for Exit Messages
The next few items contain definitions for terms in the fmincon
exit messages.
local minimum
A local minimum of a function is a point where the function value is smaller than at nearby points, but possibly greater than at a distant point.
A global minimum is a point where the function value is smaller than at all other feasible points.
Solvers try to find a local minimum. The result can be a global minimum. For more information, see Local vs. Global Optima.
tolerance
Generally, a tolerance is a threshold which, if crossed, stops the iterations of a solver. For more information on tolerances, see Tolerances and Stopping Criteria.
ConstraintTolerance
The constraint tolerance called
ConstraintTolerance
is the maximum of the values of all
constraint functions at the current point.
ConstraintTolerance
operates differently from other tolerances.
If ConstraintTolerance
is not satisfied (i.e., if the magnitude
of the constraint function exceeds ConstraintTolerance
), the
solver attempts to continue, unless it is halted for another reason. A solver does
not halt simply because ConstraintTolerance
is satisfied.
Constraint Violation
The constraint violation is the maximum of the values of all constraint functions
at the current point. This is measured against the tolerance called
ConstraintTolerance
.
ConstraintTolerance
operates differently from other tolerances.
If ConstraintTolerance
is not satisfied (i.e., if the magnitude
of the constraint function exceeds ConstraintTolerance
), the
solver attempts to continue, unless it is halted for another reason. A solver does
not halt simply because ConstraintTolerance
is satisfied.
Feasible Directions
Feasible directions are those vectors from the current point that locally satisfy the constraints. They either point to the interior of the region where the constraints are satisfied, or are tangent to the boundary of binding constraints.
firstorder optimality measure
The first order optimality measure for constrained problems is the maximum of the following two quantities:
$$\begin{array}{l}\Vert {\nabla}_{x}L(x,\lambda )\Vert =\Vert \nabla f(x)+{A}^{T}{\lambda}_{ineqlin}+Ae{q}^{T}{\lambda}_{eqlin}\\ \text{}+{\displaystyle \sum {\lambda}_{ineqnonlin,i}\nabla {c}_{i}(x)+{\displaystyle \sum {\lambda}_{eqnonlin,i}\nabla ce{q}_{i}(x)}}\Vert ,\end{array}$$
$$\Vert \overrightarrow{\left{l}_{i}{x}_{i}\right{\lambda}_{lower,i}},\overrightarrow{\left{x}_{i}{u}_{i}\right{\lambda}_{upper,i}},\overrightarrow{\left{(Axb)}_{i}\right{\lambda}_{ineqlin,i}},\overrightarrow{\left{c}_{i}(x)\right{\lambda}_{ineqnonlin,i}}\Vert ,$$
For unconstrained problems, it is the maximum of the absolute value of the components of the gradient vector (also known as the infinity norm).
This should be zero at a minimizing point.
For more information, including definitions of all the variables in these equations, see FirstOrder Optimality Measure.
OptimalityTolerance
The tolerance called OptimalityTolerance
relates to the
firstorder optimality measure. Iterations end when the firstorder optimality
measure is less than OptimalityTolerance
. For more information,
see FirstOrder Optimality Measure.
Predicted Change in Objective Function
The predicted change in objective function is the amount the solver estimates the objective function would decrease if the current point were moved along the estimated best search direction. This estimated decrease is the inner product of the gradient of the objective at the current point with the search direction, times the step length. Optimization Toolbox™ solvers compute search directions via various algorithms, described in Constrained Nonlinear Optimization Algorithms.
Output or Plot Function
An output function (or plot function) is evaluated once per iteration of a solver. It can report many optimization quantities during the course of a solver's progress, and can halt the solver.
For more information, see Output Functions for Optimization Toolbox or Plot Functions.
MaxIterations
MaxIterations
is a tolerance on the number of iterations the solver performs. When the solver has taken MaxIterations
iterations, the iterations end.
For more information, see Iterations and Function Counts or Tolerances and Stopping Criteria.
MaxFunctionEvaluations
MaxFunctionEvaluations
is a tolerance on the number of points where the solver evaluates the objective and/or constraint functions. When the solver has evaluated functions at MaxFunctionEvaluations
points, the iterations end.
For more information, see Iterations and Function Counts or Tolerances and Stopping Criteria.
Objective Function Limit
The solver reached a feasible point whose objective function value was less
than or equal to the ObjectiveLimit
tolerance. The problem
is unbounded, or poorly scaled, or the ObjectiveLimit
option
is too high.
For suggestions on how to proceed, see Problem Unbounded.
MaxSQPIter
MaxSQPIter
is a tolerance on the number of
sequential quadratic programming subproblem iterations the solver performs. When the
solver has taken MaxSQPIter
iterations for the subproblem, the
subproblem iterations end.
For more information, see Sequential Quadratic Programming (SQP).
Relative Changes in All Elements of X
Relative changes in all elements of x is the normalized step vector. This vector is the change in location where the objective function was evaluated, divided by the infinity norm of the current position. If the maximum of this relative norm goes below the StepTolerance tolerance, then the iterations end.
Size of the Current Step
The size of the current step is the norm of the change in location where the
objective function was evaluated. In this case, fmincon
uses a
relative size: the step size divided by the infinity norm of the current position.
When this relative step size goes below the StepTolerance
tolerance, then the
iterations end.
StepTolerance
StepTolerance
is a tolerance for the size of
the last step, meaning the size of the change in location where the objective
function was evaluated.
Constraint Violation Locally Stationary
The constraint violations are the constraint functions that are not satisfied at the current point. The norm of the gradient of these functions is so small that the solver could not proceed. The current point is not feasible (some constraint violation exceeds the ConstraintTolerance tolerance).
For suggestions on how to proceed, see Converged to an Infeasible Point.
Norm of Search Direction
The search direction is the vector from the current point along which the solver looks for an improvement. The norm of this direction is the infinity norm, the maximum of the absolute values of the components of the search vector.
Optimization Toolbox solvers compute search directions via various algorithms, described in Constrained Nonlinear Optimization Algorithms.
Gradient Calculation Is Undefined
fmincon
estimates gradients of objective and nonlinear
constraint functions by taking finite differences. A finite difference calculation
stepped outside the region where a function is welldefined, returning
Inf
, NaN
, or a complex result.
For more information about how solvers compute and use gradients, see Constrained Nonlinear Optimization Algorithms. For suggestions on how to proceed, see 6. Provide Gradient or Jacobian.
Feasibility Mode
The fmincon
"interiorpoint"
algorithm can search for a feasible point using
a specialized algorithm. Enable this search by setting the
EnableFeasibilityMode
option to true
using
optimoptions
. For added efficiency with difficult problems,
set the SubproblemAlgorithm
option to
"cg"
:
options = optimoptions("fmincon",... Algorithm="interiorpoint",... EnableFeasibilityMode=true,... SubproblemAlgorithm="cg");
For details of the EnableFeasibilityMode
algorithm, see Feasibility Mode.
Algorithms
Choosing the Algorithm
For help choosing the algorithm, see fmincon Algorithms. To set the algorithm, use optimoptions
to create options
, and use the
'Algorithm'
namevalue pair.
The rest of this section gives brief summaries or pointers to information about each algorithm.
InteriorPoint Optimization
This algorithm is described in fmincon Interior Point Algorithm. There is more extensive description in [1], [41], and [9].
SQP and SQPLegacy Optimization
The fmincon
'sqp'
and 'sqplegacy'
algorithms
are similar to the 'activeset'
algorithm described
in ActiveSet Optimization. fmincon SQP Algorithm describes the main
differences. In summary, these differences are:
ActiveSet Optimization
fmincon
uses a sequential quadratic programming (SQP) method. In this
method, the function solves a quadratic
programming (QP) subproblem at each iteration. fmincon
updates
an estimate of the Hessian of the Lagrangian at each iteration using
the BFGS formula (see fminunc
and
references [7] and [8]).
fmincon
performs a line search using a
merit function similar to that proposed by [6], [7], and [8]. The QP subproblem is solved using
an active set strategy similar to that described in [5]. fmincon Active Set Algorithm describes this algorithm in
detail.
See also SQP Implementation for more details on the algorithm used.
TrustRegionReflective Optimization
The 'trustregionreflective'
algorithm is
a subspace trustregion method and is based on the interiorreflective
Newton method described in [3] and [4]. Each iteration involves the approximate
solution of a large linear system using the method of preconditioned
conjugate gradients (PCG). See the trustregion and preconditioned
conjugate gradient method descriptions in fmincon Trust Region Reflective Algorithm.
Alternative Functionality
App
The Optimize Live Editor task provides a visual interface for fmincon
.
References
[1] Byrd, R. H., J. C. Gilbert, and J. Nocedal. “A Trust Region Method Based on Interior Point Techniques for Nonlinear Programming.” Mathematical Programming, Vol 89, No. 1, 2000, pp. 149–185.
[2] Byrd, R. H., Mary E. Hribar, and Jorge Nocedal. “An Interior Point Algorithm for LargeScale Nonlinear Programming.” SIAM Journal on Optimization, Vol 9, No. 4, 1999, pp. 877–900.
[3] Coleman, T. F. and Y. Li. “An Interior, Trust Region Approach for Nonlinear Minimization Subject to Bounds.” SIAM Journal on Optimization, Vol. 6, 1996, pp. 418–445.
[4] Coleman, T. F. and Y. Li. “On the Convergence of Reflective Newton Methods for LargeScale Nonlinear Minimization Subject to Bounds.” Mathematical Programming, Vol. 67, Number 2, 1994, pp. 189–224.
[5] Gill, P. E., W. Murray, and M. H. Wright. Practical Optimization, London, Academic Press, 1981.
[6] Han, S. P. “A Globally Convergent Method for Nonlinear Programming.” Journal of Optimization Theory and Applications, Vol. 22, 1977, pp. 297.
[7] Powell, M. J. D. “A Fast Algorithm for Nonlinearly Constrained Optimization Calculations.” Numerical Analysis, ed. G. A. Watson, Lecture Notes in Mathematics, SpringerVerlag, Vol. 630, 1978.
[8] Powell, M. J. D. “The Convergence of Variable Metric Methods For Nonlinearly Constrained Optimization Calculations.” Nonlinear Programming 3 (O. L. Mangasarian, R. R. Meyer, and S. M. Robinson, eds.), Academic Press, 1978.
[9] Waltz, R. A., J. L. Morales, J. Nocedal, and D. Orban. “An interior algorithm for nonlinear optimization that combines line search and trust region steps.” Mathematical Programming, Vol 107, No. 3, 2006, pp. 391–408.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
fmincon
supports code generation using either thecodegen
(MATLAB Coder) function or the MATLAB Coder™ app. You must have a MATLAB Coder license to generate code.The target hardware must support standard doubleprecision floatingpoint computations or standard singleprecision floatingpoint computations.
Code generation targets do not use the same math kernel libraries as MATLAB solvers. Therefore, code generation solutions can vary from solver solutions, especially for poorly conditioned problems.
All code for generation must be MATLAB code. In particular, you cannot use a custom blackbox function as an objective function for
fmincon
. You can usecoder.ceval
to evaluate a custom function coded in C or C++. However, the custom function must be called in a MATLAB function.fmincon
does not support theproblem
argument for code generation.[x,fval] = fmincon(problem) % Not supported
You must specify the objective function and any nonlinear constraint function by using function handles, not strings or character names.
x = fmincon(@fun,x0,A,b,Aeq,beq,lb,ub,@nonlcon) % Supported % Not supported: fmincon('fun',...) or fmincon("fun",...)
All
fmincon
input matrices such asA
,Aeq
,lb
, andub
must be full, not sparse. You can convert sparse matrices to full by using thefull
function.The
lb
andub
arguments must have the same number of entries as thex0
argument or must be empty[]
.If your target hardware does not support infinite bounds, use
optim.coder.infbound
.For advanced code optimization involving embedded processors, you also need an Embedded Coder^{®} license.
You must include options for
fmincon
and specify them usingoptimoptions
. The options must include theAlgorithm
option, set to'sqp'
or'sqplegacy'
.options = optimoptions('fmincon','Algorithm','sqp'); [x,fval,exitflag] = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options);
Code generation supports these options:
Algorithm
— Must be'sqp'
or'sqplegacy'
ConstraintTolerance
FiniteDifferenceStepSize
FiniteDifferenceType
MaxFunctionEvaluations
MaxIterations
ObjectiveLimit
OptimalityTolerance
ScaleProblem
SpecifyConstraintGradient
SpecifyObjectiveGradient
StepTolerance
TypicalX
Generated code has limited error checking for options. The recommended way to update an option is to use
optimoptions
, not dot notation.opts = optimoptions('fmincon','Algorithm','sqp'); opts = optimoptions(opts,'MaxIterations',1e4); % Recommended opts.MaxIterations = 1e4; % Not recommended
Do not load options from a file. Doing so can cause code generation to fail. Instead, create options in your code.
Usually, if you specify an option that is not supported, the option is silently ignored during code generation. However, if you specify a plot function or output function by using dot notation, code generation can issue an error. For reliability, specify only supported options.
Because output functions and plot functions are not supported,
fmincon
does not return the exit flag –1.Code generated from
fmincon
does not contain thebestfeasible
field in a returnedoutput
structure.
For an example, see Code Generation for Optimization Basics.
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, set the 'UseParallel'
option to true
.
options = optimoptions('
solvername
','UseParallel',true)
For more information, see Using Parallel Computing in Optimization Toolbox.
Version History
Introduced before R2006aR2024a: Singleprecision code generation
You can generate code for fmincon
on singleprecision
floating point hardware. For instructions, see SinglePrecision Code Generation.
R2023b: CheckGradients
option will be removed
The CheckGradients
option will be removed in a future release. To check the first derivatives of objective functions or nonlinear constraint functions, use the checkGradients
function.
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