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Often it is useful to find the minimum value of a function rather than just
the zeroes where it crosses the x-axis. `fminbnd`

is designed for the
simpler, but very common, case of a univariate function where the interval
to search is bounded. For unbounded minimization of a function with
potentially many variables use `fminunc`

or `fminsearch`

. The two
functions use different internal algorithms and some knowledge of the objective
function is required. For functions which can be differentiated,
`fminunc`

is appropriate. For functions with discontinuities, or for
which a gradient search would fail, use `fminsearch`

.
See Optimization, for minimization with the presence of constraint
functions. Note that searches can be made for maxima by simply inverting the
objective function
(`Fto_max = -Fto_min`

).

- :
*[*`x`,`fval`,`info`,`output`] =**fminbnd***(*`fun`,`a`,`b`,`options`) Find a minimum point of a univariate function.

`fun`should be a function handle or name.`a`,`b`specify a starting interval.`options`is a structure specifying additional options. Currently,`fminbnd`

recognizes these options:`"FunValCheck"`

,`"OutputFcn"`

,`"TolX"`

,`"MaxIter"`

,`"MaxFunEvals"`

. For a description of these options, see optimset.On exit, the function returns

`x`, the approximate minimum point and`fval`, the function value thereof.`info`is an exit flag that can have these values:- 1 The algorithm converged to a solution.
- 0 Maximum number of iterations or function evaluations has been exhausted.
- -1 The algorithm has been terminated from user output function.

Notes: The search for a minimum is restricted to be in the interval bound by

`a`and`b`. If you only have an initial point to begin searching from you will need to use an unconstrained minimization algorithm such as`fminunc`

or`fminsearch`

.`fminbnd`

internally uses a Golden Section search strategy.**See also:**fzero, fminunc, fminsearch, optimset.

- :
**fminunc***(*`fcn`,`x0`) - :
**fminunc***(*`fcn`,`x0`,`options`) - :
*[*`x`,`fval`,`info`,`output`,`grad`,`hess`] =**fminunc***(*`fcn`, …) Solve an unconstrained optimization problem defined by the function

`fcn`.`fcn`should accept a vector (array) defining the unknown variables, and return the objective function value, optionally with gradient.`fminunc`

attempts to determine a vector`x`such that

is a local minimum.`fcn`(`x`)`x0`determines a starting guess. The shape of`x0`is preserved in all calls to`fcn`, but otherwise is treated as a column vector.`options`is a structure specifying additional options. Currently,`fminunc`

recognizes these options:`"FunValCheck"`

,`"OutputFcn"`

,`"TolX"`

,`"TolFun"`

,`"MaxIter"`

,`"MaxFunEvals"`

,`"GradObj"`

,`"FinDiffType"`

,`"TypicalX"`

,`"AutoScaling"`

.If

`"GradObj"`

is`"on"`

, it specifies that`fcn`, when called with two output arguments, also returns the Jacobian matrix of partial first derivatives at the requested point.`TolX`

specifies the termination tolerance for the unknown variables`x`, while`TolFun`

is a tolerance for the objective function value`fval`. The default is`1e-7`

for both options.For a description of the other options, see

`optimset`

.On return,

`x`is the location of the minimum and`fval`contains the value of the objective function at`x`.`info`may be one of the following values:- 1
Converged to a solution point. Relative gradient error is less than specified by

`TolFun`

.- 2
Last relative step size was less than

`TolX`

.- 3
Last relative change in function value was less than

`TolFun`

.- 0
Iteration limit exceeded—either maximum number of algorithm iterations

`MaxIter`

or maximum number of function evaluations`MaxFunEvals`

.- -1
Algorithm terminated by

`OutputFcn`

.- -3
The trust region radius became excessively small.

Optionally,

`fminunc`

can return a structure with convergence statistics (`output`), the output gradient (`grad`) at the solution`x`, and approximate Hessian (`hess`) at the solution`x`.Application Notes: If the objective function is a single nonlinear equation of one variable then using

`fminbnd`

is usually a better choice.The algorithm used by

`fminunc`

is a gradient search which depends on the objective function being differentiable. If the function has discontinuities it may be better to use a derivative-free algorithm such as`fminsearch`

.**See also:**fminbnd, fminsearch, optimset.

- :
`x`=**fminsearch***(*`fun`,`x0`) - :
`x`=**fminsearch***(*`fun`,`x0`,`options`) - :
*[*`x`,`fval`] =**fminsearch***(…)* -
Find a value of

`x`which minimizes the function`fun`.The search begins at the point

`x0`and iterates using the Nelder & Mead Simplex algorithm (a derivative-free method). This algorithm is better-suited to functions which have discontinuities or for which a gradient-based search such as`fminunc`

fails.Options for the search are provided in the parameter

`options`using the function`optimset`

. Currently,`fminsearch`

accepts the options:`"TolX"`

,`"MaxFunEvals"`

,`"MaxIter"`

,`"Display"`

. For a description of these options, see`optimset`

.On exit, the function returns

`x`, the minimum point, and`fval`, the function value thereof.Example usages:

fminsearch (@(x) (x(1)-5).^2+(x(2)-8).^4, [0;0]) fminsearch (inline ("(x(1)-5).^2+(x(2)-8).^4", "x"), [0;0])

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