FindMinValue

FindMinValue[f,x]

gives the value at a local minimum of f.

FindMinValue[f,{x,x0}]

gives the value at a local minimum of f, found by a search starting from the point x=x0.

FindMinValue[f,{{x,x0},{y,y0},…}]

gives the value at a local minimum of a function of several variables.

FindMinValue[{f,cons},{{x,x0},{y,y0},…}]

gives the value at a local minimum subject to the constraints cons.

FindMinValue[{f,cons},{x,y,…}]

starts from a point within the region defined by the constraints.

Details and Options

  • FindMinValue[…] is effectively equivalent to First[FindMinimum[…]].
  • If the starting point for a variable is given as a list, the values of the variable are taken to be lists with the same dimensions.
  • cons can contain equations, inequalities or logical combinations of these.
  • The constraints cons can be any logical combination of:
  • lhs==rhsequations
    lhs>rhs or lhs>=rhs inequalities
    {x,y,…}∈regregion specification
  • FindMinValue first localizes the values of all variables, then evaluates f with the variables being symbolic, and then repeatedly evaluates the result numerically.
  • FindMinValue has attribute HoldAll, and effectively uses Block to localize variables.
  • FindMinValue[f,{x,x0,x1}] searches for a local minimum in f using x0 and x1 as the first two values of x, avoiding the use of derivatives.
  • FindMinValue[f,{x,x0,xmin,xmax}] searches for a local minimum, stopping the search if x ever gets outside the range xmin to xmax.
  • Except when f and cons are both linear, the results found by FindMinValue may correspond only to local, but not global, minima.
  • By default, all variables are assumed to be real.
  • For linear f and cons, x∈Integers can be used to specify that a variable can take on only integer values.
  • FindMinValue takes the same options as FindMinimum.

Examples

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Basic Examples  (4)

Find a minimum value of the univariate function:

Find a minimum value of a multivariate function:

Find a minimum value of a function subject to constraints:

Find a minimum value of a function over a geometric region:

Scope  (12)

With different starting points, get the values of different local minima:

Value at a local minimum of a two-variable function starting from x=2, y=2:

Value at a local minimum constrained within a disk:

Starting point does not have to be provided:

For linear objective and constraints, integer constraints can be imposed:

Or constraints can be specified:

Find the minimum value of a function over a geometric region:

Plot it:

Find the minimum distance between two regions:

Plot it:

Find the minimum such that the triangle and ellipse still intersect:

Plot it:

Find the minimum radius of a disk that contains the given three points:

Using Circumsphere gives the same result directly:

Use to specify that is a vector in :

Find the minimum distance between two regions:

Plot it:

Options  (8)

AccuracyGoal & PrecisionGoal  (2)

This enforces convergence criteria and :

This enforces convergence criteria and :

Setting a high WorkingPrecision makes the process convergent:

EvaluationMonitor  (1)

Plot convergence to the local minimum:

Gradient  (2)

Use the given gradient:

Supply both gradient and Hessian:

Method  (1)

In this case, the default derivative-based methods have difficulties:

Direct search methods that do not require derivatives can be helpful in these cases:

NMinimize also uses a range of direct search methods:

StepMonitor  (1)

Steps taken by FindMinValue in finding the minimum of a function:

WorkingPrecision  (1)

Set the working precision to ; by default, AccuracyGoal and PrecisionGoal are set to :

Properties & Relations  (1)

FindMinimum gives both the value of the minimum and the minimizing argument:

FindArgMin gives the location of the minimum as a list:

FindMinValue gives the value at the minimum:

Possible Issues  (4)

If the constraint region is empty, the algorithm will not converge:

If the minimum value is not finite, the algorithm will not converge:

Integer linear programming algorithm is only available for machine-number problems:

Sometimes providing a suitable starting point can help the algorithm to converge:

Wolfram Research (2008), FindMinValue, Wolfram Language function, https://reference.wolfram.com/language/ref/FindMinValue.html (updated 2014).

Text

Wolfram Research (2008), FindMinValue, Wolfram Language function, https://reference.wolfram.com/language/ref/FindMinValue.html (updated 2014).

CMS

Wolfram Language. 2008. "FindMinValue." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2014. https://reference.wolfram.com/language/ref/FindMinValue.html.

APA

Wolfram Language. (2008). FindMinValue. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/FindMinValue.html

BibTeX

@misc{reference.wolfram_2022_findminvalue, author="Wolfram Research", title="{FindMinValue}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/FindMinValue.html}", note=[Accessed: 02-June-2023 ]}

BibLaTeX

@online{reference.wolfram_2022_findminvalue, organization={Wolfram Research}, title={FindMinValue}, year={2014}, url={https://reference.wolfram.com/language/ref/FindMinValue.html}, note=[Accessed: 02-June-2023 ]}