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Minimize [f,x]

minimizes f symbolically with respect to x.

Minimize [f,{x,y,}]

minimizes f symbolically with respect to x, y, .

Minimize [{f,cons},{x,y,}]

minimizes f symbolically subject to the constraints cons.

Minimize [,xrdom]

constrains x to be in the region or domain rdom.

Minimize [,,dom]

constrains variables to the domain dom, typically Reals or Integers .

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Basic Uses  
Univariate Problems  
Multivariate Problems  
Parametric Problems  
Optimization over Integers  
Optimization over Regions  
Options  
WorkingPrecision  
Applications  
Basic Applications  
Geometric Distances  
Geometric Centers  
Properties & Relations  
Possible Issues  
See Also
Tech Notes
Related Guides
History
Cite this Page

Minimize

Minimize [f,x]

minimizes f symbolically with respect to x.

Minimize [f,{x,y,}]

minimizes f symbolically with respect to x, y, .

Minimize [{f,cons},{x,y,}]

minimizes f symbolically subject to the constraints cons.

Minimize [,xrdom]

constrains x to be in the region or domain rdom.

Minimize [,,dom]

constrains variables to the domain dom, typically Reals or Integers .

Details and Options

  • Minimize is also known as infimum, symbolic optimization and global optimization (GO).
  • Minimize finds the global minimum of f subject to the constraints given.
  • Minimize is typically used to find the smallest possible values given constraints. In different areas, this may be called the best strategy, best fit, best configuration and so on.
  • Minimize returns a list of the form {fmin,{x->xmin,y->ymin,}}.
  • If f and cons are linear or polynomial, Minimize will always find a global minimum.
  • The constraints cons can be any logical combination of:
  • lhs==rhs equations
    lhs>rhs, lhsrhs, lhs<rhs, lhsrhs inequalities (LessEqual ,)
    lhsrhs, lhsrhs, lhsrhs, lhsrhs vector inequalities (VectorLessEqual ,)
    Exists [], ForAll [] quantified conditions
    {x,y,}rdom region or domain specification
  • Minimize [{f,cons},xrdom] is effectively equivalent to Minimize [{f,consxrdom},x].
  • For xrdom, the different coordinates can be referred to using Indexed [x,i].
  • Possible domains rdom include:
  • Reals real scalar variable
    Integers integer scalar variable
    Vectors [n,dom] vector variable in
    Matrices [{m,n},dom] matrix variable in
    vector variable restricted to the geometric region
  • By default, all variables are assumed to be real.
  • Minimize will return exact results if given exact input. With approximate input, it automatically calls NMinimize .
  • Minimize will return the following forms:
  • {fmin,{xxmin,}} finite minimum
    {,{xIndeterminate ,}} infeasible, i.e. the constraint set is empty
    {-,{xxmin,}} unbounded, i.e. the values of f can be arbitrarily small
  • If the minimum is achieved only infinitesimally outside the region defined by the constraints, or only asymptotically, Minimize will return the infimum and the closest specifiable point.
  • Even if the same minimum is achieved at several points, only one is returned.
  • N [Minimize[]] calls NMinimize for optimization problems that cannot be solved symbolically.
  • Minimize [f,x,WorkingPrecision->n] uses n digits of precision while computing a result. »

Examples

open all close all

Basic Examples  (5)

Minimize a univariate function:

Minimize a multivariate function:

Minimize a function subject to constraints:

A minimization problem containing parameters:

Minimize a function over a geometric region:

Plot it:

Scope  (36)

Basic Uses  (7)

Minimize over the unconstrained reals:

Minimize subject to constraints :

Constraints may involve arbitrary logical combinations:

An unbounded problem:

An infeasible problem:

The infimum value may not be attained:

Use a vector variable and a vector inequality:

Univariate Problems  (7)

Unconstrained univariate polynomial minimization:

Constrained univariate polynomial minimization:

Exp-log functions:

Analytic functions over bounded constraints:

Periodic functions:

Combination of trigonometric functions with commensurable periods:

Combination of periodic functions with incommensurable periods:

Piecewise functions:

Unconstrained problems solvable using function property information:

Multivariate Problems  (9)

Multivariate linear constrained minimization:

Linear-fractional constrained minimization:

Unconstrained polynomial minimization:

Constrained polynomial optimization can always be solved:

The minimum value may not be attained:

The objective function may be unbounded:

There may be no points satisfying the constraints:

Quantified polynomial constraints:

Algebraic minimization:

Bounded transcendental minimization:

Piecewise minimization:

Convex minimization:

Minimize convex objective function such that is positive semidefinite and :

Plot the region and the minimizing point:

Parametric Problems  (4)

Parametric linear optimization:

The minimum value is a continuous function of parameters:

Parametric quadratic optimization:

The minimum value is a continuous function of parameters:

Unconstrained parametric polynomial minimization:

Constrained parametric polynomial minimization:

Optimization over Integers  (3)

Univariate problems:

Integer linear programming:

Polynomial minimization over the integers:

Optimization over Regions  (6)

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

Plot it:

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  (1)

WorkingPrecision  (1)

Finding the exact solution takes a long time:

With WorkingPrecision->100, you get an exact minimum value, but it might be incorrect:

Applications  (10)

Basic Applications  (3)

Find the minimal perimeter among rectangles with a unit area:

Find the minimal perimeter among triangles with a unit area:

The minimal perimeter triangle is equilateral:

Find the distance to a parabola from a point on its axis:

Assuming a particular relationship between the and parameters:

Geometric Distances  (6)

The shortest distance of a point in a region to a given point p and a point q realizing the shortest distance is given by Minimize [EuclideanDistance [p,q],q]. Find the shortest distance and the nearest point to {1,1} in the unit Disk []:

Plot it:

Find the shortest distance and the nearest point to {1,3/4} in the standard unit simplex Simplex [2]:

Plot it:

Find the shortest distance and the nearest point to {1,1,1} in the standard unit sphere Sphere []:

Plot it:

Find the shortest distance and the nearest point to {-1/3,1/3,1/3} in the standard unit simplex Simplex [3]:

Plot it:

The nearest points p and q and their distance can be found through Minimize [EuclideanDistance [p,q],{p,q}]. Find the nearest points in Disk [{0,0}] and Rectangle [{3,3}] and the distance between them:

Plot it:

Find the nearest points in Line [{{0,0,0},{1,1,1}}] and Ball [{5,5,0},1] and the distance between them:

Plot it:

Geometric Centers  (1)

If n is a region that is full dimensional, then the Chebyshev center is the center of the largest inscribed ball of . The center and the radius of the largest inscribed ball of can be found through Minimize [SignedRegionDistance [,p], p]. Find the Chebyshev center and the radius of the largest inscribed ball for Rectangle []:

Find the Chebyshev center and the radius of the largest inscribed ball for Triangle []:

Properties & Relations  (6)

Minimize gives an exact global minimum of the objective function:

NMinimize attempts to find a global minimum numerically, but may find a local minimum:

FindMinimum finds local minima depending on the starting point:

The minimum point satisfies the constraints, unless messages say otherwise:

The given point minimizes the distance from the point {2,}:

When the minimum is not attained, Minimize may give a point on the boundary:

Here the objective function tends to the minimum value when y tends to infinity:

Minimize can solve linear programming problems:

LinearProgramming can be used to solve the same problem given in matrix notation:

This computes the minimum value:

Use RegionDistance and RegionNearest to compute the distance and the nearest point:

Both can be computed using Minimize :

Use RegionBounds to compute the bounding box:

Use Maximize and Minimize to compute the same bounds:

Possible Issues  (1)

Minimize requires that all functions present in the input be real-valued:

Values for which the equation is satisfied but the square roots are not real are disallowed:

History

Introduced in 2003 (5.0) | Updated in 2014 (10.0) 2021 (12.3)

Wolfram Research (2003), Minimize, Wolfram Language function, https://reference.wolfram.com/language/ref/Minimize.html (updated 2021).

Text

Wolfram Research (2003), Minimize, Wolfram Language function, https://reference.wolfram.com/language/ref/Minimize.html (updated 2021).

CMS

Wolfram Language. 2003. "Minimize." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2021. https://reference.wolfram.com/language/ref/Minimize.html.

APA

Wolfram Language. (2003). Minimize. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/Minimize.html

BibTeX

@misc{reference.wolfram_2025_minimize, author="Wolfram Research", title="{Minimize}", year="2021", howpublished="\url{https://reference.wolfram.com/language/ref/Minimize.html}", note=[Accessed: 04-January-2026]}

BibLaTeX

@online{reference.wolfram_2025_minimize, organization={Wolfram Research}, title={Minimize}, year={2021}, url={https://reference.wolfram.com/language/ref/Minimize.html}, note=[Accessed: 04-January-2026]}

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