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FindArgMin

FindArgMin
gives the position of a local minimum of f.
FindArgMin
gives the position of a local minimum of f, found by a search starting from the point .
FindArgMin
gives the position of a local minimum of a function of several variables.
FindArgMin
gives the position of a local minimum subject to the constraints cons.
FindArgMin
starts from a point within the region defined by the constraints.
  • 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.
  • FindArgMin first localizes the values of all variables, then evaluates f with the variables being symbolic, and then repeatedly evaluates the result numerically.
  • FindArgMin searches for a local minimum in f using and as the first two values of x, avoiding the use of derivatives.
  • FindArgMin searches for a local minimum, stopping the search if x ever gets outside the range to .
  • Except when f and cons are both linear, the results found by FindArgMin may correspond only to local, but not global, minima.
  • By default, all variables are assumed to be real.
  • For linear f and cons, xIntegers can be used to specify that a variable can take on only integer values.
Find a point at which the univariate function has a minimum:
Find a point at which the function Sin[x]Sin[2y] has a minimum:
Find a point at which a function is a minimum subject to constraints:
Find a point at which the univariate function has a minimum:
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Find a point at which the function Sin[x]Sin[2y] has a minimum:
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Find a point at which a function is a minimum subject to constraints:
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With different starting points, get the locations of different local minima:
Location of a local minimum of a two-variable function starting from , :
Location of 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:
This enforces convergence criteria and :
This enforces convergence criteria and :
Setting a high WorkingPrecision makes the process convergent:
Plot convergence to the local minimum:
Use a given gradient; the Hessian is computed automatically:
Supply both gradient and Hessian:
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:
Steps taken by FindArgMin in finding the minimum of a function:
Set the working precision to ; by default AccuracyGoal and PrecisionGoal are set to :
FindMinimum gives both the value of the minimum and the minimizer point:
FindArgMin gives the location of the minimum:
FindMinValue gives the value at the minimum:
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:
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