Numeric Optimization
Functionality for numeric optimization has been greatly improved and
now rivals or exceeds equivalent functions in many dedicated
optimization packages.
The new function FindMaximum has been added to complement FindMinimum for convenience in obtaining
local optimization. Both now support array variables, offer new and
improved algorithms, and work better in limited-memory situations.
The global optimization function NMinimize has been expanded and moved
from a standard package into the Mathematica kernel. Advanced documentation for NMinimize is
now included, giving details of implementation and algorithms.
Here we minimize  inside the unit disk ![x^2 + y^2 [LessThanOrEqualTo] 1](images/findminmax_2.gif "x^2 + y^2 [LessThanOrEqualTo] 1") .
![NMinimize[{(1 - x)^2 + 100 (y - x^2)^2, x^2 + y^2≤ [LessThanOrEqualTo] 1}, {x, y}]](images/findminmax_3.gif "NMinimize[{(1 - x)^2 + 100 (y - x^2)^2, x^2 + y^2≤ [LessThanOrEqualTo] 1}, {x, y}]")
The red dot shows the position of the minimum on a representation of
the constraint and the function, shaded by height.
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