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{{About|the mathematical property|the peninsula in the Antarctic|Saddle Point|the type of landform and general uses of the word Saddle used as a technical term|Saddle (landform)}}
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[[File:Saddle point.svg|thumb|right|300px|A saddle point (in red) on the graph of z=x<sup>2</sup>−y<sup>2</sup> ([[Paraboloid#Hyperbolic_paraboloid|hyperbolic paraboloid]])]]
[[File:Saddle_Point_between_maxima.svg|thumb|300px|right|Saddle point between two hills (the intersection of the figure-eight <math>z</math>-contour)]]
In [[mathematics]], a '''saddle point''' or '''minimax point'''<ref>Howard Anton, Irl Bivens, Stephen Davis (2002): ''Calculus, Multivariable Version'', p. 844</ref> is a point on the [[surface (mathematics)|surface]] of the [[graph of a function|graph]] of a [[function (mathematics)|function]] where the slopes (derivatives) in orthogonal directions are both zero (a [[Critical point (mathematics)|critical point]]), but which is not a [[local extremum]] of the function.<ref>{{cite book |last=Chiang |first=Alpha C. |title=Fundamental Methods of Mathematical Economics |location=New York |publisher=McGraw-Hill |edition=3rd |year=1984 |isbn=0-07-010813-7 |page=312 }}</ref> An example of a saddle point shown on the right is when there is a critical point with a relative [[minimum]] along one axial direction (between peaks) and at a [[maxima and minima|relative maximum]] along the crossing axis. However, a saddle point need not be in this form. For example, the function <math>f(x,y) = x^2 + y^3</math> has a critical point at <math>(0, 0)</math> that is a saddle point since it is neither a relative maximum nor relative minimum, but it does not have a relative maximum or relative minimum in the <math>y</math>-direction.
The name derives from the fact that the prototypical example in two dimensions is a [[surface (mathematics)|surface]] that ''curves up'' in one direction, and ''curves down'' in a different direction, resembling a riding [[saddle]] or a [[mountain pass]] between two peaks forming a [[Saddle (landform)|landform saddle]]. In terms of [[contour line]]s, a saddle point in two dimensions gives rise to a contour graph or trace in which the contour corresponding to the saddle point's value appears to intersect itself.

== Mathematical discussion ==

A simple criterion for checking if a given stationary point of a real-valued function ''F''(''x'',''y'') of two real variables is a saddle point is to compute the function's [[Hessian matrix]] at that point: if the Hessian is [[Positive-definite matrix#Indefinite|indefinite]], then that point is a saddle point. For example, the Hessian matrix of the function <math>z=x^2-y^2</math> at the stationary point <math>(x, y, z)=(0, 0, 0)</math> is the matrix
: <math>\begin{bmatrix}
2 & 0\\
0 & -2 \\
\end{bmatrix}
</math>
which is indefinite. Therefore, this point is a saddle point. This criterion gives only a sufficient condition. For example, the point <math>(0, 0, 0)</math> is a saddle point for the function <math>z=x^4-y^4,</math> but the Hessian matrix of this function at the origin is the null matrix, which is not indefinite.

In the most general terms, a '''saddle point''' for a [[smooth function]] (whose [[graph of a function|graph]] is a [[curve]], [[surface (mathematics)|surface]] or [[hypersurface]]) is a stationary point such that the curve/surface/etc. in the [[neighborhood (mathematics)|neighborhood]] of that point is not entirely on any side of the [[tangent space]] at that point.
[[File:x cubed plot.svg|thumb|150px|The plot of ''y''&nbsp;=&nbsp;''x''<sup>3</sup> with a saddle point at 0]]

In a domain of one dimension, a saddle point is a [[Point (geometry)|point]] which is both a [[stationary point]] and a [[Inflection point|point of inflection]]. Since it is a point of inflection, it is not a [[local extremum]].

== Saddle surface {{anchor|Surface}} ==
[[Image:HyperbolicParaboloid.svg|thumb|right|[[Hyperbolic paraboloid]]]]
[[Image:Ruled hyperboloid.jpg|thumb|right|A model of an [[elliptic hyperboloid]] of one sheet]]
[[Image:Monkey_saddle_surface.svg|thumb|right|A [[monkey saddle]]|300px]]

A '''saddle surface''' is a [[smooth surface]] containing one or more saddle points.

Classical examples of two-dimensional saddle surfaces in the [[Euclidean space]] are second order surfaces, the [[hyperbolic paraboloid]] <math>z=x^2-y^2</math> (which is often referred to as "''the'' saddle surface" or "the standard saddle surface") and the [[hyperboloid of one sheet]]. The [[Pringles]] potato chip or crisp is an everyday example of a hyperbolic paraboloid shape.

Saddle surfaces have negative [[Gaussian curvature]] which distinguish them from convex/elliptical surfaces which have positive Gaussian curvature. A classical third-order saddle surface is the [[monkey saddle]].<ref>{{cite book |first=R. Creighton |last=Buck |year=2003 |title=Advanced Calculus |location= Long Grove, IL |publisher=Waveland Press |edition=Third |isbn=1-57766-302-0 |page=160 |url=https://books.google.com/books?id=7cYQAAAAQBAJ&pg=PA160 }}</ref>

==Examples==

In a two-player [[zero-sum (game theory)|zero sum]] game defined on a continuous space, the [[Nash equilibrium|equilibrium]] point is a saddle point.

For a second-order linear autonomous system, a [[critical point (mathematics)|critical point]] is a saddle point if the [[Characteristic equation (calculus)|characteristic equation]] has one positive and one negative real eigenvalue.<ref>{{harvnb|von Petersdorff|2006}}</ref>

In optimization subject to equality constraints, the first-order conditions describe a saddle point of the [[Lagrange multiplier|Lagrangian]].

== Other uses ==

In [[dynamical systems]], if the dynamic is given by a [[differentiable map]] ''f'' then a point is hyperbolic if and only if the differential of ''&fnof;'' <sup>''n''</sup> (where ''n'' is the period of the point) has no eigenvalue on the (complex) [[unit circle]] when computed at the point. Then
a ''saddle point'' is a hyperbolic [[periodic point]] whose [[stable manifold|stable]] and [[unstable manifold]]s have a [[dimension]] that is not zero.

A saddle point of a matrix is an element which is both the largest element in its column and the smallest element in its row.

== See also ==
* [[Saddle-point method]] is an extension of [[Laplace's method]] for approximating integrals
* [[Extremum]]
* [[Derivative test]]
* [[Hyperbolic equilibrium point]]
* [[Minimax theorem]]
* [[Max–min inequality]]
* [[Monkey saddle]]

== Notes ==
<references/>

== References ==

* {{citation |last1=Gray |first1=Lawrence F.|last2=Flanigan|first2=Francis J.|last3=Kazdan|first3=Jerry L.|last4=Frank|first4=David H|last5=Fristedt|first5=Bert |title=Calculus two: linear and nonlinear functions |publisher=Springer-Verlag |location=Berlin |year=1990 |page= 375|isbn=0-387-97388-5 |oclc= |doi=}}
* {{Citation | last1=Hilbert | first1=David | author1-link=David Hilbert | last2=Cohn-Vossen | first2=Stephan | author2-link=Stephan Cohn-Vossen | title=Geometry and the Imagination | publisher=Chelsea | location=New York | edition=2nd | isbn=978-0-8284-1087-8 | year=1952 }}
* {{citation|first=Tobias|last=von Petersdorff|url=http://www2.math.umd.edu/~petersd/246/stab.html|chapter=Critical Points of Autonomous Systems|year=2006|title=Differential Equations for Scientists and Engineers (Math 246 lecture notes)}}
* {{citation |last=Widder|first=D. V. |title=Advanced calculus |publisher=Dover Publications |location=New York |year=1989 |page=128 |isbn=0-486-66103-2 |oclc= |doi=}}
* {{citation |last=Agarwal|first=A. |title=Study on the Nash Equilibrium (Lecture Notes)|url=http://www.cse.iitd.ernet.in/~rahul/cs905/lecture3/nash1.html#SECTION00041000000000000000}}

== Further reading ==
* {{cite book
| author = [[David Hilbert|Hilbert, David]]; Cohn-Vossen, Stephan
| title = Geometry and the Imagination
| edition = 2nd
| year = 1952
| publisher = Chelsea
| isbn = 0-8284-1087-9}}

{{Commons category|Saddle point}}

{{DEFAULTSORT:Saddle Point}}
[[Category:Differential geometry of surfaces]]
[[Category:Multivariable calculus]]
[[Category:Stability theory]]
[[Category:Analytic geometry]]

Revision as of 17:22, 30 September 2018

A saddle point (in red) on the graph of z=x2−y2 (hyperbolic paraboloid)
Saddle point between two hills (the intersection of the figure-eight -contour)

In mathematics, a saddle point or minimax point[1] is a point on the surface of the graph of a function where the slopes (derivatives) in orthogonal directions are both zero (a critical point), but which is not a local extremum of the function.[2] An example of a saddle point shown on the right is when there is a critical point with a relative minimum along one axial direction (between peaks) and at a relative maximum along the crossing axis. However, a saddle point need not be in this form. For example, the function has a critical point at that is a saddle point since it is neither a relative maximum nor relative minimum, but it does not have a relative maximum or relative minimum in the -direction.

The name derives from the fact that the prototypical example in two dimensions is a surface that curves up in one direction, and curves down in a different direction, resembling a riding saddle or a mountain pass between two peaks forming a landform saddle. In terms of contour lines, a saddle point in two dimensions gives rise to a contour graph or trace in which the contour corresponding to the saddle point's value appears to intersect itself.

Mathematical discussion

A simple criterion for checking if a given stationary point of a real-valued function F(x,y) of two real variables is a saddle point is to compute the function's Hessian matrix at that point: if the Hessian is indefinite, then that point is a saddle point. For example, the Hessian matrix of the function at the stationary point is the matrix

which is indefinite. Therefore, this point is a saddle point. This criterion gives only a sufficient condition. For example, the point is a saddle point for the function but the Hessian matrix of this function at the origin is the null matrix, which is not indefinite.

In the most general terms, a saddle point for a smooth function (whose graph is a curve, surface or hypersurface) is a stationary point such that the curve/surface/etc. in the neighborhood of that point is not entirely on any side of the tangent space at that point.

The plot of y = x3 with a saddle point at 0

In a domain of one dimension, a saddle point is a point which is both a stationary point and a point of inflection. Since it is a point of inflection, it is not a local extremum.

Saddle surface

Hyperbolic paraboloid
A model of an elliptic hyperboloid of one sheet
A monkey saddle

A saddle surface is a smooth surface containing one or more saddle points.

Classical examples of two-dimensional saddle surfaces in the Euclidean space are second order surfaces, the hyperbolic paraboloid (which is often referred to as "the saddle surface" or "the standard saddle surface") and the hyperboloid of one sheet. The Pringles potato chip or crisp is an everyday example of a hyperbolic paraboloid shape.

Saddle surfaces have negative Gaussian curvature which distinguish them from convex/elliptical surfaces which have positive Gaussian curvature. A classical third-order saddle surface is the monkey saddle.[3]

Examples

In a two-player zero sum game defined on a continuous space, the equilibrium point is a saddle point.

For a second-order linear autonomous system, a critical point is a saddle point if the characteristic equation has one positive and one negative real eigenvalue.[4]

In optimization subject to equality constraints, the first-order conditions describe a saddle point of the Lagrangian.

Other uses

In dynamical systems, if the dynamic is given by a differentiable map f then a point is hyperbolic if and only if the differential of ƒ n (where n is the period of the point) has no eigenvalue on the (complex) unit circle when computed at the point. Then a saddle point is a hyperbolic periodic point whose stable and unstable manifolds have a dimension that is not zero.

A saddle point of a matrix is an element which is both the largest element in its column and the smallest element in its row.

See also

Notes

  1. ^ Howard Anton, Irl Bivens, Stephen Davis (2002): Calculus, Multivariable Version, p. 844
  2. ^ Chiang, Alpha C. (1984). Fundamental Methods of Mathematical Economics (3rd ed.). New York: McGraw-Hill. p. 312. ISBN 0-07-010813-7.
  3. ^ Buck, R. Creighton (2003). Advanced Calculus (Third ed.). Long Grove, IL: Waveland Press. p. 160. ISBN 1-57766-302-0.
  4. ^ von Petersdorff 2006

References

Further reading