Kennedy–Thorndike experiment: Difference between revisions

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In [[mathematics]], a '''concave function''' is the [[additive inverse|negative]] of a [[convex function]]. A concave function is also [[synonym]]ously called '''concave downwards''', '''concave down''', '''convex upwards''', '''convex cap''' or '''upper convex'''.
 
==Definition==
A real-valued [[function (mathematics)|function]] ''f'' on an [[interval (mathematics)|interval]] (or, more generally, a [[convex set]] in [[vector space]]) is said to be ''concave'' if, for any ''x'' and ''y'' in the interval and for any ''t''  in [0,1],
:<math>f(tx+(1-t)y)\geq t f(x)+(1-t)f(y).</math>
 
A function is called ''strictly concave'' if
:<math>f(tx + (1-t)y) > t f(x) + (1-t)f(y)\,</math>
for any ''t'' in (0,1) and ''x'' ≠ ''y''.
 
For a function ''f'':''R''→''R'', this definition merely states that for every ''z'' between ''x'' and ''y'', the point (''z'', ''f''(''z'') ) on the graph of ''f'' is above the straight line joining the points (''x'', ''f''(''x'') ) and (''y'', ''f''(''y'') ).
 
[[Image:ConcaveDef.png]]
 
A function ''f(x)'' is [[quasiconvex function|quasiconcave]] if the upper contour sets of the function <math>S(a)=\{x: f(x)\geq a\}</math> are convex sets.{{sfn|Varian|1992|p=496}}
 
==Properties==
A function ''f''(''x'') is concave over a convex set [[if and only if]] the function &minus;''f''(''x'') is a [[convex function]] over the set.
 
A [[differentiable]] [[graph of a function|function]] ''f'' is concave on an [[interval (mathematics)|interval]] if its [[derivative]] function ''f'' &prime;  is [[monotonically decreasing]] on that interval: a concave function has a decreasing [[slope]]. ("Decreasing" here means non-increasing, rather than strictly decreasing, and thus allows zero slopes.)
 
For a twice-differentiable function ''f'', if the [[second derivative]], ''f &prime;&prime;(x)'', is positive (or, if the [[acceleration]] is positive), then the graph is convex; if ''f &prime;&prime;(x)'' is negative, then the graph is concave. [[Point (geometry)|Points]] where concavity changes are [[inflection point]]s.
 
If a convex (i.e., concave upward) function has a "bottom", any [[point (geometry)|point]] at the bottom is a [[Maxima and minima|minimal extremum]].  If a concave (i.e., concave downward) function has an "apex", any point at the apex is a [[Maxima and minima|maximal extremum]].
 
If ''f''(''x'') is twice-[[differentiable]], then ''f''(''x'') is concave [[if and only if]] ''f'' &prime;&prime;(''x'') is [[non-positive]]. If its second derivative is [[negative numbers|negative]] then it is strictly concave, but the opposite is not true, as shown by ''f''(''x'') = -''x''<sup>4</sup>.
 
If ''f'' is concave and differentiable, then it is bounded above by its first-order [[Taylor approximation]]:
:<math>f(y) \leq f(x) + f'(x)[y-x]</math>{{sfn|Varian|1992|p=489}}
 
A [[continuous function]] on ''C'' is concave [[if and only if]] for any ''x'' and ''y'' in ''C''
:<math>f\left( \frac{x+y}2 \right) \ge \frac{f(x) + f(y)}2</math>
 
If a function ''f'' is concave, and ''f''(0) ≥ 0, then ''f'' is [[subadditivity|subadditive]]. Proof:
* since ''f'' is concave, let ''y'' = 0, <math>f(tx) = f(tx+(1-t)\cdot 0) \ge t f(x)+(1-t)f(0) \ge t f(x)</math>
* <math>f(a) + f(b) = f \left((a+b) \frac{a}{a+b} \right) + f \left((a+b) \frac{b}{a+b} \right)
\ge \frac{a}{a+b} f(a+b) + \frac{b}{a+b} f(a+b) = f(a+b)</math>
 
==Examples==
* The functions <math>f(x)=-x^2</math> and <math>g(x)=\sqrt{x}</math> are concave on their domains, as their second derivatives <math>f''(x) = -2</math> and <math>g''(x) = -\frac{1}{4 x^{1.5}}</math> are always negative.
* Any [[affine function]] <math>f(x)=ax+b</math> is both (non-strictly) concave and convex.
* The [[sine]] function is concave on the interval <math>[0, \pi]</math>.
* The function <math>f(x) = \log |B|</math>, where <math>|B|</math> is the [[determinant]] of a [[nonnegative-definite matrix]] ''B'', is concave.<ref name="Cover 1988">{{cite journal|author=[[Thomas M. Cover]] and J. A. Thomas|  title=Determinant inequalities via information theory| journal=[[SIAM Journal on Matrix Analysis and Applications]]| year=1988| volume=9|number=3| pages=384&ndash;392}}</ref>
* Practical example: rays bending in [[Computation of radiowave attenuation in the atmosphere]].
 
==See also==
* [[Concave polygon]]
* [[Convex function]]
* [[Jensen's inequality]]
* [[Logarithmically concave function]]
* [[Quasiconcave function]]
 
==Notes==
{{reflist}}
 
==References==
*{{cite book|last=Crouzeix|first=J.-P.|chapter=Quasi-concavity|title=The New&nbsp;Palgrave Dictionary of Economics|editor-first=Steven&nbsp;N.|editor-last=Durlauf|editor2-first=Lawrence&nbsp;E<!-- . -->|editor2-last=Blume|publisher=Palgrave Macmillan|year=2008|edition=Second|pages=|url=http://www.dictionaryofeconomics.com/article?id=pde2008_Q000008|doi=10.1057/9780230226203.1375|ref=harv}}
*{{cite book |title=Engineering Optimization: Theory and Practice|first=Singiresu S.|last=Rao|
publisher=John Wiley and Sons|year=2009|isbn=0-470-18352-7|page=779}}
*{{cite book |ref=harv |last=Varian |first=Hal R. |authorlink=Hal Varian |title=Microeconomic Analysis |year=1992 |edition=Third |publisher=W.W. Norton and Company}}
 
{{DEFAULTSORT:Concave Function}}
[[Category:Types of functions]]
[[Category:Convex analysis]]
 
[[de:Konvexe und konkave Funktionen]]
[[ja:凹関数]]

Latest revision as of 15:45, 30 August 2014

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