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Concave function

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Title: Concave function  
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Subject: Inflection point, Concave, Inequality of arithmetic and geometric means, Distortion risk measure, Submodular set function
Collection: Convex Analysis, Types of Functions
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Concave function

In mathematics, a concave function is the negative of a convex function. A concave function is also synonymously called concave downwards, concave down, convex upwards, convex cap or upper convex.


  • Definition 1
  • Properties 2
  • Examples 3
  • See also 4
  • Notes 5
  • References 6


A real-valued function f on an 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],

f((1-t)x+(t)y)\geq (1-t) f(x)+(t)f(y).

A function is called strictly concave if

f((1-t)x + (t)y) > (1-t) f(x) + (t)f(y)\,

for any t in (0,1) and xy.

For a function f:RR, 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) ).

A function f(x) is quasiconcave if the upper contour sets of the function S(a)=\{x: f(x)\geq a\} are convex sets.[1]


A function f(x) is concave over a convex set if and only if the function −f(x) is a convex function over the set.

A differentiable function f is concave on an interval if its derivative function f ′ 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 ′′(x), is positive (or, if the acceleration is positive), then the graph is convex; if f ′′(x) is negative, then the graph is concave. Points where concavity changes are inflection points.

If a convex (i.e., concave upward) function has a "bottom", any point at the bottom is a minimal extremum. If a concave (i.e., concave downward) function has an "apex", any point at the apex is a maximal extremum.

If f(x) is twice-differentiable, then f(x) is concave if and only if f ′′(x) is non-positive. If its second derivative is negative then it is strictly concave, but the opposite is not true, as shown by f(x) = -x4.

If f is concave and differentiable, then it is bounded above by its first-order Taylor approximation:

f(y) \leq f(x) + f'(x)[y-x][2]

A continuous function on C is concave if and only if for any x and y in C

f\left( \frac{x+y}2 \right) \ge \frac{f(x) + f(y)}2

If a function f is concave, and f(0) ≥ 0, then f is subadditive. Proof:

  • since f is concave, let y = 0, f(tx) = f(tx+(1-t)\cdot 0) \ge t f(x)+(1-t)f(0) \ge t f(x)
  • 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)


See also


  1. ^ Varian 1992, p. 496.
  2. ^ Varian 1992, p. 489.
  3. ^  


  • Crouzeix, J.-P. (2008). "Quasi-concavity". In Durlauf, Steven N.; Blume, Lawrence E. The New Palgrave Dictionary of Economics (Second ed.). Palgrave Macmillan.  
  • Rao, Singiresu S. (2009). Engineering Optimization: Theory and Practice. John Wiley and Sons. p. 779.  
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