Convex function: Difference between revisions

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<math display=block>f\left(t x_1 + (1-t) x_2\right) < t f\left(x_1\right) + (1-t) f\left(x_2\right)</math>

A strictly convex function <math>f</math> is a function such that the straight line between any pair of points on the curve <math>f</math> is above the curve <math>f</math> except for the intersection points between the straight line and the curve. An example of a function which is convex but not strictly convex is <math>f(x,y) = x^2 + y</math>. This function is not strictly convex because any two points sharing an x coordinate will have a straight line between them, while any two points NOT sharing an x coordinate will have a greater value of the function than the points between them.

The function <math>f</math> is said to be '''{{em|[[Concave function|concave]]}}''' (resp. '''{{em|strictly concave}}''') if <math>-f</math> (<math>f</math> multiplied by −1) is convex (resp. strictly convex).

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* Suppose <math>f</math> is a function of one [[real number|real]] variable defined on an interval, and let <math display=block>R(x_1, x_2) = \frac{f(x_2) - f(x_1)}{x_2 - x_1}</math> (note that <math>R(x_1, x_2)</math> is the slope of the purple line in the first drawing; the function <math>R</math> is [[Symmetric function|symmetric]] in <math>(x_1, x_2),</math> means that <math>R</math> does not change by exchanging <math>x_1</math> and <math>x_2</math>). <math>f</math> is convex if and only if <math>R(x_1, x_2)</math> is [[monotonically non-decreasing]] in <math>x_1,</math> for every fixed <math>x_2</math> (or vice versa). This characterization of convexity is quite useful to prove the following results.

* A convex function <math>f</math> of one real variable defined on some [[open interval]] <math>C</math> is [[Continuous function|continuous]] on <math>C. </math>. Moreover, <math>f</math> admits [[Semi-differentiability|left and right derivatives]], and these are [[monotonically non-decreasing]]. In addition, the left derivative is left-continuous and the right-derivative is right-continuous. As a consequence, <math>f</math> is [[differentiable function|differentiable]] at all but at most [[countable|countably many]] points, the set on which <math>f</math> is not differentiable can however still be dense. If <math>C</math> is closed, then <math>f</math> may fail to be continuous at the endpoints of <math>C</math> (an example is shown in the [[#Examples|examples section]]).

* A [[differentiable function|differentiable]] function of one variable is convex on an interval if and only if its [[derivative]] is [[monotonically non-decreasing]] on that interval. If a function is differentiable and convex then it is also [[continuously differentiable]].

* A differentiable function of one variable is convex on an interval if and only if its graph lies above all of its [[tangent]]s:<ref name="boyd">{{cite book| title=Convex Optimization| first1=Stephen P.|last1=Boyd |first2=Lieven| last2=Vandenberghe | year = 2004 |publisher=Cambridge University Press| isbn=978-0-521-83378-3| url= https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf#page=83 |format=pdf | access-date=October 15, 2011}}</ref>{{rp|69}} <math display=block>f(x) \geq f(y) + f'(y) (x-y)</math> for all <math>x</math> and <math>y</math> in the interval.