enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Second derivative - Wikipedia

    en.wikipedia.org/wiki/Second_derivative

    The second derivative of a function f can be used to determine the concavity of the graph of f. [2] A function whose second derivative is positive is said to be concave up (also referred to as convex), meaning that the tangent line near the point where it touches the function will lie below the graph of the function.

  3. Concave function - Wikipedia

    en.wikipedia.org/wiki/Concave_function

    A function f is concave over a convex set if and only if the function −f is a convex function over the set. The sum of two concave functions is itself concave and so is the pointwise minimum of two concave functions, i.e. the set of concave functions on a given domain form a semifield.

  4. Inflection point - Wikipedia

    en.wikipedia.org/wiki/Inflection_point

    Definition. Inflection points in differential geometry are the points of the curve where the curvature changes its sign. [2][3] For example, the graph of the differentiable function has an inflection point at (x, f(x)) if and only if its first derivative f' has an isolated extremum at x. (this is not the same as saying that f has an extremum).

  5. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    For example, the problem of maximizing a concave function can be re-formulated equivalently as the problem of minimizing the convex function . The problem of maximizing a concave function over a convex set is commonly called a convex optimization problem. [8]

  6. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    If the objective function is concave (maximization problem), or convex (minimization problem) and the constraint set is convex, then the program is called convex and general methods from convex optimization can be used in most cases. If the objective function is quadratic and the constraints are linear, quadratic programming techniques are used.

  7. Jensen's inequality - Wikipedia

    en.wikipedia.org/wiki/Jensen's_inequality

    Jensen's inequality generalizes the statement that a secant line of a convex function lies above its graph. In mathematics, Jensen's inequality, named after the Danish mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function. It was proved by Jensen in 1906, [1] building on an ...

  8. Convex function - Wikipedia

    en.wikipedia.org/wiki/Convex_function

    A strictly convex function is a function that the straight line between any pair of points on the curve is above the curve 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 f ( x , y ) = x 2 + y {\displaystyle f(x,y)=x^{2}+y} .

  9. Sigmoid function - Wikipedia

    en.wikipedia.org/wiki/Sigmoid_function

    A sigmoid function is a function whose graph follows the logistic function. It is defined by the formula: σ {\displaystyle \sigma (x)= {\frac {1} {1+e^ {-x}}}= {\frac {e^ {x}} {1+e^ {x}}}=1-\sigma (-x).} In many fields, especially in the context of artificial neural networks, the term "sigmoid function" is correctly recognized as a synonym for ...