concave function

Similarly, a concave function of a martingale is a supermartingale.

Because the reasons are in decreasing order, the cumulative function is a concave function.

In mathematics, a concave function is the negative of a convex function.

For this, he paid his attention to the Jensen inequality for operator concave functions.

Notice that log is a concave function.

A concave function, such as the "gauge" formula.

A concave function with is also subadditive.

Every concave function that is nonnegative on its domain is log-concave.

Taking the minorize-maximization version, let be the objective concave function to be maximized.

Submodular functions have properties which are very similar to convex and concave functions.

To explain risk aversion within this framework, Bernoulli proposed that subjective value, or utility, is a concave function of money.

A concave function can be quasiconvex function.

Let be an increasing, strictly concave function, called the utility, which measures how much benefit a user obtains by transmitting at rate .

In the case where the maximization is an integral of a concave function of utility over an horizon (0,T), dynamic programming is used.

A proper concave function is any function g such that is a proper convex function.

Convex sets, convex and concave functions, Kuhn-Tucker conditions.

Here taking the square root introduces further downward bias, by Jensen's inequality, due to the square root being a concave function.

The surplus yield models of fisheries management usually assume that a concave function of equilibrium yield versus fishing effort exists.

At an inflection point, a function switches from being a convex function to being a concave function or vice versa.

Given a metric space and an increasing concave function such that if and only if , then is also a metric on .

It is an increasing concave function with respect to L because of the Diminishing Marginal Product of Labor.

If is a submodular function then defined as where is a concave function, is also a submodular function.

Since the square root is a strictly concave function, it follows from Jensen's inequality that the square root of the sample variance is an underestimate.

It deals with optimization problems (minimization) subject to linear constraints whose objective functions consist of a composite concave function of two linear functions.

For example, the problem of maximizing a concave function f can be re-formulated equivalently as a problem of minimizing the function -f, which is convex.