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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.
In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input. The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point.
In Unix and Unix-like operating systems, printf is a shell builtin (and utility program [2]) that formats and outputs text like the same-named C function. Originally named for outputting to a printer, it actually outputs to standard output. [3] The command accepts a format string, which specifies how to format values, and a list of values.
printf is a C standard library function that formats text and writes it to standard output. The name, printf is short for print formatted where print refers to output to a printer although the functions are not limited to printer output. The standard library provides many other similar functions that form a family of printf-like functions.
Specifically, a twice-differentiable function f is concave up if ″ > and concave down if ″ <. Note that if f ( x ) = x 4 {\displaystyle f(x)=x^{4}} , then x = 0 {\displaystyle x=0} has zero second derivative, yet is not an inflection point, so the second derivative alone does not give enough information to determine whether a given point is ...
Assume that there is a counterexample: an integer n ≥ 2 such that there is no prime p with n < p < 2n. If 2 ≤ n < 427, then p can be chosen from among the prime numbers 3, 5, 7, 13, 23, 43, 83, 163, 317, 631 (each being the largest prime less than twice its predecessor) such that n < p < 2n. Therefore, n ≥ 427.
Every concave function that is nonnegative on its domain is log-concave. However, the reverse does not necessarily hold. An example is the Gaussian function f(x) = exp(−x 2 /2) which is log-concave since log f(x) = −x 2 /2 is a concave function of x. But f is not concave since the second derivative is positive for | x | > 1:
The dual function g is concave, even when the initial problem is not convex, because it is a point-wise infimum of affine functions. The dual function yields lower bounds on the optimal value p ∗ {\displaystyle p^{*}} of the initial problem; for any λ ≥ 0 {\displaystyle \lambda \geq 0} and any ν {\displaystyle \nu } we have g ( λ , ν ...