In any case, we can now use finder to find different types of “best-fit” functions: finder(plucker('age'), Math.max, people); //=> {name: "Fred", age: 65} finder(plucker('name'), function(x,y) { return (x.charAt(0) === "L") ? x : y }, people); //=> {name: ...
It remains to derive the fully discrete solution in (4) based on discrete Laplace transform, i.e., generating function. Let F ˜ h n = F h n − f h 0 − t n f h ′ ( 0 ) . The scheme (4) has the following equivalent form: ( κ 1 ∂ τ + κ 2 ∂ τ α ...
, of the second derivative of the Bessel function J ν (x). We are interested first in how many zeros there are on the interval (0, j ν 1 ), where j ν1 is the smallest positive zero of J ν (x). We show that there exists a number = —0.19937078… such that and . More...
摘要: In this paper, we prove some explicit upper bounds for the average order of the generalized divisor function, and, according to an idea of Lenstra, we use them to obtain bounds for the class number of number fields.年份: 2002 ...
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ans-stage IRK method with a given stability function and stage orders−1 if the stability function is an approximation to the exponential function of at least orders. It is further indicated how to construct such methods as well as in which cases the constructed methods will be stiffly ...
Consider an arbitrary two-dimensional curve that can locally be expressed in the form of the height-function(4)z=f(x), as illustrated in Fig. 2. To each local interval [xi,xi+1], we can associate the height(5)Hi=1Δxi∫xixi+1f(x)dx, where Δxi=xi+1−xi. The middle of thi...
Part II: Optimal control of Zakai's eq... We consider here the value function corresponding to the optimal control of Zakai's equation and we study various regularity properties of this value funct... PL Lions - 《Lecture Notes in Math》 被引量: 232发表: 0年...
On reduction of functional-differential equations Let D 1, D 2,… Dm be any m partial derivative operators in with the form where j = (j1 ,j2 ,…jn ) is a multi-index. Suppose that f is an entire function ... Bao,Qin,Li - 《Complex Variables Theory & Application An International ...
4.1. The Cost Function The cost function (also known as the loss function) is essential for a majority of algorithms in machine learning. The model’s optimization is the process of training the cost function, and the partial derivative of the cost function with respect to each parameter is ...