Suppose you have a general shape defined by a bunch of coordinate points that form something that looks like a circle, ellipse, or general closed curve - how do you find the a开发者_Python百科rea boun
While learning about precision in floating point arithmetic and different methods to avoid it (using a conjugate, taylor series,...) books frequently mention the subtraction of two very similar number
Without resorting to asymptotic notation, is tedious step counting the only way to get the time complexity 开发者_开发问答of an algorithm? And without step count of each line of code can we arrive at
Background: I have a function in my program that takes a set of points and finds the minimum and maximum on the curve generated by those points. The thing is it is incredibly slow as it uses a while l
I am trying to work out if I can parallelise the training aspect of a machine learning algorithm. The computationally expensive part of the training involves Cholesky decomposing a positive-definite m
The DBL_EPSILON/std::numeric_limits::epsilon will give me the smallest value that will make a difference when adding w开发者_如何转开发ith one.
It is \"well开发者_Python百科-known\" that the BFGS optimization algorithm is superlinearly convergent for strictly convex problems, but is there any analysis for problems that are non-strictly convex
Many numerical algorithms tend to run on 32/64bit floating points. However, what if you had access to lower precision (and les开发者_如何学Gos power hungry) co-processors? How can then be utilized in
let us say I have have polynomial in x, divided by a power of x: p = (a + x(b + x(c + ..)))/(x**n) efficiency aside, which would be more accurate computation numerically, the abo开发者_开发技巧ve o
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