newtons-method

问题 So beginner here at Python, using it for economic research. I am currently trying to run code to find the roots of a CES Function using the Newton-Ralphson Method (https://quanteconpy.readthedocs.io/en/latest/optimize/root_finding.html). However, I am running into an error here where it says "TypeError: unsupported operand type(s) for -: 'CPUDispatcher' and 'int'". I have no clue what this means (I am using Spyder for Python 3.8) so any help would be much appreciated. My code is attached

问题 So beginner here at Python, using it for economic research. I am currently trying to run code to find the roots of a CES Function using the Newton-Ralphson Method (https://quanteconpy.readthedocs.io/en/latest/optimize/root_finding.html). However, I am running into an error here where it says "TypeError: unsupported operand type(s) for -: 'CPUDispatcher' and 'int'". I have no clue what this means (I am using Spyder for Python 3.8) so any help would be much appreciated. My code is attached

问题 I understand what Gradient Descent does. Basically it tries to move towards the local optimal solution by slowly moving down the curve. I am trying to understand what is the actual difference between the plan gradient descent and the newton's method? From Wikipedia, I read this short line "Newton's method uses curvature information to take a more direct route." What does this intuitively mean? 回答1: At a local minimum (or maximum) x , the derivative of the target function f vanishes: f'(x) = 0

问题 I've found in 'addons/math/misc/brent.ijs' implementation of Brent's method as an adverb. I would like to build a Newton's method as an adverb too but it's much harder than building tacit verbs. Here is a explicit version of Newton's iteration: newton_i =: 1 : '] - u % u d.1' With such usage: 2&o. newton_i^:_ (1) NB. (-: 1p1) must be found 1.5708 2 o. 1.5708 NB. after substitution we get almost 0 _3.67321e_6 And of course, for convenience: newton =: 1 : 'u newton_i^:_' What's a tacit

问题 First, I have no idea wether the professor gave the wrong question. Anyway, I tried to generate F(x)~U(0,1) , where CDF F(x)=1-(1+x)exp(-x) (For this CDF, you could not calculate x=g(F(x)) by hand). And then calculate the root of F(x) to achieve what the question want. Because the root range from 0 to INF , uniroot() is out of question. Therefore, I use Newton Method to write one. Then, my code is like this: f=function(x) { ifelse(x>=0,x*exp(-x),0) } in.C=function(n) { a=runif(n) G=NULL for(i

问题 I am trying to write a function that implements Newton's method in two dimensions and whilst I have done this, I have to now adjust my script so that the input parameters of my function must be f(x) in a column vector, the Jacobian matrix of f(x) , the initial guess x0 and the tolerance where the function f(x) and its Jacobian matrix are in separate .m files. As an example of a script I wrote that implements Newton's method, I have: n=0; %initialize iteration counter eps=1; %initialize error

问题 I am trying to write an assembly program using the 8086 processor that will find the cube root of a number. Obviously I am using floating points. Algorithm based upon Newton-Raphson method: root := 1.0; repeat oldRoot := root; root := (2.0*root + x/(root*root)) / 3.0 until ( |root – oldRoot| < 0.001; How do I divide (2*root + x) by (root*root)? .586 .MODEL FLAT .STACK 4096 .DATA root REAL4 1.0 oldRoot REAL4 2.0 Two REAL4 2.0 inttwo DWORD 2 itThree DWORD 3 three REAL4 3.0 x DOWRD 27 .CODE main

问题 I made a test program to try the NewtonRaphsonSolver class through the Apache Commons Math library. Newton's method is used to find roots for a given function. The test program that I wrote references the cos(x) function (I have a more difficult function to analyze and am looking at the cos(x) function first). The code for the test program is import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; import org.apache.commons.math3.analysis.differentiation

问题 I am having trouble in implementing an approach to call Newton's method in a Fortran program. So I want to use Newton's method to solve an equation following the link However, my program is slightly different with the example above. In my case, the equation requires some additional information which are produced during runtime. subroutine solve(f, fp, x0, x, iters, debug) which means the f is calculated not only based on x, but also a few other variables (but x is the unknown). I have a

问题 I am not sure this question is on topic here or elsewhere (or not on topic at all anywhere). I have inherited Fortran 90 code that does Newton Raphson interpolation where logarithm of temperature is interpolated against logarithm of pressure. The interpolation is of the type t = a ln(p) + b and where a, b are defined as a = ln(tup/tdwn)/(alogpu - alogpd) and b = ln T - a * ln P Here is the test program. It is shown only for a single iteration. But the actual program runs over three FOR loops