newtons-method

问题 I am using the Newton-Raphson Algorithm to divide IEEE-754 single-precision floating point values using single precision hardware. I am using the method described at these two links: Wikipedia Newton-Raphson Division Newton-Raphson Method I'm Using However, despite computing Xi to X_3 (i.e. using 3 iterations), my answers are still off a bit. I'm wondering why this is so? I'm comparing my results using MATLAB. Here is the output showing an example of an incorrect result ===== DIVISION RESULT

问题 I am doing an optimisation problem using Scipy, where I am taking a flat network of vertices and bonds of size NNxNN , connecting two sides of it (i.e., making it periodic), and minimising an energy function, so that it curls up to form a cylinder. (See the links below.) Since I have the function energy(xyz-position) and it's gradient, I decided to use the three methods recommended in the Scipy manual -- Newton-CG , BFGS , L-BFGS-B -- and compare how they performed. I call the optimisation

问题 How many recursions are made before every single root is found? Also, which ones are the roots? Here's my code: e=0.000001; f1=@(x) 14.*x.*exp(x-2)-12.*exp(x-2)-7.*x.^3+20.*x.^2-26.*x+12; a=0; c=3; while abs(c-a)>e b=(c+a)/2; if f1(a)*f1(b)<0 c=b; else a=b; end disp(b); end 回答1: Bisection works by taking endpoints of some initial interval [a,b] and finding which half of the interval must contain the root (it evaluates the midpoint, and identifies which half has the sign change). Then

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last month . Over the past couple of weeks I've been trying to simulate orbits in a solar system simulation I am making as part of a University module. To cut things short, my simulation is written in C++ using the Ogre3D rendering engine. I have attempted to implement orbits using Newton's law

问题 I am new to matlab and I need to create a function that does n iterations of the Newton-Raphson method with starting approximation x = a. This starting approximation does not count as an interation and another requirement is that a for loop is required. I have looked at other similar questions posted but in my case I do not want to use a while loop. This is what my inputs are supposed to be: mynewton(f,a,n) which takes three inputs: f: A function handle for a function of x. a: A real number.

问题 I'm reading this document: http://software.intel.com/en-us/articles/interactive-ray-tracing and I stumbled upon these three lines of code: The SIMD version is already quite a bit faster, but we can do better. Intel has added a fast 1/sqrt(x) function to the SSE2 instruction set. The only drawback is that its precision is limited. We need the precision, so we refine it using Newton-Rhapson: __m128 nr = _mm_rsqrt_ps( x ); __m128 muls = _mm_mul_ps( _mm_mul_ps( x, nr ), nr ); result = _mm_mul_ps(

问题 Algorithm to be coded in C#: fn = f(xn) f′n = df(xn)/dx ∆xn = -fn / f′n Update: xn+1 = xn + ∆xn Repeat the process until ∆xn ≤ e I must use the Newton-Raphson method to solve but I do not know how to do a loop that puts in the next answer each time. How do I compute this? This is my broken code double a = 1, Lspan = 30, Lcable = 33, fn, fdn, dfn, j; fn = (2 * a * (Math.Sinh(Lspan / 2 * a))) - Lcable; fdn = (2 * (Math.Sinh(Lspan / 2 * a)) - ((Lspan / 2 * a) * Math.Cosh(Lspan / 2 * a))); dfn =

问题 I'm quite a beginner in this topic, and couldn't find out the reason: sometimes the program works, sometimes not (after asking the question, it simply doensn't want to take in my answers, than I can write in as much as I want, it doesn't respond, just list out the numbers, I tiped in) #include <stdio.h> float abszolut (float szam) { float abszoluterteke; if (szam >=0) abszoluterteke = szam; else abszoluterteke = -szam; return abszoluterteke; } float negyzetgyok (float szam) { float pontossag

问题 I am creating a basic Newton-method algorithm for an unconstrained optimization problem, and my results from the algorithm are not what I expected. It is a simple objective function so it is clear that the algorithm should converge on (1,1). This is confirmed by a gradient descent algorithm I created previously, here: def grad_descent(x, t, count, magnitude): xvalues.append(x) gradvalues.append(np.array([dfx1(x), dfx2(x)])) fvalues.append(f(x)) temp=x-t*dfx(x) x = temp magnitude = mag(dfx(x))

问题 I am trying to solve the kmv merton model for default prediction (based on the black scholes model) in Python. I referred to the following code as a jump off point for my code. My code is as follows: # -*- coding: utf-8 -*- """ Created on Mon Sep 10 15:42:24 2018 @author: CAFRAL """ from io import StringIO import time, boto3 import pandas as pd import numpy as np import scipy.optimize as sco from scipy.stats import norm import datetime #generating random values to create a database for