least-squares

I am trying to find a java code to compute the least squares solution (x) in the Ax=b equation. Suppose that A = [1 0 0;1 0 0]; b = [1; 2]; x = A\b returns the x = 1.5000 0 0 I found Class LeastSquares, public LeastSquares(double[] a, double[] b, int degree) but in the input both A and B are one dimensional arrays, however, in above example, A is a matrix and B is an array. In Class NonNegativeLeastSquares public NonNegativeLeastSquares(int M, int N, double a[][],double b[]) A is a matrix and B is an array, but the description of the class says that it finds an approximate solution to the

I want to compute ordinary least square ( OLS ) estimates in R without using "lm" , and this for several reasons. First, "lm" also computes lots of stuff I don't need (such as the fitted values) considering that data size is an issue in my case. Second, I want to be able to implement OLS myself in R before doing it in another language (eg. in C with the GSL). As you may know, the model is: Y=Xb+E; with E ~ N(0, sigma^2). As detailed below, b is a vector with 2 parameters, the mean (b0) and another coefficients (b1). At the end, for each linear regression I will do, I want the estimate for b1

Dear fellow stackoverflow users, I am trying to calculate the normal vectors over an arbitrary (but smooth) surface defined by a set of 3D points. For this, I am using a plane fitting algorithm that finds the local least square plane based on the 10 nearest neighbors of the point at which I'm calculating the normal vector. However, it does not always find what seems to be the best plane. Thus, I'm wondering whether there is a flaw in my implementation or a flaw in my algorithm. I'm using Singular Value Decomposition as I found recommended in several links on the subject of plane fitting. Here

I am trying to fit a power-law function, and in order to find the best fit parameter. However, I find that if the initial guess of parameter is different, the "best fit" output is different. Unless I find the right initial guess, I can get the best optimizing, instead of local optimizing. Is there any way to find the **appropriate initial guess ** ????. My code is listed below. Please feel free make any input. Thanks! import numpy as np import pandas as pd from scipy.optimize import curve_fit import matplotlib.pyplot as plt %matplotlib inline # power law function def func_powerlaw(x,a,b,c):