我试图为接触网求解一个方程,并想使用牛顿-拉夫森方法。
from math import sinh, cosh
y = 0.4 #Has taken to initiate the iteration.
k = 3/2
for _ in range(5): #Iterations for Newton-Raphson Method
y = y - (sinh(y)-y*k)/(cosh(y)-k)
print(y)
print(y)
Output
-0.05174312094834577
9.262910138898434e-05
由于R对函数参数的求值,可以指定一组一致的输入参数,并自动计算其他参数。
考虑下面的函数,在化学中将浓度、质量、体积和摩尔重量联系起来,
concentration <- function(c = m / (M*V), m = c*M*V, V = m / (M*c), M = 417.84){
cat(c("c=", c*1e6, "micro.mol/L\n",
"m=", m*1e3, "mg\n",
"M=", M, "g/mol\n",
对于线性矩阵方程的求解,可以使用numpy.linalg.solve来实现LAPACK例程。
根据文档
DGESV computes the solution to a real system of linear equations
A * X = B,
where A is an N-by-N matrix and X and B are N-by-NRHS matrices.
The LU decomposition with partial pivoting and row interchanges is
used to factor A as
A = P * L
为了加快SARIMAX模型的训练时间,我第一次使用线程库。但是代码始终失败,出现了以下错误
Bad direction in the line search; refresh the lbfgs memory and restart the iteration.
This problem is unconstrained.
This problem is unconstrained.
This problem is unconstrained.
以下是我的代码:
import numpy as np
import pandas as pd
from statsmodels.tsa.arima_