Python矩阵嵌套循环代码优化咨询：MATLAB转译后效率提升

我有多年MATLAB编程经验，刚接触Python。需要通过多矩阵嵌套循环求解一系列方程，已将MATLAB实现逻辑借助ChatGPT转译为Python语法（使用Python 3.9及Spyder界面），但运行效率极低。此前尝试用itertools优化未成功，现寻求该代码的具体高效优化方案，相关代码如下：

import numpy as np

R1init=[]
R2init=[]
L1init=[]
L2init=[]
p1init=[]
p2init=[]
m1init=[]
m2init=[]
dVrinit=[]
dVlinit=[]

R1 = np.arange(50, 200.001, 2)
R2 = R1  # Since R1 and R2 are identical
L1 = -1*R1
L2 = np.arange(-50,-300.001,-10)

#converted values to be thousands, assuming this would help with matrix sizes.
dVl = 194329/1000  
dVr = 51936/1000  
dVg = 188384/1000  
DR = 0. 
DB = 0.

m1 = np.abs(dVl / R1) 
m2 = np.abs(dVr / L2)


j1 = 0
j2 = 0

for i in R1:
    for j in R2:
        for k in L1:
            for m in L2:
                for n in m1:
                    for q in m2:
                        p1 = ((j2*(1+q)-q)*m+j+dVr)/i
                        p2 = 1-j2*(1+q)+q-(i/m)*(1-j1*(1+n)+n-p1)+dVg/m
                        dVrchk = (q-(j2*q)-q)*m+(p1*i)-j+DR+DB
                        dVlchk =(j1-n+(j1*n))*i+k-(p2*m)
                        dVgchk = (1-j1-p1+n-j1*n)*i-(1-j2-p2+q-j2*q)*m
                        if 0<p2<1.05 and 0<p1<1.05 and dVl-100<dVlchk<dVl+100 and dVr-100<dVrchk<dVr+100:
                            R1init.append(i)
                            R2init.append(j)
                            L1init.append(k)
                            L2init.append(m)
                            p1init.append(p1)
                            p2init.append(p2)
                            m1init.append(n)
                            m2init.append(q)
                            dVrinit.append(dVrchk)
                            dVlinit.append(dVlchk)

内容的提问来源于stack exchange，提问作者Nikole