I'm trying to solve symbolically a system of the two following differential equations either in python or in r:
I want to find symbolic solutions for dH/dt=0 and dV/dt=0. However, I struggle to convert summations with matrixes and vector into code (python or R). Could someone help me?
Thanks in advance!
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I'am trying to solve a following problem.
In fact, this is Least Absolute Deviation Regression problem. I want to know how to solve this with python. I know that scipy has "linprog" which solve linear system with linear inequality constraints. But here there is two variable in inequality constraints, t, x. So, I want to know how to apply the "linprog" or is there other library which can solve this problem? Thanks
You have to write your problem in standard form, splitting the second inequality constraint and concatenating the two optimization variables. Then, you can feed it to linprog.
It is more of a math problem than an implementation one.
I am trying to solve a system of N*N nonlinear equations, but I get stuck and do not understand what is the problem.
My equations are :
h_{j,i} = (T/2) \sum_{k=1..N}{f(h_{k,j})} - (T/2) f(h_{i,j})
for i and j in [1..N]^2, and where the h are the unknowns, f is a known function and T is a parameter.
In all the examples I have found, there are maybe two or three equations/unknowns, so one can implement the equations directly. Nevertheless, I have too many equations here, and I do not understand how to implement a code without explicitly writing all the equations, and using fsolve (on python).
Thanks for your help
I wanted to know how to create symbolic vectors in sympy. Once I have accomplished that, I would like to understand if it possible to integrate the following type of complex equation using sympy platform. I want the analytical solutions and hence want the solutions in symbolic form.
x1,x2,B are all 3D vectors, c is a small positive constant and
integrating w.r.t 't'
If this gets complicated in sympy could you please suggest any other platform to go forward with such analytical solutions.
Any help will be greatly appreciated. Thank you!
I have a following
ordinary differential equation
and numeric parameters Sigma=0.4, x(0) = 4 and dx(0)/dt = 0
My task is to get Cauchy problem solution (Initial value problem solution) of differential equation using ode function
Can someone help me? I don't even know how to write equation and especially numeric parameters in correct way for SciPy.
P.S. Sorry for not posting images, I've just registered.
Like Warren said, scipy.integrate.odeint is the 'SciPy' way to solve this.
But before you take your problem to SciPy (or whatever solver you end up using) you'll want to convert your 2nd order ODE to a first order ODE using something like: http://tutorial.math.lamar.edu/Classes/DE/SystemsDE.aspx
To get things into SciPy you need to get your equation looking like:
y' = f(y)
But right now your equation is written like:
y'' = f(y, y')
The solution is to add more variables to your system, but the link will explain it more thoroughly.
I have a system of linear equations with some constraints. I would appreciate it if someone could help me solving this system of equations in Python.
Should be fairly easy to solve with sympy: http://docs.sympy.org/dev/modules/solvers/solveset.html#sympy.solvers.solveset.linsolve
Add all your equations to a list. Then pass that list to linsolve(). You'll probably loop through each value of j to generate those equations.