I am currently writing a code in Python where the objective is to find the root of the output of a function with respect to input variable x. The code looks like this: As can be seen in the code block above, there are two outputs specified: Equity_Solve Mezzanine_Solve I now want to find the root for both outputs separately.
Tag: scipy-optimize
Scipy BasinHopping not returning correct global minima
I’m working with the following scipy code. The global minimum of this function is at 0, but this isn’t what basin hopping returns. Depending on the start position x0, it returns different local minima – not the global one at 0. If we set x_0 = -6, it returns a minima at -7.7, if we set x0 = 1, then
Scipy minimize with pandas dataframe with group by
I have a data frame (a sample df below) and trying to minimize cost function on that. Below are minimize and cost function. Then I’m iterating this function in loop as below, which is working but takes lot of time and memory, Please note that I have bigger dataframe with more rows and columns, for this questions I just created
How to pass arguments to non-linear constraints in scipy.optimize?
I am trying to use scipy optimization to solve an optimization problem. I have defined the non-linear constraints and fitness functions as shown below in the code. I am able to pass arguments to the fitness function but not to the non-linear constraints. Is there clean way to do it? The arguments to be passed to fitness function and the
How to hide `delta_grad == 0.0` warning in scipy.optimize.minimize?
I have a loop that executes several hundred optimizations using scipy.optimize.minimize. Unfortunately, I keep getting this annoying warning: Because I am running hundreds of optimizations, this warning shows up dozens and dozens of times during the loop, and it just clutters the console and obscures the rest of my program’s output. Is there a way to either Check if this
How to write a function to fit data to a sum of N Gaussian-like peaks without explicitly defining the expression for every possible N?
I am trying to fit a progression of Gaussian peaks to a spectral lineshape. The progression is a summation of N evenly spaced Gaussian peaks. When coded as a function, the formula for N=1 looks like this: where A, e0, hf, S and fwhm are to be determined from the fit with some good initial guesses. Importantly, the parameter i
Numerical Solutions for System of Non-Linear Equation in Python
I have 2 simple equations: These equations can be solved analytically where k = 5.77 and h = 8.47. I tried to solve it in Python using fsolve and I have followed the way from: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html#scipy.optimize.fsolve Below is my code: And the result is I am not sure what I did wrong here such that I did not get the
Scipy minimze with constrains that have no simple expression
I am trying to find the values that minimize a least squares function. The issue is that a solution may be valid or not in a way that cannot be given as a simple expression of the values. Instead, we can check the validity by calling a function. What I tried to do was to set the sum of squares
Problem in linear constraints of scipy. All the elements of population is getting rejected
I am using scipy differential evolution. I have to set the following linear constraints. 0<x1+x2+x3+x4<=1. x2+x3=1. I have set the following matrix A=[0 1 1 0] B=[1]. linear_constraint = LinearConstraint(A,B,B,True). i have also set lower and upper bound to 0 and1. However, during each iteration, the output of the objective function is InF, whereas the differential evolution is not calling
What are ‘population energies’?
In scipy.optimize.differential_evolution, the convergence criteria are that: This begs the question, what are ‘population energies’ please? This could be a follow up question to: Explain the intuition for the tol paramer in scipy differential evolution I tried looking in the code, but I got: So a follow up question would be what does that do please? Answer As you wrote