minimization

I am trying to minimize a function in Python using scipy.optimize.minimize , to determine four distinct parameters. I would like to print the currently evaluated params at each step of the optimisation algorithm, so I can use them to make my initial guess better. How can I do this? Use the callback keyword argument. scipy.optimize.minimize can take a keyword argument callback . This should be a function that accepts, as input, the current vector of parameters. This function is called after every iteration. For instance, from scipy.optimize import minimize def objective_function(xs): """

I am curious is there is a straightforward method for utilizing scipy.optimize.minimize with multiple variables that take different shapes. For example, let's take a look at a matrix decomposition problem. I apologize, but I will be using latex here in the hope that one day SO will implement it. We can deconstruct the matrix $ A_{n \times m} $ into two matrices $ W_{k \times n} $ and $ H_{k \times m} s.t. A \approx W^TH $ There are numerous methods for solving for W and H , but let this just serve as an example problem. We could solve this problem with scipy.optimize.minimize by first defining

I have been trying to fit a linear model to a set of stress/strain data by minimizing chi-squared. Unfortunately using the code below is not correctly minimizing the chisqfunc function. It is finding the minimum at the initial conditions, x0 , which is not correct. I have looked through the scipy.optimize documentation and tested minimizing other functions which has worked correctly. Could you please suggest how to fix the code below or suggest another method I can use to fit a linear model to data by minimizing chi-squared? import numpy import scipy.optimize as opt filename = 'data.csv' data

I try to use the z3 solver for a minimization problem. I was trying to get a timeout, and return the best solution so far. I use the python API, and the timeout option "smt.timeout" with set_option("smt.timeout", 1000) # 1s timeout This actually times out after about 1 second. However a larger timeout does not provide a smaller objective. I ended up turning on the verbosity with set_option("verbose", 2) And I think that z3 successively evaluates larger values of my objective, until the problem is satisfiable: (opt.maxres [0:6117664]) (opt.maxres [175560:6117664]) (opt.maxres [236460:6117664])

I want to use the scipy.optimize module to minimize a function. Let's say my function is f(x,a) : def f(x,a): return a*x**2 For a fixed a , I want to minimize f(x,a) with respect to x . With scipy I can import for example the fmin function (I have an old scipy: v.0.9.0), give an initial value x0 and then optimize ( documentation ): from scipy.optimize import fmin x0 = [1] xopt = fmin(f, x0, xtol=1e-8) which fails because f takes two arguments and fmin is passing only one (actually, I haven't even defined a yet). If I do: from scipy.optimize import fmin x0 = [1] a = 1 xopt = fmin(f(x,a), x0,