What I am looking for here is a yes/no answer as to whether scipy can handle bounding intervals restricted to a single float, and, on a higher level, whether I have the correct approach.
Scipy linprog code#
The code I have developed is really quite extensive, which is why I have not posted it here, so of course I don't expect a code-based solution. (c, AubNone, bubNone, AeqNone, beqNone, boundsNone, method'simplex', callbackNone, optionsNone Minimize a linear objective function subject to linear equality and inequality constraints. linprog (c, Aub None, bub None, Aeq None, beq None, bounds None, method 'interior-point', callback None, options None, x0 None) source Linear programming: minimize a linear objective function subject to linear equality and inequality constraints. Note that the highs methods are recommended for usage with scipy>1.6.0 because they are the fastest ones. Preferably, do not use sudo pip, as this combination can cause problems. pip installs packages for the local user and does not write to the system directories.
Scipy linprog install#
Is this the best way to be solving my problem? linprog to solve the linear programming formulation. python -m pip install -user numpy scipy matplotlib ipython jupyter pandas sympy nose We recommend using an user install, sending the -user flag to pip. Linear programming: minimize a linear objective function subject to linear equality and inequality constraints. is demonstrated for solving a nonlinear objective function subject to general inequality and equality constraints. This can run without error, but it seems like scipy/numpy explicitly ignore the bounds I place on the variables - I get results for the variables that I have 'fixed' that are not the corresponding opt_val.Ĭan scipy handle bounds that restrict a variable to a single floating point number? (c, AubNone, bubNone, AeqNone, beqNone, boundsNone, methodinterior-point, callbackNone, optionsNone, x0None) source ¶. I came across these two lists today and even though there is some overlap I thought I would post them both rather than writing a summary of the ones I liked.
Rerun simulation with modified bounds and inequality vector.Make inequality constraints corresponding to that variable ineffective, by changing the coefficient of b_ub to numpy.inf.Assuming optimisation problem is feasible and successfully solved, fix the maximum to opt_val by setting bounds=, where all other variables have the bounds (-numpy.inf, numpy.inf).Run scipy.linprog(., bounds=) with all variables unbounded, to minimise the maximum of the set of numbers.
I want to run an optimisation problem repeatedly to further refine the end result.Įssentially, the objective is to minimise the maximum of a set of variables (subject to inequality and equality constraints), and then minimise the maximum of the set excluding the maximum, and then minimise the maximum of the set excluding the two largest numbers and so on.
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