Is there a wrapper library for solving optimisation problems by declaring known and unknown variables? - optimization

Question

cvxpy has a very neat way to write out the optimisation form without worrying too much about converting it into a "standard" matrix form as this is done internally somehow. Best to explain with an example:
def cvxpy_implementation():
var1 = cp.Variable()
var2 = cp.Variable()
constraints = [
var1 <= 3,
var2 >= 2
]
obj_fun = cp.Minimize(var1**2 + var2**2)
problem = cp.Problem(obj_fun, constraints)
problem.solve()
return var1.value, var2.value
def scipy_implementation1():
A = np.diag(np.ones(2))
lb = np.array([-np.inf, 2])
ub = np.array([3, np.inf])
con = LinearConstraint(A, lb, ub)
def obj_fun(x):
return (x**2).sum()
result = minimize(obj_fun, [0, 0], constraints=con)
return result.x
def scipy_implementation2():
con = [
{'type': 'ineq', 'fun': lambda x: 3 - x[0]},
{'type': 'ineq', 'fun': lambda x: x[1] - 2},]
def obj_fun(x):
return (x**2).sum()
result = minimize(obj_fun, [0, 0], constraints=con)
return result.x
All of the above give the correct result but the cvxpy implementation is much "easier" to write out, specifically I don't have to worry about the inequalities and can name variables useful thinks when writing out the inequalities. Compare that to the scipy1 and scipy2 implementations where in the first case I have to write out these extra infs and in the second case I have to remember which variable is which. You can imagine a case where I have 100 variables and while concatenating them will ultimately need to be done I'd like to be able to write it out like in cvxpy.
Question:
Has anyone implemented this for scipy? or is there an alternative library that could make this work?
thank you

Answer 1

Wrote something up that would do this and seems to cover the main issues I had in mind.
The general idea is you define variables and then create a simple expression as you would normally write it out and then the solver class optimises over the defined variables
https://github.com/evan54/optimisation/blob/master/var.py
The example below illustrates a simple use case
# fake data
a = 2
m = 3
x = np.linspace(0, 10)
y = a * x + m + np.random.randn(len(x))
a_ = Variable()
m_ = Variable()
y_ = a_ * x + m_
error = y_ - y
prob = Problem((error**2).sum(), None)
prob.minimize() print(f'a = {a}, a_ = {a_}') print(f'm = {m}, m_ = {m_}')