Force all floating point arithmetics to use float32 instead of float64?

[myultimatespm]

By default, floating point arithmetic operations use float64 i.e. double.

The following are 2 python examples from both pulp and pyomo for the sum operation.

For float64 it works exactly as expected, but I need it to do arithmetics in float32 instead.

import pulp
import pyomo.environ as pyo
import numpy as np


def pulp_example(values, target_float):
    variables = pulp.LpVariable.dicts("x", range(len(values)), 0, 1, pulp.LpBinary)
    problem = pulp.LpProblem("Problem", pulp.LpMinimize)
    problem += 0
    problem += pulp.lpSum([variables[i] for i in range(len(values))]) == 2
    sum_exp = pulp.lpSum([variables[i] * float(values[i]) for i in range(len(values))])
    problem += sum_exp == target_float
    status = problem.solve()
    return status


def pyomo_example(values, target_float):
    model = pyo.ConcreteModel()
    model.I = pyo.RangeSet(0, len(values) - 1)
    model.x = pyo.Var(model.I, within=pyo.Binary)
    model.selection_constraint = pyo.Constraint(expr=sum(model.x[i] for i in model.I) == 2)

    def sum_expr_rule(model):
        return sum(model.x[i] * values[i] for i in model.I)
    model.eq_expr = pyo.Expression(rule=sum_expr_rule)
    model.constraint = pyo.Constraint(expr=model.eq_expr == target_float)
    model.objective = pyo.Objective(expr=0, sense=pyo.minimize)
    solver = pyo.SolverFactory('cbc')
    result = solver.solve(model, tee=False)
    return result.solver.status


input_values = [90.2743351459503174, 4.8144315292034]

# As expected, summation of same input values results in different outputs depending on float data type
float32_target = 95.0887680053711  # float32 arithmetic
float64_target = 95.08876667515372  # float64 arithmetic

assert np.sum([input_values], dtype=np.float32) == float32_target
assert np.sum([input_values], dtype=np.float64) == float64_target


pulp_res = pulp_example(input_values, float32_target)  # 'Infeasible
pyomo_res = pyomo_example(input_values, float32_target)  # 'warning'

pulp_res = pulp_example(input_values, float64_target)  # 'Optimal'
pyomo_res = pyomo_example(input_values, float64_target)  # 'ok'