def stuffed_objective(self, problem, extractor):
# Extract to c.T * x + r; c is represented by a ma
c = extractor.affine(problem.objective.expr)
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(extractor.x_length, boolean=boolean, integer=integer)
return c, x
def stuffed_objective(self, problem, extractor):
# extract to x.T * P * x + q.T * x + r
expr = problem.objective.expr.copy()
P, q, r = extractor.quad_form(expr)
# concatenate all variables in one vector
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(extractor.x_length, boolean=boolean, integer=integer)
new_obj = QuadForm(x, P) + q.T @ x
return new_obj, x, r
def stuffed_objective(self, problem, extractor):
# Extract to c.T * x + r
C, R = extractor.affine(problem.objective.expr)
c = C.toarray().flatten()
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(extractor.N, boolean=boolean, integer=integer)
new_obj = c.T * x + 0
return new_obj, x, R[0]
def stuffed_objective(self, problem, extractor):
# extract to x.T * P * x + q.T * x + r
# TODO need to copy objective?
P, q, r = extractor.quad_form(problem.objective.expr)
# concatenate all variables in one vector
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(extractor.N, boolean=boolean, integer=integer)
new_obj = QuadForm(x, P) + q.T*x
return new_obj, x, r
def stuffed_objective(self, problem, extractor):
# extract to 0.5 * x.T * P * x + q.T * x + r
expr = problem.objective.expr.copy()
params_to_P, params_to_q = extractor.quad_form(expr)
# Handle 0.5 factor.
params_to_P = 2 * params_to_P
# concatenate all variables in one vector
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(extractor.x_length, boolean=boolean, integer=integer)
return params_to_P, params_to_q, x
def stuffed_objective(self, problem, inverse_data):
extractor = CoeffExtractor(inverse_data)
# Extract to c.T * x, store r
C, R = extractor.get_coeffs(problem.objective.expr)
c = np.asarray(C.todense()).flatten()
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(inverse_data.x_length, boolean=boolean, integer=integer)
new_obj = c.T * x + 0
inverse_data.r = R[0]
return new_obj, x
def stuffed_objective(self, problem, inverse_data):
# We need to copy the problem because we are changing atoms in the
# expression tree
problem_copy = problems.problem.Problem(
Minimize(problem.objective.expr.tree_copy()),
[con.tree_copy() for con in problem.constraints])
inverse_data_of_copy = InverseData(problem_copy)
extractor = CoeffExtractor(inverse_data_of_copy)
# extract to x.T * P * x + q.T * x, store r
P, q, r = extractor.quad_form(problem_copy.objective.expr)
# concatenate all variables in one vector
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(inverse_data.x_length, boolean=boolean, integer=integer)
new_obj = QuadForm(x, P) + q.T * x
inverse_data.r = r
return new_obj, x
def stuffed_objective(self, problem, extractor):
# Extract to c.T * x + r
C, R = extractor.affine(problem.objective.expr)
c = C.toarray().flatten()
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(extractor.N, boolean=boolean, integer=integer)
# CHANGE --------------------------
# set the value of the variable. We use NaNs to
# show which values are unknown. This would not
# satisfy the requirements imposed on x.value
# Hence, we have to set the variable x._value
# directly to circumvent the check.
# x.value will be set when the problem is solved, the
# inconsistent value will be overwritten soon anyway.
x._value = stack_vals(problem.variables())
# ----------------------------------
new_obj = c.T * x + 0
return new_obj, x, R[0]
