def findSolutionValues(self, lp):
prob = lp.solverModel
if self.mip and self.hasMIPConstraints(lp.solverModel):
solutionStatus = glpk.glp_mip_status(prob)
else:
solutionStatus = glpk.glp_get_status(prob)
glpkLpStatus = {
glpk.GLP_OPT: LpStatusOptimal,
glpk.GLP_UNDEF: LpStatusUndefined,
glpk.GLP_FEAS: LpStatusNotSolved,
glpk.GLP_INFEAS: LpStatusInfeasible,
glpk.GLP_NOFEAS: LpStatusInfeasible,
glpk.GLP_UNBND: LpStatusUnbounded
}
#populate pulp solution values
for var in lp.variables():
if self.mip and self.hasMIPConstraints(lp.solverModel):
var.varValue = glpk.glp_mip_col_val(prob, var.glpk_index)
else:
var.varValue = glpk.glp_get_col_prim(prob, var.glpk_index)
var.dj = glpk.glp_get_col_dual(prob, var.glpk_index)
#put pi and slack variables against the constraints
for constr in lp.constraints.values():
if self.mip and self.hasMIPConstraints(lp.solverModel):
row_val = glpk.glp_mip_row_val(prob, constr.glpk_index)
else:
row_val = glpk.glp_get_row_prim(prob, constr.glpk_index)
constr.slack = -constr.constant - row_val
constr.pi = glpk.glp_get_row_dual(prob, constr.glpk_index)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
lp.status = glpkLpStatus.get(solutionStatus, LpStatusUndefined)
return lp.status
def callSolver(self, lp, callback=None):
"""Solves the problem with glpk"""
self.solveTime = -clock()
glpk.glp_adv_basis(lp.solverModel, 0)
glpk.glp_simplex(lp.solverModel, None)
if self.mip and self.hasMIPConstraints(lp.solverModel):
status = glpk.glp_get_status(lp.solverModel)
if status in (glpk.GLP_OPT, glpk.GLP_UNDEF, glpk.GLP_FEAS):
glpk.glp_intopt(lp.solverModel, None)
self.solveTime += clock()
def solve_problem(self, problem, solverConfig):
if (solverConfig["simplex"] in ["True", "true", "1", "TRUE"]):
lp_solver_str = "using simplex"
simplex = True
else:
lp_solver_str = "using interior point"
simplex = False
if (solverConfig["presolve"] in ["True", "true", "1", "TRUE"]):
presolv_str = "with ILP presolver"
lp_solver_str = ""
presolve = True
else:
presolv_str = "no ILP presolver"
presolve = False
logging.info("CSR Optimizer solving problem using %s %s", presolv_str,
lp_solver_str)
if (False == presolve):
if (True == simplex):
logging.info("CSR Optimizer solving problem using simplex...")
glpk_lp_param = glpki.glp_smcp()
glpki.glp_init_smcp(glpk_lp_param)
glpk_lp_param.msg_lev = glpki.GLP_MSG_OFF
glpki.glp_simplex(problem, glpk_lp_param)
res = glpki.glp_get_status(problem)
else:
logging.info(
"CSR Optimizer solving problem using interior point...")
glpk_lp_param = glpki.glp_iptcp()
glpki.glp_init_iptcp(glpk_lp_param)
glpk_lp_param.msg_lev = glpki.GLP_MSG_OFF
glpki.glp_interior(problem, glpk_lp_param)
res = glpki.glp_ipt_status(problem)
if (res == glpki.GLP_OPT):
logging.info("Linear optimal")
else:
logging.warning("Problem is linear unfeasible")
return problem
glpk_ilp_param = glpki.glp_iocp()
glpki.glp_init_iocp(glpk_ilp_param)
if (True == presolve):
glpk_ilp_param.presolve = glpki.GLP_ON
else:
glpk_ilp_param.presolve = glpki.GLP_OFF
glpk_ilp_param.msg_lev = glpki.GLP_MSG_OFF
glpki.glp_intopt(problem, glpk_ilp_param)
res = glpki.glp_mip_status(problem)
if (res == glpki.GLP_OPT):
logging.info("Integer optimal")
else:
logging.warning("Problem is integer unfeasible")
return problem
return problem