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cbc.py
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cbc.py
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"""Python-MIP interface to the COIN-OR Branch-and-Cut solver CBC"""
from typing import Dict, List, Tuple, Optional
from sys import platform, maxsize
from os.path import dirname, isfile
import os
from cffi import FFI
from mip.model import xsum
import mip
from mip import Model, Var, Constr, Column, LinExpr, VConstrList, VVarList, Solver
from mip.constants import MAXIMIZE, SearchEmphasis, CONTINUOUS, BINARY, \
INTEGER, MINIMIZE, EQUAL, LESS_OR_EQUAL, GREATER_OR_EQUAL, \
OptimizationStatus, LP_Method
warningMessages = 0
ffi = FFI()
CData = ffi.CData
has_cbc = False
os_is_64_bit = maxsize > 2 ** 32
INF = float('inf')
cut_idx = 0
# for variables and rows
MAX_NAME_SIZE = 512
DEF_PUMPP = 30
try:
pathmip = dirname(mip.__file__)
pathlib = os.path.join(pathmip, 'libraries')
libfile = ''
if 'linux' in platform.lower():
if os_is_64_bit:
libfile = os.path.join(pathlib, 'cbc-c-linux-x86-64.so')
elif platform.lower().startswith('win'):
if os_is_64_bit:
libfile = os.path.join(pathlib, 'cbc-c-windows-x86-64.dll')
else:
libfile = os.path.join(pathlib, 'cbc-c-windows-x86-32.dll')
elif platform.lower().startswith('darwin') or platform.lower().startswith('macos'):
if os_is_64_bit:
libfile = os.path.join(pathlib, 'cbc-c-darwin-x86-64.dylib')
if not libfile:
raise Exception(
"You operating system/platform is not supported")
cbclib = ffi.dlopen(libfile)
has_cbc = True
except Exception:
has_cbc = False
print('cbc not found')
if has_cbc:
ffi.cdef("""
typedef int(*cbc_progress_callback)(void *model,
int phase,
int step,
const char *phaseName,
double seconds,
double lb,
double ub,
int nint,
int *vecint,
void *cbData
);
typedef void(*cbc_callback)(void *model, int msgno, int ndouble,
const double *dvec, int nint, const int *ivec,
int nchar, char **cvec);
typedef void(*cbc_cut_callback)(void *osiSolver,
void *osiCuts, void *appdata);
typedef int (*cbc_incumbent_callback)(void *cbcModel,
double obj, int nz,
char **vnames, double *x, void *appData);
typedef void Cbc_Model;
void *Cbc_newModel();
void Cbc_readLp(Cbc_Model *model, const char *file);
void Cbc_readMps(Cbc_Model *model, const char *file);
void Cbc_writeLp(Cbc_Model *model, const char *file);
void Cbc_writeMps(Cbc_Model *model, const char *file);
int Cbc_getNumCols(Cbc_Model *model);
int Cbc_getNumRows(Cbc_Model *model);
int Cbc_getNumIntegers(Cbc_Model *model);
int Cbc_getNumElements(Cbc_Model *model);
int Cbc_getRowNz(Cbc_Model *model, int row);
int *Cbc_getRowIndices(Cbc_Model *model, int row);
double *Cbc_getRowCoeffs(Cbc_Model *model, int row);
double Cbc_getRowRHS(Cbc_Model *model, int row);
void Cbc_setRowRHS(Cbc_Model *model, int row, double rhs);
char Cbc_getRowSense(Cbc_Model *model, int row);
const double *Cbc_getRowActivity(Cbc_Model *model);
int Cbc_getColNz(Cbc_Model *model, int col);
int *Cbc_getColIndices(Cbc_Model *model, int col);
double *Cbc_getColCoeffs(Cbc_Model *model, int col);
void Cbc_addCol(Cbc_Model *model, const char *name,
double lb, double ub, double obj, char isInteger,
int nz, int *rows, double *coefs);
void Cbc_addRow(Cbc_Model *model, const char *name, int nz,
const int *cols, const double *coefs, char sense, double rhs);
void Cbc_addLazyConstraint(Cbc_Model *model, int nz,
int *cols, double *coefs, char sense, double rhs);
void Cbc_addSOS(Cbc_Model *model, int numRows, const int *rowStarts,
const int *colIndices, const double *weights, const int type);
void Cbc_setObjCoeff(Cbc_Model *model, int index, double value);
double Cbc_getObjSense(Cbc_Model *model);
const double *Cbc_getObjCoefficients(Cbc_Model *model);
const double *Cbc_getColSolution(Cbc_Model *model);
const double *Cbc_getReducedCost(Cbc_Model *model);
double *Cbc_bestSolution(Cbc_Model *model);
int Cbc_numberSavedSolutions(Cbc_Model *model);
const double *Cbc_savedSolution(Cbc_Model *model, int whichSol);
double Cbc_savedSolutionObj(Cbc_Model *model, int whichSol);
double Cbc_getObjValue(Cbc_Model *model);
void Cbc_setObjSense(Cbc_Model *model, double sense);
int Cbc_isProvenOptimal(Cbc_Model *model);
int Cbc_isProvenInfeasible(Cbc_Model *model);
int Cbc_isContinuousUnbounded(Cbc_Model *model);
int Cbc_isAbandoned(Cbc_Model *model);
const double *Cbc_getColLower(Cbc_Model *model);
const double *Cbc_getColUpper(Cbc_Model *model);
void Cbc_setColLower(Cbc_Model *model, int index, double value);
void Cbc_setColUpper(Cbc_Model *model, int index, double value);
int Cbc_isInteger(Cbc_Model *model, int i);
void Cbc_getColName(Cbc_Model *model,
int iColumn, char *name, size_t maxLength);
void Cbc_getRowName(Cbc_Model *model,
int iRow, char *name, size_t maxLength);
void Cbc_setContinuous(Cbc_Model *model, int iColumn);
void Cbc_setInteger(Cbc_Model *model, int iColumn);
void Cbc_setParameter(Cbc_Model *model, const char *name,
const char *value);
double Cbc_getCutoff(Cbc_Model *model);
void Cbc_setCutoff(Cbc_Model *model, double cutoff);
double Cbc_getAllowableGap(Cbc_Model *model);
void Cbc_setAllowableGap(Cbc_Model *model, double allowedGap);
double Cbc_getAllowableFractionGap(Cbc_Model *model);
void Cbc_setAllowableFractionGap(Cbc_Model *model,
double allowedFracionGap);
double Cbc_getAllowablePercentageGap(Cbc_Model *model);
void Cbc_setAllowablePercentageGap(Cbc_Model *model,
double allowedPercentageGap);
double Cbc_getMaximumSeconds(Cbc_Model *model);
void Cbc_setMaximumSeconds(Cbc_Model *model, double maxSeconds);
int Cbc_getMaximumNodes(Cbc_Model *model);
void Cbc_setMaximumNodes(Cbc_Model *model, int maxNodes);
int Cbc_getMaximumSolutions(Cbc_Model *model);
void Cbc_setMaximumSolutions(Cbc_Model *model, int maxSolutions);
int Cbc_getLogLevel(Cbc_Model *model);
void Cbc_setLogLevel(Cbc_Model *model, int logLevel);
double Cbc_getBestPossibleObjValue(Cbc_Model *model);
void Cbc_setMIPStart(Cbc_Model *model, int count,
const char **colNames, const double colValues[]);
void Cbc_setMIPStartI(Cbc_Model *model, int count, const int colIdxs[],
const double colValues[]);
enum LPMethod {
LPM_Auto = 0, /*! Solver will decide automatically which method to use */
LPM_Dual = 1, /*! Dual simplex */
LPM_Primal = 2, /*! Primal simplex */
LPM_Barrier = 3 /*! The barrier algorithm. */
};
void
Cbc_setLPmethod(Cbc_Model *model, enum LPMethod lpm );
int Cbc_solve(Cbc_Model *model);
void *Cbc_deleteModel(Cbc_Model *model);
int Osi_getNumIntegers( void *osi );
const double *Osi_getReducedCost( void *osi );
const double *Osi_getObjCoefficients();
double Osi_getObjSense();
void *Osi_newSolver();
void Osi_deleteSolver( void *osi );
void Osi_initialSolve(void *osi);
void Osi_resolve(void *osi);
void Osi_branchAndBound(void *osi);
char Osi_isAbandoned(void *osi);
char Osi_isProvenOptimal(void *osi);
char Osi_isProvenPrimalInfeasible(void *osi);
char Osi_isProvenDualInfeasible(void *osi);
char Osi_isPrimalObjectiveLimitReached(void *osi);
char Osi_isDualObjectiveLimitReached(void *osi);
char Osi_isIterationLimitReached(void *osi);
double Osi_getObjValue( void *osi );
void Osi_setColUpper (void *osi, int elementIndex, double ub);
void Osi_setColLower(void *osi, int elementIndex, double lb);
int Osi_getNumCols( void *osi );
void Osi_getColName( void *osi, int i, char *name, int maxLen );
const double *Osi_getColLower( void *osi );
const double *Osi_getColUpper( void *osi );
int Osi_isInteger( void *osi, int col );
int Osi_getNumRows( void *osi );
int Osi_getRowNz(void *osi, int row);
const int *Osi_getRowIndices(void *osi, int row);
const double *Osi_getRowCoeffs(void *osi, int row);
double Osi_getRowRHS(void *osi, int row);
char Osi_getRowSense(void *osi, int row);
void Osi_setObjCoef(void *osi, int index, double obj);
void Osi_setObjSense(void *osi, double sense);
const double *Osi_getColSolution(void *osi);
void OsiCuts_addRowCut( void *osiCuts, int nz, const int *idx,
const double *coef, char sense, double rhs );
void Osi_addCol(void *osi, const char *name, double lb, double ub,
double obj, char isInteger, int nz, int *rows, double *coefs);
void Osi_addRow(void *osi, const char *name, int nz,
const int *cols, const double *coefs, char sense, double rhs);
void Cbc_deleteRows(Cbc_Model *model, int numRows, const int rows[]);
void Cbc_deleteCols(Cbc_Model *model, int numCols, const int cols[]);
void Cbc_storeNameIndexes(Cbc_Model *model, char _store);
int Cbc_getColNameIndex(Cbc_Model *model, const char *name);
int Cbc_getRowNameIndex(Cbc_Model *model, const char *name);
void Cbc_problemName(Cbc_Model *model, int maxNumberCharacters,
char *array);
int Cbc_setProblemName(Cbc_Model *model, const char *array);
double Cbc_getPrimalTolerance(Cbc_Model *model);
void Cbc_setPrimalTolerance(Cbc_Model *model, double tol);
double Cbc_getDualTolerance(Cbc_Model *model);
void Cbc_setDualTolerance(Cbc_Model *model, double tol);
void Cbc_addCutCallback(Cbc_Model *model, cbc_cut_callback cutcb,
const char *name, void *appData, int howOften, char atSolution );
void Cbc_addIncCallback(
void *model, cbc_incumbent_callback inccb,
void *appData );
void Cbc_registerCallBack(Cbc_Model *model,
cbc_callback userCallBack);
void Cbc_addProgrCallback(void *model,
cbc_progress_callback prgcbc, void *appData);
void Cbc_clearCallBack(Cbc_Model *model);
const double *Cbc_getRowPrice(Cbc_Model *model);
const double *Osi_getRowPrice(void *osi);
double Osi_getIntegerTolerance(void *osi);
""")
CHAR_ONE = "{}".format(chr(1)).encode("utf-8")
CHAR_ZERO = "\0".encode("utf-8")
Osi_getNumCols = cbclib.Osi_getNumCols
Osi_getColSolution = cbclib.Osi_getColSolution
Osi_getIntegerTolerance = cbclib.Osi_getIntegerTolerance
Osi_isInteger = cbclib.Osi_isInteger
Osi_isProvenOptimal = cbclib.Osi_isProvenOptimal
def cbc_set_parameter(model: Model, param: str, value: str):
cbclib.Cbc_setParameter(model._model, param.encode("utf-8"),
value.encode("utf-8"))
class SolverCbc(Solver):
def __init__(self, model: Model, name: str, sense: str):
super().__init__(model, name, sense)
self._model = cbclib.Cbc_newModel()
cbclib.Cbc_storeNameIndexes(self._model, CHAR_ONE)
self.iidx_space = 4096
self.iidx = ffi.new('int[%d]' % self.iidx_space)
self.dvec = ffi.new('double[%d]' % self.iidx_space)
self._objconst = 0.0
# to not add cut generators twice when reoptimizing
self.added_cut_callback = False
self.added_inc_callback = False
# setting objective sense
if sense == MAXIMIZE:
cbclib.Cbc_setObjSense(self._model, -1.0)
self.emphasis = SearchEmphasis.DEFAULT
self.__threads = 0
self.__verbose = 1
# pre-allocate temporary space to query names
self.__name_space = ffi.new("char[{}]".format(MAX_NAME_SIZE))
# in cut generation
self.__name_spacec = ffi.new("char[{}]".format(MAX_NAME_SIZE))
self.__log = []
self.set_problem_name(name)
self.__pumpp = DEF_PUMPP
def add_var(self,
obj: float = 0,
lb: float = 0,
ub: float = float("inf"),
coltype: str = "C",
column: "Column" = None,
name: str = ""):
# collecting column data
numnz = 0 if column is None else len(column.constrs)
if not numnz:
vind = ffi.NULL
vval = ffi.NULL
else:
vind = ffi.new("int[]", [c.idx for c in column.constrs])
vval = ffi.new("double[]", [coef for coef in column.coeffs])
isInt = \
CHAR_ONE if coltype.upper() == "B" or coltype.upper() == "I" \
else CHAR_ZERO
cbclib.Cbc_addCol(
self._model, name.encode("utf-8"),
lb, ub, obj,
isInt, numnz, vind, vval)
def get_objective_const(self) -> float:
return self._objconst
def get_objective(self) -> LinExpr:
obj = cbclib.Cbc_getObjCoefficients(self._model)
if obj == ffi.NULL:
raise Exception("Error getting objective function coefficients")
return xsum(obj[j] * self.model.vars[j] for j in range(self.num_cols())
if abs(obj[j]) >= 1e-15) + self._objconst
def set_objective(self, lin_expr: "LinExpr", sense: str = "") -> None:
# collecting variable coefficients
for var, coeff in lin_expr.expr.items():
cbclib.Cbc_setObjCoeff(self._model, var.idx, coeff)
# objective function constant
self._objconst = lin_expr.const
# setting objective sense
if sense == MAXIMIZE:
cbclib.Cbc_setObjSense(self._model, -1.0)
elif sense == MINIMIZE:
cbclib.Cbc_setObjSense(self._model, 1.0)
def relax(self):
for var in self.model.vars:
if cbclib.Cbc_isInteger(self._model, var.idx):
cbclib.Cbc_setContinuous(self._model, var.idx)
def get_max_seconds(self) -> float:
return cbclib.Cbc_getMaximumSeconds(self._model)
def set_max_seconds(self, max_seconds: float):
cbclib.Cbc_setMaximumSeconds(self._model, max_seconds)
def get_max_solutions(self) -> int:
return cbclib.Cbc_getMaximumSolutions(self._model)
def set_max_solutions(self, max_solutions: int):
cbclib.Cbc_setMaximumSolutions(self._model, max_solutions)
def get_max_nodes(self) -> int:
return cbclib.Cbc_getMaximumNodes(self._model)
def set_max_nodes(self, max_nodes: int):
cbclib.Cbc_setMaximumNodes(self._model, max_nodes)
def get_verbose(self) -> int:
return self.__verbose
def set_verbose(self, verbose: int):
self.__verbose = verbose
def var_set_var_type(self, var: "Var", value: str):
cv = var.var_type
if (value == cv):
return
if cv == CONTINUOUS:
if value == INTEGER or value == BINARY:
cbclib.Cbc_setInteger(self._model, var.idx)
else:
if value == CONTINUOUS:
cbclib.Cbc_setContinuous(self._model, var.idx)
if value == BINARY:
# checking bounds
if var.lb != 0.0:
var.lb = 0.0
if var.ub != 1.0:
var.ub = 1.0
def var_set_obj(self, var: "Var", value: float):
cbclib.Cbc_setObjCoeff(self._model, var.idx, value)
def optimize(self) -> OptimizationStatus:
self.__evtimes = {}
# get name indexes from an osi problem
def cbc_get_osi_name_indexes(osi_solver: CData) -> Dict[str, int]:
nameIdx = {}
n = cbclib.Osi_getNumCols(osi_solver)
for i in range(n):
cbclib.Osi_getColName(osi_solver, i, self.__name_spacec,
MAX_NAME_SIZE)
cname = ffi.string(self.__name_spacec).decode('utf-8')
nameIdx[cname] = i
return nameIdx
# progress callback
@ffi.callback("""
int (void *, int, int, const char *, double, double, double,
int, int *, void *)
""")
def cbc_progress_callback(model: CData, phase: int, step: int,
phaseName: CData, seconds: float,
lb: float, ub: float, nint: int, vint: CData,
cbData: CData) -> int:
self.__log.append((seconds, (lb, ub)))
return -1
# incumbent callback
def cbc_inc_callback(cbc_model: CData,
obj: float, nz: int,
colNames: CData,
colValues: CData,
appData: CData):
return
# cut callback
@ffi.callback("""
void (void *osi_solver, void *osi_cuts, void *app_data)
""")
def cbc_cut_callback(osi_solver: CData, osi_cuts: CData,
app_data: CData):
if osi_solver == ffi.NULL or osi_cuts == ffi.NULL or \
(self.model.cuts_generator is None
and self.model.lazy_constrs_generator is None):
return
if Osi_isProvenOptimal(osi_solver) != CHAR_ONE:
return
# checking if solution is fractional or not
nc = Osi_getNumCols(osi_solver)
x = Osi_getColSolution(osi_solver)
itol = Osi_getIntegerTolerance(osi_solver)
fractional = False
for j in range(nc):
if Osi_isInteger(osi_solver, j):
if abs(x[j] - round(x[j])) > itol:
fractional = True
break
osi_model = ModelOsi(osi_solver)
osi_model._status = osi_model.solver.get_status()
osi_model.solver.osi_cutsp = osi_cuts
osi_model.fractional = fractional
if fractional and self.model.cuts_generator:
self.model.cuts_generator.generate_constrs(osi_model)
if (not fractional) and self.model.lazy_constrs_generator:
self.model.lazy_constrs_generator.generate_constrs(osi_model)
# adding cut generators
m = self.model
if m.cuts_generator is not None:
atSol = CHAR_ZERO
cbclib.Cbc_addCutCallback(self._model, cbc_cut_callback,
'UserCuts'.encode('utf-8'),
ffi.NULL, 1, atSol)
if m.lazy_constrs_generator is not None:
atSol = CHAR_ONE
cbc_set_parameter(self, "preprocess", "off")
cbc_set_parameter(self, "heur", "off")
cbclib.Cbc_addCutCallback(self._model, cbc_cut_callback,
'LazyConstraints'.encode('utf-8'),
ffi.NULL, 1, atSol)
if self.__verbose == 0:
cbclib.Cbc_setLogLevel(self._model, 0)
else:
cbclib.Cbc_setLogLevel(self._model, 1)
if self.emphasis == SearchEmphasis.FEASIBILITY:
cbc_set_parameter(self, 'passf', '50')
cbc_set_parameter(self, 'proximity', 'on')
if self.emphasis == SearchEmphasis.OPTIMALITY:
cbc_set_parameter(self, 'strong', '10')
cbc_set_parameter(self, 'trust', '20')
cbc_set_parameter(self, 'lagomory', 'endonly')
cbc_set_parameter(self, 'latwomir', 'endonly')
if self.__pumpp != DEF_PUMPP:
cbc_set_parameter(self, 'passf', '{}'.format(self.__pumpp))
if self.model.cuts == 0:
cbc_set_parameter(self, 'cuts', 'off')
if self.model.cuts >= 1:
cbc_set_parameter(self, 'cuts', 'on')
if self.model.cuts >= 2:
cbc_set_parameter(self, 'lagomory',
'endcleanroot')
cbc_set_parameter(self, 'latwomir',
'endcleanroot')
cbc_set_parameter(self, 'passC', '-25')
if self.model.cuts >= 3:
cbc_set_parameter(self, 'passC', '-35')
cbc_set_parameter(self, 'lift', 'ifmove')
if (self.__threads >= 1):
cbc_set_parameter(self, 'timeM',
'{}'.format('elapsed'))
cbc_set_parameter(self, 'threads',
'{}'.format(self.__threads))
elif self.__threads == -1:
import multiprocessing
cbc_set_parameter(self, 'threads',
'{}'.format(multiprocessing.cpu_count()))
if self.model.preprocess == 0:
cbc_set_parameter(self, 'preprocess', 'off')
elif self.model.preprocess == 1:
cbc_set_parameter(self, 'preprocess', 'sos')
if self.model.cut_passes != -1:
cbc_set_parameter(self, 'passc', '{}'.format(
self.model.cut_passes))
if self.model.clique == 0:
cbc_set_parameter(self, 'clique', 'off')
elif self.model.clique == 1:
cbc_set_parameter(self, 'clique', 'forceon')
cbc_set_parameter(self, 'maxSavedSolutions', '10')
if self.model.store_search_progress_log:
cbclib.Cbc_addProgrCallback(self._model,
cbc_progress_callback, ffi.NULL)
if self.model.integer_tol >= 0.0:
cbc_set_parameter(self, 'integerT',
'{}'.format(self.model.integer_tol))
if self.model.infeas_tol >= 0.0:
cbclib.Cbc_setPrimalTolerance(self._model, self.model.infeas_tol)
if self.model.opt_tol >= 0.0:
cbclib.Cbc_setDualTolerance(self._model, self.model.opt_tol)
if self.model.lp_method == LP_Method.BARRIER:
cbclib.Cbc_setLPmethod(self._model, cbclib.LPM_Barrier)
elif self.model.lp_method == LP_Method.DUAL:
cbclib.Cbc_setLPmethod(self._model, cbclib.LPM_Dual)
elif self.model.lp_method == LP_Method.PRIMAL:
cbclib.Cbc_setLPmethod(self._model, cbclib.LPM_Primal)
else:
cbclib.Cbc_setLPmethod(self._model, cbclib.LPM_Auto)
cbclib.Cbc_solve(self._model)
if cbclib.Cbc_isAbandoned(self._model):
return OptimizationStatus.ERROR
if cbclib.Cbc_isProvenOptimal(self._model):
return OptimizationStatus.OPTIMAL
if cbclib.Cbc_isProvenInfeasible(self._model):
return OptimizationStatus.INFEASIBLE
if cbclib.Cbc_isContinuousUnbounded(self._model):
return OptimizationStatus.UNBOUNDED
if cbclib.Cbc_getNumIntegers(self._model):
if cbclib.Cbc_bestSolution(self._model):
return OptimizationStatus.FEASIBLE
return OptimizationStatus.NO_SOLUTION_FOUND
def get_objective_sense(self) -> str:
obj = cbclib.Cbc_getObjSense(self._model)
if obj < 0.0:
return MAXIMIZE
return MINIMIZE
def set_objective_sense(self, sense: str):
if sense.strip().upper() == MAXIMIZE.strip().upper():
cbclib.Cbc_setObjSense(self._model, -1.0)
elif sense.strip().upper() == MINIMIZE.strip().upper():
cbclib.Cbc_setObjSense(self._model, 1.0)
else:
raise Exception("Unknown sense: {}, use {} or {}".format(sense,
MAXIMIZE,
MINIMIZE))
def get_objective_value(self) -> float:
return cbclib.Cbc_getObjValue(self._model) + self._objconst
def get_status(self) -> OptimizationStatus:
if cbclib.Cbc_isAbandoned(self._model):
return OptimizationStatus.ERROR
if cbclib.Cbc_isProvenOptimal(self._model):
return OptimizationStatus.OPTIMAL
if cbclib.Cbc_isProvenInfeasible(self._model):
return OptimizationStatus.INFEASIBLE
if cbclib.Cbc_isContinuousUnbounded(self._model):
return OptimizationStatus.UNBOUNDED
if cbclib.Cbc_getNumIntegers(self._model):
if cbclib.Cbc_bestSolution(self._model):
return OptimizationStatus.FEASIBLE
return OptimizationStatus.NO_SOLUTION_FOUND
def get_log(self) -> List[Tuple[float, Tuple[float, float]]]:
return self.__log
def get_objective_bound(self) -> float:
return cbclib.Cbc_getBestPossibleObjValue(self._model) + self._objconst
def var_get_x(self, var: Var) -> float:
if cbclib.Cbc_getNumIntegers(self._model) > 0:
x = cbclib.Cbc_bestSolution(self._model)
else:
x = cbclib.Cbc_getColSolution(self._model)
if x == ffi.NULL:
raise Exception('no solution found')
return float(x[var.idx])
def get_num_solutions(self) -> int:
return cbclib.Cbc_numberSavedSolutions(self._model)
def get_objective_value_i(self, i: int) -> float:
return cbclib.Cbc_savedSolutionObj(self._model, i) + self._objconst
def var_get_xi(self, var: "Var", i: int) -> float:
x = cbclib.Cbc_savedSolution(self._model, i)
if x == ffi.NULL:
raise Exception('no solution available')
return float(x[var.idx])
def var_get_rc(self, var: Var) -> float:
rc = cbclib.Cbc_getReducedCost(self._model)
if rc == ffi.NULL:
raise Exception('reduced cost not available')
return float(rc[var.idx])
def var_get_lb(self, var: "Var") -> float:
lb = cbclib.Cbc_getColLower(self._model)
if lb == ffi.NULL:
raise Exception('Error while getting lower bound of variables')
return float(lb[var.idx])
def var_set_lb(self, var: "Var", value: float):
cbclib.Cbc_setColLower(self._model, var.idx, value)
def var_get_ub(self, var: "Var") -> float:
ub = cbclib.Cbc_getColUpper(self._model)
if ub == ffi.NULL:
raise Exception('Error while getting upper bound of variables')
return float(ub[var.idx])
def var_set_ub(self, var: "Var", value: float):
cbclib.Cbc_setColUpper(self._model, var.idx, value)
def var_get_name(self, idx: int) -> str:
namep = self.__name_space
cbclib.Cbc_getColName(self._model, idx, namep, MAX_NAME_SIZE)
return ffi.string(namep).decode('utf-8')
def var_get_index(self, name: str) -> int:
return cbclib.Cbc_getColNameIndex(self._model, name.encode("utf-8"))
def constr_get_index(self, name: str) -> int:
return cbclib.Cbc_getRowNameIndex(self._model, name.encode("utf-8"))
def constr_get_rhs(self, idx: int) -> float:
return float(cbclib.Cbc_getRowRHS(self._model, idx))
def constr_set_rhs(self, idx: int, rhs: float):
cbclib.Cbc_setRowRHS(self._model, idx, rhs)
def var_get_obj(self, var: Var) -> float:
obj = cbclib.Cbc_getObjCoefficients(self._model)
if obj == ffi.NULL:
raise Exception("Error getting objective function coefficients")
return obj[var.idx]
def var_get_var_type(self, var: "Var") -> str:
isInt = cbclib.Cbc_isInteger(self._model, var.idx)
if isInt:
lb = self.var_get_lb(var)
ub = self.var_get_ub(var)
if abs(lb) <= 1e-15 and abs(ub - 1.0) <= 1e-15:
return BINARY
else:
return INTEGER
return CONTINUOUS
def var_get_column(self, var: "Var") -> Column:
numnz = cbclib.Cbc_getColNz(self._model, var.idx)
cidx = cbclib.Cbc_getColIndices(self._model, var.idx)
if cidx == ffi.NULL:
raise Exception("Error getting column indices'")
ccoef = cbclib.Cbc_getColCoeffs(self._model, var.idx)
col = Column()
for i in range(numnz):
col.constrs.append(Constr(self, cidx[i]))
col.coeffs.append(ccoef[i])
return col
def add_constr(self, lin_expr: LinExpr, name: str = ""):
# collecting linear expression data
numnz = len(lin_expr.expr)
if numnz > self.iidx_space:
self.iidx_space = max(numnz, self.iidx_space * 2)
self.iidx = ffi.new('int[%d]' % self.iidx_space)
self.dvec = ffi.new('double[%d]' % self.iidx_space)
# cind = self.iidx
self.iidx = [var.idx for var in lin_expr.expr.keys()]
# cind = ffi.new("int[]", [var.idx for var in lin_expr.expr.keys()])
# cval = ffi.new("double[]", [coef for coef in lin_expr.expr.values()])
# cval = self.dvec
self.dvec = [coef for coef in lin_expr.expr.values()]
# constraint sense and rhs
sense = lin_expr.sense.encode("utf-8")
rhs = -lin_expr.const
namestr = name.encode("utf-8")
mp = self._model
cbclib.Cbc_addRow(mp, namestr, numnz, self.iidx, self.dvec, sense, rhs)
def add_lazy_constr(self, lin_expr: LinExpr):
# collecting linear expression data
numnz = len(lin_expr.expr)
cind = ffi.new("int[]", [var.idx for var in lin_expr.expr.keys()])
cval = ffi.new("double[]", [coef for coef in lin_expr.expr.values()])
# constraint sense and rhs
sense = lin_expr.sense.encode("utf-8")
rhs = -lin_expr.const
mp = self._model
cbclib.Cbc_addLazyConstraint(mp, numnz, cind, cval, sense, rhs)
def add_sos(self, sos: List[Tuple["Var", float]], sos_type: int):
starts = ffi.new("int[]", [0, len(sos)])
idx = ffi.new("int[]", [v.idx for (v, f) in sos])
w = ffi.new("double[]", [f for (v, f) in sos])
cbclib.Cbc_addSOS(self._model, 1, starts, idx, w, sos_type)
def add_cut(self, lin_expr: LinExpr):
global cut_idx
name = 'cut{}'.format(cut_idx)
self.add_constr(lin_expr, name)
def write(self, file_path: str):
fpstr = file_path.encode("utf-8")
if ".mps" in file_path.lower():
cbclib.Cbc_writeMps(self._model, fpstr)
elif ".lp" in file_path.lower():
cbclib.Cbc_writeLp(self._model, fpstr)
else:
raise Exception("Enter a valid extension (.lp or .mps) \
to indicate the file format")
def read(self, file_path: str) -> None:
if not isfile(file_path):
raise Exception('File {} does not exists'.format(file_path))
fpstr = file_path.encode("utf-8")
if ".mps" in file_path.lower():
cbclib.Cbc_readMps(self._model, fpstr)
elif ".lp" in file_path.lower():
cbclib.Cbc_readLp(self._model, fpstr)
else:
raise Exception("Enter a valid extension (.lp or .mps) \
to indicate the file format")
def set_start(self, start: List[Tuple[Var, float]]) -> None:
n = len(start)
dv = ffi.new("double[]", [start[i][1] for i in range(n)])
iv = ffi.new("int[]", [start[i][0].idx for i in range(n)])
mdl = self._model
cbclib.Cbc_setMIPStartI(mdl, n, iv, dv)
def num_cols(self) -> int:
return cbclib.Cbc_getNumCols(self._model)
def num_int(self) -> int:
return cbclib.Cbc_getNumIntegers(self._model)
def num_rows(self) -> int:
return cbclib.Cbc_getNumRows(self._model)
def num_nz(self) -> int:
return cbclib.Cbc_getNumElements(self._model)
def get_cutoff(self) -> float:
return cbclib.Cbc_getCutoff(self._model)
def set_cutoff(self, cutoff: float):
cbclib.Cbc_setCutoff(self._model, cutoff)
def get_mip_gap_abs(self) -> float:
return cbclib.Cbc_getAllowableGap(self._model)
def set_mip_gap_abs(self, allowable_gap: float):
cbclib.Cbc_setAllowableGap(self._model, allowable_gap)
def get_mip_gap(self) -> float:
return cbclib.Cbc_getAllowableFractionGap(self._model)
def set_mip_gap(self, allowable_ratio_gap: float):
cbclib.Cbc_setAllowableFractionGap(self._model, allowable_ratio_gap)
def constr_get_expr(self, constr: Constr) -> LinExpr:
numnz = cbclib.Cbc_getRowNz(self._model, constr.idx)
ridx = cbclib.Cbc_getRowIndices(self._model, constr.idx)
if ridx == ffi.NULL:
raise Exception("Error getting row indices.")
rcoef = cbclib.Cbc_getRowCoeffs(self._model, constr.idx)
if rcoef == ffi.NULL:
raise Exception("Error getting row coefficients.")
rhs = cbclib.Cbc_getRowRHS(self._model, constr.idx)
rsense = cbclib.Cbc_getRowSense(self._model,
constr.idx).decode("utf-8").upper()
sense = ''
if (rsense == 'E'):
sense = EQUAL
elif (rsense == 'L'):
sense = LESS_OR_EQUAL
elif (rsense == 'G'):
sense = GREATER_OR_EQUAL
else:
raise Exception('Unknow sense: {}'.format(rsense))
expr = LinExpr(const=-rhs, sense=sense)
for i in range(numnz):
expr.add_var(self.model.vars[ridx[i]], rcoef[i])
return expr
def constr_get_name(self, idx: int) -> str:
namep = self.__name_space
cbclib.Cbc_getRowName(self._model, idx,
namep, MAX_NAME_SIZE)
return ffi.string(namep).decode('utf-8')
def set_processing_limits(self,
max_time: float = INF,
max_nodes: int = INF,
max_sol: int = INF):
if max_time != INF:
cbc_set_parameter(self, 'timeMode', 'elapsed')
self.set_max_seconds(max_time)
if max_nodes != INF:
self.set_max_nodes(max_nodes)
if max_sol != INF:
self.set_max_solutions(max_sol)
def get_emphasis(self) -> SearchEmphasis:
return self.emphasis
def set_emphasis(self, emph: SearchEmphasis):
self.emphasis = emph
def set_num_threads(self, threads: int):
self.__threads = threads
def remove_constrs(self, constrs: List[int]):
idx = ffi.new("int[]", constrs)
cbclib.Cbc_deleteRows(self._model, len(constrs), idx)
def remove_vars(self, varsList: List[int]):