Classes | |
class | ConshdlrSubtour |
class | EventhdlrNewSol |
class | Heur2opt |
class | HeurFarthestInsert |
class | HeurFrats |
class | ProbDataTSP |
class | ReaderTSP |
Functions | |
def | distance (x1, y1, x2, y2) |
def | make_data (n) |
SCIP_RETCODE | SCIPcreateConsSubtour (SCIP *scip, SCIP_CONS **cons, const char *name, GRAPH *graph, SCIP_Bool initial, SCIP_Bool separate, SCIP_Bool enforce, SCIP_Bool check, SCIP_Bool propagate, SCIP_Bool local, SCIP_Bool modifiable, SCIP_Bool dynamic, SCIP_Bool removable) |
def | solve_tsp (V, c) |
Variables | |
c | |
edges | |
int | n = 200 |
obj | |
int | seed = 1 |
V | |
x | |
y | |
def tsp.distance | ( | x1, | |
y1, | |||
x2, | |||
y2 | |||
) |
def tsp.make_data | ( | n | ) |
make_data: compute matrix distance based on euclidean distance
Definition at line 101 of file tsp.py.
References distance().
def tsp.solve_tsp | ( | V, | |
c | |||
) |
solve_tsp -- solve the traveling salesman problem - start with assignment model - add cuts until there are no sub-cycles Parameters: - V: set/list of nodes in the graph - c[i,j]: cost for traversing edge (i,j) Returns the optimum objective value and the list of edges used.
Definition at line 21 of file tsp.py.
References pyscipopt.expr.quicksum().