def optimise_eef(cfg, full_red=False):
"""Optimise using the reduced extended edge formulation (Constantino et al., EJOR, 2013).
Note that this implementation does not yet include chains, and throws an exception
if a chain cap greater than zero is used.
Args:
cfg: an OptConfig object
full_red: True if cycles should be generated in order to reduce number of variables further
Returns:
an OptSolution object
"""
if cfg.edge_success_prob != 1:
raise ValueError(
"This formulation does not support failure-aware matching.")
m = create_ip_model(cfg.timelimit, cfg.verbose)
m.params.method = 2
m.params.presolve = 0
# For each vertex v, a list of variables corresponding to in-edges to v
vtx_to_in_edges = [[] for __ in cfg.digraph.vs]
add_chain_vars_and_constraints(cfg.digraph, cfg.ndds, cfg.max_chain, m,
vtx_to_in_edges)
vars_and_edges = add_eef_vars_and_constraints(cfg.max_cycle, cfg.digraph,
m, full_red,
cfg.eef_alt_constraints,
vtx_to_in_edges)
obj_expr = quicksum(edge.score * var
for var, edge, low_v_id in vars_and_edges)
if cfg.max_chain > 0:
obj_expr += quicksum(e.score * e.edge_var for ndd in cfg.ndds
for e in ndd.edges)
obj_expr += quicksum(e.score * var for e in cfg.digraph.es
for var in e.grb_vars)
m.setObjective(obj_expr, GRB.MAXIMIZE)
optimise(m, cfg)
cycle_start_vv = []
cycle_next_vv = {}
for var, edge, low_v_id in vars_and_edges:
if var.x > 0.1:
cycle_next_vv[edge.src.id] = edge.tgt.id
cycle_start_vv.append(edge.src.id)
return OptSolution(ip_model=m,
cycles=kidney_utils.selected_edges_to_cycles(
cfg.digraph, cycle_start_vv, cycle_next_vv),
chains=[] if cfg.max_chain == 0 else
kidney_utils.get_optimal_chains(cfg.digraph, cfg.ndds),
digraph=cfg.digraph)
def optimise_eef(cfg, full_red=False):
"""Optimise using the reduced extended edge formulation (Constantino et al., EJOR, 2013).
Note that this implementation does not yet include chains, and throws an exception
if a chain cap greater than zero is used.
Args:
cfg: an OptConfig object
full_red: True if cycles should be generated in order to reduce number of variables further
Returns:
an OptSolution object
"""
if cfg.edge_success_prob != 1:
raise ValueError("This formulation does not support failure-aware matching.")
m = create_ip_model(cfg.timelimit, cfg.verbose)
m.params.method = 2
m.params.presolve = 0
# For each vertex v, a list of variables corresponding to in-edges to v
vtx_to_in_edges = [[] for __ in cfg.digraph.vs]
add_chain_vars_and_constraints(cfg.digraph, cfg.ndds, cfg.max_chain, m, vtx_to_in_edges)
vars_and_edges = add_eef_vars_and_constraints(
cfg.max_cycle, cfg.digraph, m, full_red, cfg.eef_alt_constraints, vtx_to_in_edges
)
obj_expr = quicksum(edge.score * var for var, edge, low_v_id in vars_and_edges)
if cfg.max_chain > 0:
obj_expr += quicksum(e.score * e.edge_var for ndd in cfg.ndds for e in ndd.edges)
obj_expr += quicksum(e.score * var for e in cfg.digraph.es for var in e.grb_vars)
m.setObjective(obj_expr, GRB.MAXIMIZE)
optimise(m, cfg)
cycle_start_vv = []
cycle_next_vv = {}
for var, edge, low_v_id in vars_and_edges:
if var.x > 0.1:
cycle_next_vv[edge.src.id] = edge.tgt.id
cycle_start_vv.append(edge.src.id)
return OptSolution(
ip_model=m,
cycles=kidney_utils.selected_edges_to_cycles(cfg.digraph, cycle_start_vv, cycle_next_vv),
chains=[] if cfg.max_chain == 0 else kidney_utils.get_optimal_chains(cfg.digraph, cfg.ndds),
digraph=cfg.digraph,
)
def optimise_uuef(cfg):
"""Optimise using the uncapped edge formulation.
Args:
cfg: an OptConfig object
Returns:
an OptSolution object
"""
if cfg.edge_success_prob != 1:
raise ValueError(
"This formulation does not support failure-aware matching.")
m = create_ip_model(cfg.timelimit, cfg.verbose)
add_unlimited_vars_and_constraints(cfg.digraph, cfg.ndds, m)
obj_expr = (quicksum(e.score * e.edge_var for ndd in cfg.ndds
for e in ndd.edges) + quicksum(e.score * var
for e in cfg.digraph.es
for var in e.grb_vars))
m.setObjective(obj_expr, GRB.MAXIMIZE)
optimise(m, cfg)
# Try all possible cycle start positions
cycle_start_vv = range(cfg.digraph.n)
cycle_next_vv = {}
for e in cfg.digraph.es:
for var in e.grb_vars:
if var.x > 0.1:
cycle_next_vv[e.src.id] = e.tgt.id
return OptSolution(
ip_model=m,
cycles=kidney_utils.selected_edges_to_cycles(cfg.digraph,
cycle_start_vv,
cycle_next_vv),
chains=kidney_utils.get_optimal_chains(cfg.digraph, cfg.ndds),
digraph=cfg.digraph)
def optimise_uuef(cfg):
"""Optimise using the uncapped edge formulation.
Args:
cfg: an OptConfig object
Returns:
an OptSolution object
"""
if cfg.edge_success_prob != 1:
raise ValueError("This formulation does not support failure-aware matching.")
m = create_ip_model(cfg.timelimit, cfg.verbose)
add_unlimited_vars_and_constraints(cfg.digraph, cfg.ndds, m)
obj_expr = quicksum(e.score * e.edge_var for ndd in cfg.ndds for e in ndd.edges) + quicksum(
e.score * var for e in cfg.digraph.es for var in e.grb_vars
)
m.setObjective(obj_expr, GRB.MAXIMIZE)
optimise(m, cfg)
# Try all possible cycle start positions
cycle_start_vv = range(cfg.digraph.n)
cycle_next_vv = {}
for e in cfg.digraph.es:
for var in e.grb_vars:
if var.x > 0.1:
cycle_next_vv[e.src.id] = e.tgt.id
return OptSolution(
ip_model=m,
cycles=kidney_utils.selected_edges_to_cycles(cfg.digraph, cycle_start_vv, cycle_next_vv),
chains=kidney_utils.get_optimal_chains(cfg.digraph, cfg.ndds),
digraph=cfg.digraph,
)
def optimise_hpief_prime(cfg, full_red=False, hpief_2_prime=False):
"""Optimise using the HPIEF' or HPIEF'' formulation.
The HPIEF' model is based on HPIEF, but does not include cycle-edge variables at position zero.
HPIEF'' also removes variables corresponding to edges at the last possible position of a cycle.
Args:
cfg: an OptConfig object
full_red: True if cycles should be generated in order to reduce number of variables further
hpief_2_prime: Use HPIEF''? Default: HPIEF'
Returns:
an OptSolution object
"""
if cfg.edge_success_prob != 1:
raise ValueError(
"This formulation does not support failure-aware matching.")
if cfg.max_cycle < 3:
hpief_2_prime = False
m = create_ip_model(cfg.timelimit, cfg.verbose)
m.params.method = 2
m.params.presolve = 0
# For each vertex v, a list of variables corresponding to in-edges to v
vtx_to_in_edges = [[] for __ in cfg.digraph.vs]
add_chain_vars_and_constraints(cfg.digraph, cfg.ndds, cfg.max_chain, m,
vtx_to_in_edges)
vars_and_edges = add_hpief_prime_vars_and_constraints(
cfg.max_cycle, cfg.digraph, vtx_to_in_edges, m, full_red,
hpief_2_prime)
obj_terms = []
for var, pos, edge, low_v_id in vars_and_edges:
score = edge.score
if pos == 1:
score += cfg.digraph.adj_mat[low_v_id][edge.src.id].score
if hpief_2_prime and pos == cfg.max_cycle - 2 and edge.tgt.id != low_v_id:
score += cfg.digraph.adj_mat[edge.tgt.id][low_v_id].score
obj_terms.append(score * var)
obj_expr = quicksum(obj_terms)
if cfg.max_chain > 0:
obj_expr += quicksum(e.score * e.edge_var for ndd in cfg.ndds
for e in ndd.edges)
obj_expr += quicksum(e.score * var for e in cfg.digraph.es
for var in e.grb_vars)
m.setObjective(obj_expr, GRB.MAXIMIZE)
optimise(m, cfg)
cycle_start_vv = []
cycle_next_vv = {}
for var, pos, edge, low_v_id in vars_and_edges:
if var.x > 0.1:
cycle_next_vv[edge.src.id] = edge.tgt.id
if pos == 1:
cycle_start_vv.append(low_v_id)
cycle_next_vv[low_v_id] = edge.src.id
if hpief_2_prime and pos == cfg.max_cycle - 2 and edge.tgt.id != low_v_id:
cycle_next_vv[edge.tgt.id] = low_v_id
return OptSolution(ip_model=m,
cycles=kidney_utils.selected_edges_to_cycles(
cfg.digraph, cycle_start_vv, cycle_next_vv),
chains=[] if cfg.max_chain == 0 else
kidney_utils.get_optimal_chains(cfg.digraph, cfg.ndds),
digraph=cfg.digraph)
def optimise_hpief_prime(cfg, full_red=False, hpief_2_prime=False):
"""Optimise using the HPIEF' or HPIEF'' formulation.
The HPIEF' model is based on HPIEF, but does not include cycle-edge variables at position zero.
HPIEF'' also removes variables corresponding to edges at the last possible position of a cycle.
Args:
cfg: an OptConfig object
full_red: True if cycles should be generated in order to reduce number of variables further
hpief_2_prime: Use HPIEF''? Default: HPIEF'
Returns:
an OptSolution object
"""
if cfg.edge_success_prob != 1:
raise ValueError("This formulation does not support failure-aware matching.")
if cfg.max_cycle < 3:
hpief_2_prime = False
m = create_ip_model(cfg.timelimit, cfg.verbose)
m.params.method = 2
m.params.presolve = 0
# For each vertex v, a list of variables corresponding to in-edges to v
vtx_to_in_edges = [[] for __ in cfg.digraph.vs]
add_chain_vars_and_constraints(cfg.digraph, cfg.ndds, cfg.max_chain, m, vtx_to_in_edges)
vars_and_edges = add_hpief_prime_vars_and_constraints(
cfg.max_cycle, cfg.digraph, vtx_to_in_edges, m, full_red, hpief_2_prime
)
obj_terms = []
for var, pos, edge, low_v_id in vars_and_edges:
score = edge.score
if pos == 1:
score += cfg.digraph.adj_mat[low_v_id][edge.src.id].score
if hpief_2_prime and pos == cfg.max_cycle - 2 and edge.tgt.id != low_v_id:
score += cfg.digraph.adj_mat[edge.tgt.id][low_v_id].score
obj_terms.append(score * var)
obj_expr = quicksum(obj_terms)
if cfg.max_chain > 0:
obj_expr += quicksum(e.score * e.edge_var for ndd in cfg.ndds for e in ndd.edges)
obj_expr += quicksum(e.score * var for e in cfg.digraph.es for var in e.grb_vars)
m.setObjective(obj_expr, GRB.MAXIMIZE)
optimise(m, cfg)
cycle_start_vv = []
cycle_next_vv = {}
for var, pos, edge, low_v_id in vars_and_edges:
if var.x > 0.1:
cycle_next_vv[edge.src.id] = edge.tgt.id
if pos == 1:
cycle_start_vv.append(low_v_id)
cycle_next_vv[low_v_id] = edge.src.id
if hpief_2_prime and pos == cfg.max_cycle - 2 and edge.tgt.id != low_v_id:
cycle_next_vv[edge.tgt.id] = low_v_id
return OptSolution(
ip_model=m,
cycles=kidney_utils.selected_edges_to_cycles(cfg.digraph, cycle_start_vv, cycle_next_vv),
chains=[] if cfg.max_chain == 0 else kidney_utils.get_optimal_chains(cfg.digraph, cfg.ndds),
digraph=cfg.digraph,
)
