... | ... |
@@ -319,350 +319,342 @@ |
319 | 319 |
} |
320 | 320 |
} |
321 | 321 |
return min < 0; |
322 | 322 |
} |
323 | 323 |
|
324 | 324 |
}; //class BestEligiblePivotRule |
325 | 325 |
|
326 | 326 |
|
327 | 327 |
// Implementation of the Block Search pivot rule |
328 | 328 |
class BlockSearchPivotRule |
329 | 329 |
{ |
330 | 330 |
private: |
331 | 331 |
|
332 | 332 |
// References to the NetworkSimplex class |
333 | 333 |
const IntVector &_source; |
334 | 334 |
const IntVector &_target; |
335 | 335 |
const CostVector &_cost; |
336 | 336 |
const IntVector &_state; |
337 | 337 |
const CostVector &_pi; |
338 | 338 |
int &_in_arc; |
339 | 339 |
int _search_arc_num; |
340 | 340 |
|
341 | 341 |
// Pivot rule data |
342 | 342 |
int _block_size; |
343 | 343 |
int _next_arc; |
344 | 344 |
|
345 | 345 |
public: |
346 | 346 |
|
347 | 347 |
// Constructor |
348 | 348 |
BlockSearchPivotRule(NetworkSimplex &ns) : |
349 | 349 |
_source(ns._source), _target(ns._target), |
350 | 350 |
_cost(ns._cost), _state(ns._state), _pi(ns._pi), |
351 | 351 |
_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num), |
352 | 352 |
_next_arc(0) |
353 | 353 |
{ |
354 | 354 |
// The main parameters of the pivot rule |
355 | 355 |
const double BLOCK_SIZE_FACTOR = 0.5; |
356 | 356 |
const int MIN_BLOCK_SIZE = 10; |
357 | 357 |
|
358 | 358 |
_block_size = std::max( int(BLOCK_SIZE_FACTOR * |
359 | 359 |
std::sqrt(double(_search_arc_num))), |
360 | 360 |
MIN_BLOCK_SIZE ); |
361 | 361 |
} |
362 | 362 |
|
363 | 363 |
// Find next entering arc |
364 | 364 |
bool findEnteringArc() { |
365 | 365 |
Cost c, min = 0; |
366 | 366 |
int cnt = _block_size; |
367 |
int e |
|
367 |
int e; |
|
368 | 368 |
for (e = _next_arc; e < _search_arc_num; ++e) { |
369 | 369 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
370 | 370 |
if (c < min) { |
371 | 371 |
min = c; |
372 |
|
|
372 |
_in_arc = e; |
|
373 | 373 |
} |
374 | 374 |
if (--cnt == 0) { |
375 |
if (min < 0) |
|
375 |
if (min < 0) goto search_end; |
|
376 | 376 |
cnt = _block_size; |
377 | 377 |
} |
378 | 378 |
} |
379 |
if (min == 0 || cnt > 0) { |
|
380 |
for (e = 0; e < _next_arc; ++e) { |
|
381 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
|
382 |
if (c < min) { |
|
383 |
min = c; |
|
384 |
min_arc = e; |
|
385 |
} |
|
386 |
if (--cnt == 0) { |
|
387 |
if (min < 0) break; |
|
388 |
cnt = _block_size; |
|
389 |
|
|
379 |
for (e = 0; e < _next_arc; ++e) { |
|
380 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
|
381 |
if (c < min) { |
|
382 |
min = c; |
|
383 |
_in_arc = e; |
|
384 |
} |
|
385 |
if (--cnt == 0) { |
|
386 |
if (min < 0) goto search_end; |
|
387 |
cnt = _block_size; |
|
390 | 388 |
} |
391 | 389 |
} |
392 | 390 |
if (min >= 0) return false; |
393 |
|
|
391 |
|
|
392 |
search_end: |
|
394 | 393 |
_next_arc = e; |
395 | 394 |
return true; |
396 | 395 |
} |
397 | 396 |
|
398 | 397 |
}; //class BlockSearchPivotRule |
399 | 398 |
|
400 | 399 |
|
401 | 400 |
// Implementation of the Candidate List pivot rule |
402 | 401 |
class CandidateListPivotRule |
403 | 402 |
{ |
404 | 403 |
private: |
405 | 404 |
|
406 | 405 |
// References to the NetworkSimplex class |
407 | 406 |
const IntVector &_source; |
408 | 407 |
const IntVector &_target; |
409 | 408 |
const CostVector &_cost; |
410 | 409 |
const IntVector &_state; |
411 | 410 |
const CostVector &_pi; |
412 | 411 |
int &_in_arc; |
413 | 412 |
int _search_arc_num; |
414 | 413 |
|
415 | 414 |
// Pivot rule data |
416 | 415 |
IntVector _candidates; |
417 | 416 |
int _list_length, _minor_limit; |
418 | 417 |
int _curr_length, _minor_count; |
419 | 418 |
int _next_arc; |
420 | 419 |
|
421 | 420 |
public: |
422 | 421 |
|
423 | 422 |
/// Constructor |
424 | 423 |
CandidateListPivotRule(NetworkSimplex &ns) : |
425 | 424 |
_source(ns._source), _target(ns._target), |
426 | 425 |
_cost(ns._cost), _state(ns._state), _pi(ns._pi), |
427 | 426 |
_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num), |
428 | 427 |
_next_arc(0) |
429 | 428 |
{ |
430 | 429 |
// The main parameters of the pivot rule |
431 |
const double LIST_LENGTH_FACTOR = |
|
430 |
const double LIST_LENGTH_FACTOR = 0.25; |
|
432 | 431 |
const int MIN_LIST_LENGTH = 10; |
433 | 432 |
const double MINOR_LIMIT_FACTOR = 0.1; |
434 | 433 |
const int MIN_MINOR_LIMIT = 3; |
435 | 434 |
|
436 | 435 |
_list_length = std::max( int(LIST_LENGTH_FACTOR * |
437 | 436 |
std::sqrt(double(_search_arc_num))), |
438 | 437 |
MIN_LIST_LENGTH ); |
439 | 438 |
_minor_limit = std::max( int(MINOR_LIMIT_FACTOR * _list_length), |
440 | 439 |
MIN_MINOR_LIMIT ); |
441 | 440 |
_curr_length = _minor_count = 0; |
442 | 441 |
_candidates.resize(_list_length); |
443 | 442 |
} |
444 | 443 |
|
445 | 444 |
/// Find next entering arc |
446 | 445 |
bool findEnteringArc() { |
447 | 446 |
Cost min, c; |
448 |
int e |
|
447 |
int e; |
|
449 | 448 |
if (_curr_length > 0 && _minor_count < _minor_limit) { |
450 | 449 |
// Minor iteration: select the best eligible arc from the |
451 | 450 |
// current candidate list |
452 | 451 |
++_minor_count; |
453 | 452 |
min = 0; |
454 | 453 |
for (int i = 0; i < _curr_length; ++i) { |
455 | 454 |
e = _candidates[i]; |
456 | 455 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
457 | 456 |
if (c < min) { |
458 | 457 |
min = c; |
459 |
|
|
458 |
_in_arc = e; |
|
460 | 459 |
} |
461 |
if (c >= 0) { |
|
460 |
else if (c >= 0) { |
|
462 | 461 |
_candidates[i--] = _candidates[--_curr_length]; |
463 | 462 |
} |
464 | 463 |
} |
465 |
if (min < 0) { |
|
466 |
_in_arc = min_arc; |
|
467 |
return true; |
|
468 |
} |
|
464 |
if (min < 0) return true; |
|
469 | 465 |
} |
470 | 466 |
|
471 | 467 |
// Major iteration: build a new candidate list |
472 | 468 |
min = 0; |
473 | 469 |
_curr_length = 0; |
474 | 470 |
for (e = _next_arc; e < _search_arc_num; ++e) { |
475 | 471 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
476 | 472 |
if (c < 0) { |
477 | 473 |
_candidates[_curr_length++] = e; |
478 | 474 |
if (c < min) { |
479 | 475 |
min = c; |
480 |
|
|
476 |
_in_arc = e; |
|
481 | 477 |
} |
482 |
if (_curr_length == _list_length) |
|
478 |
if (_curr_length == _list_length) goto search_end; |
|
483 | 479 |
} |
484 | 480 |
} |
485 |
if (_curr_length < _list_length) { |
|
486 |
for (e = 0; e < _next_arc; ++e) { |
|
487 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
|
488 |
if (c < 0) { |
|
489 |
_candidates[_curr_length++] = e; |
|
490 |
if (c < min) { |
|
491 |
min = c; |
|
492 |
min_arc = e; |
|
493 |
} |
|
494 |
if (_curr_length == _list_length) break; |
|
481 |
for (e = 0; e < _next_arc; ++e) { |
|
482 |
c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
|
483 |
if (c < 0) { |
|
484 |
_candidates[_curr_length++] = e; |
|
485 |
if (c < min) { |
|
486 |
min = c; |
|
487 |
_in_arc = e; |
|
495 | 488 |
} |
489 |
if (_curr_length == _list_length) goto search_end; |
|
496 | 490 |
} |
497 | 491 |
} |
498 | 492 |
if (_curr_length == 0) return false; |
493 |
|
|
494 |
search_end: |
|
499 | 495 |
_minor_count = 1; |
500 |
_in_arc = min_arc; |
|
501 | 496 |
_next_arc = e; |
502 | 497 |
return true; |
503 | 498 |
} |
504 | 499 |
|
505 | 500 |
}; //class CandidateListPivotRule |
506 | 501 |
|
507 | 502 |
|
508 | 503 |
// Implementation of the Altering Candidate List pivot rule |
509 | 504 |
class AlteringListPivotRule |
510 | 505 |
{ |
511 | 506 |
private: |
512 | 507 |
|
513 | 508 |
// References to the NetworkSimplex class |
514 | 509 |
const IntVector &_source; |
515 | 510 |
const IntVector &_target; |
516 | 511 |
const CostVector &_cost; |
517 | 512 |
const IntVector &_state; |
518 | 513 |
const CostVector &_pi; |
519 | 514 |
int &_in_arc; |
520 | 515 |
int _search_arc_num; |
521 | 516 |
|
522 | 517 |
// Pivot rule data |
523 | 518 |
int _block_size, _head_length, _curr_length; |
524 | 519 |
int _next_arc; |
525 | 520 |
IntVector _candidates; |
526 | 521 |
CostVector _cand_cost; |
527 | 522 |
|
528 | 523 |
// Functor class to compare arcs during sort of the candidate list |
529 | 524 |
class SortFunc |
530 | 525 |
{ |
531 | 526 |
private: |
532 | 527 |
const CostVector &_map; |
533 | 528 |
public: |
534 | 529 |
SortFunc(const CostVector &map) : _map(map) {} |
535 | 530 |
bool operator()(int left, int right) { |
536 | 531 |
return _map[left] > _map[right]; |
537 | 532 |
} |
538 | 533 |
}; |
539 | 534 |
|
540 | 535 |
SortFunc _sort_func; |
541 | 536 |
|
542 | 537 |
public: |
543 | 538 |
|
544 | 539 |
// Constructor |
545 | 540 |
AlteringListPivotRule(NetworkSimplex &ns) : |
546 | 541 |
_source(ns._source), _target(ns._target), |
547 | 542 |
_cost(ns._cost), _state(ns._state), _pi(ns._pi), |
548 | 543 |
_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num), |
549 | 544 |
_next_arc(0), _cand_cost(ns._search_arc_num), _sort_func(_cand_cost) |
550 | 545 |
{ |
551 | 546 |
// The main parameters of the pivot rule |
552 |
const double BLOCK_SIZE_FACTOR = 1. |
|
547 |
const double BLOCK_SIZE_FACTOR = 1.0; |
|
553 | 548 |
const int MIN_BLOCK_SIZE = 10; |
554 | 549 |
const double HEAD_LENGTH_FACTOR = 0.1; |
555 | 550 |
const int MIN_HEAD_LENGTH = 3; |
556 | 551 |
|
557 | 552 |
_block_size = std::max( int(BLOCK_SIZE_FACTOR * |
558 | 553 |
std::sqrt(double(_search_arc_num))), |
559 | 554 |
MIN_BLOCK_SIZE ); |
560 | 555 |
_head_length = std::max( int(HEAD_LENGTH_FACTOR * _block_size), |
561 | 556 |
MIN_HEAD_LENGTH ); |
562 | 557 |
_candidates.resize(_head_length + _block_size); |
563 | 558 |
_curr_length = 0; |
564 | 559 |
} |
565 | 560 |
|
566 | 561 |
// Find next entering arc |
567 | 562 |
bool findEnteringArc() { |
568 | 563 |
// Check the current candidate list |
569 | 564 |
int e; |
570 | 565 |
for (int i = 0; i < _curr_length; ++i) { |
571 | 566 |
e = _candidates[i]; |
572 | 567 |
_cand_cost[e] = _state[e] * |
573 | 568 |
(_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
574 | 569 |
if (_cand_cost[e] >= 0) { |
575 | 570 |
_candidates[i--] = _candidates[--_curr_length]; |
576 | 571 |
} |
577 | 572 |
} |
578 | 573 |
|
579 | 574 |
// Extend the list |
580 | 575 |
int cnt = _block_size; |
581 |
int last_arc = 0; |
|
582 | 576 |
int limit = _head_length; |
583 | 577 |
|
584 |
for ( |
|
578 |
for (e = _next_arc; e < _search_arc_num; ++e) { |
|
585 | 579 |
_cand_cost[e] = _state[e] * |
586 | 580 |
(_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
587 | 581 |
if (_cand_cost[e] < 0) { |
588 | 582 |
_candidates[_curr_length++] = e; |
589 |
last_arc = e; |
|
590 | 583 |
} |
591 | 584 |
if (--cnt == 0) { |
592 |
if (_curr_length > limit) |
|
585 |
if (_curr_length > limit) goto search_end; |
|
593 | 586 |
limit = 0; |
594 | 587 |
cnt = _block_size; |
595 | 588 |
} |
596 | 589 |
} |
597 |
if (_curr_length <= limit) { |
|
598 |
for (int e = 0; e < _next_arc; ++e) { |
|
599 |
_cand_cost[e] = _state[e] * |
|
600 |
(_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
|
601 |
if (_cand_cost[e] < 0) { |
|
602 |
_candidates[_curr_length++] = e; |
|
603 |
last_arc = e; |
|
604 |
} |
|
605 |
if (--cnt == 0) { |
|
606 |
if (_curr_length > limit) break; |
|
607 |
limit = 0; |
|
608 |
cnt = _block_size; |
|
609 |
|
|
590 |
for (e = 0; e < _next_arc; ++e) { |
|
591 |
_cand_cost[e] = _state[e] * |
|
592 |
(_cost[e] + _pi[_source[e]] - _pi[_target[e]]); |
|
593 |
if (_cand_cost[e] < 0) { |
|
594 |
_candidates[_curr_length++] = e; |
|
595 |
} |
|
596 |
if (--cnt == 0) { |
|
597 |
if (_curr_length > limit) goto search_end; |
|
598 |
limit = 0; |
|
599 |
cnt = _block_size; |
|
610 | 600 |
} |
611 | 601 |
} |
612 | 602 |
if (_curr_length == 0) return false; |
613 |
|
|
603 |
|
|
604 |
search_end: |
|
614 | 605 |
|
615 | 606 |
// Make heap of the candidate list (approximating a partial sort) |
616 | 607 |
make_heap( _candidates.begin(), _candidates.begin() + _curr_length, |
617 | 608 |
_sort_func ); |
618 | 609 |
|
619 | 610 |
// Pop the first element of the heap |
620 | 611 |
_in_arc = _candidates[0]; |
612 |
_next_arc = e; |
|
621 | 613 |
pop_heap( _candidates.begin(), _candidates.begin() + _curr_length, |
622 | 614 |
_sort_func ); |
623 | 615 |
_curr_length = std::min(_head_length, _curr_length - 1); |
624 | 616 |
return true; |
625 | 617 |
} |
626 | 618 |
|
627 | 619 |
}; //class AlteringListPivotRule |
628 | 620 |
|
629 | 621 |
public: |
630 | 622 |
|
631 | 623 |
/// \brief Constructor. |
632 | 624 |
/// |
633 | 625 |
/// The constructor of the class. |
634 | 626 |
/// |
635 | 627 |
/// \param graph The digraph the algorithm runs on. |
636 | 628 |
NetworkSimplex(const GR& graph) : |
637 | 629 |
_graph(graph), _node_id(graph), _arc_id(graph), |
638 | 630 |
INF(std::numeric_limits<Value>::has_infinity ? |
639 | 631 |
std::numeric_limits<Value>::infinity() : |
640 | 632 |
std::numeric_limits<Value>::max()) |
641 | 633 |
{ |
642 | 634 |
// Check the value types |
643 | 635 |
LEMON_ASSERT(std::numeric_limits<Value>::is_signed, |
644 | 636 |
"The flow type of NetworkSimplex must be signed"); |
645 | 637 |
LEMON_ASSERT(std::numeric_limits<Cost>::is_signed, |
646 | 638 |
"The cost type of NetworkSimplex must be signed"); |
647 | 639 |
|
648 | 640 |
// Resize vectors |
649 | 641 |
_node_num = countNodes(_graph); |
650 | 642 |
_arc_num = countArcs(_graph); |
651 | 643 |
int all_node_num = _node_num + 1; |
652 | 644 |
int max_arc_num = _arc_num + 2 * _node_num; |
653 | 645 |
|
654 | 646 |
_source.resize(max_arc_num); |
655 | 647 |
_target.resize(max_arc_num); |
656 | 648 |
|
657 | 649 |
_lower.resize(_arc_num); |
658 | 650 |
_upper.resize(_arc_num); |
659 | 651 |
_cap.resize(max_arc_num); |
660 | 652 |
_cost.resize(max_arc_num); |
661 | 653 |
_supply.resize(all_node_num); |
662 | 654 |
_flow.resize(max_arc_num); |
663 | 655 |
_pi.resize(all_node_num); |
664 | 656 |
|
665 | 657 |
_parent.resize(all_node_num); |
666 | 658 |
_pred.resize(all_node_num); |
667 | 659 |
_forward.resize(all_node_num); |
668 | 660 |
_thread.resize(all_node_num); |
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