2 * Copyright 2008-2009 Katholieke Universiteit Leuven
4 * Use of this software is governed by the MIT license
6 * Written by Sven Verdoolaege, K.U.Leuven, Departement
7 * Computerwetenschappen, Celestijnenlaan 200A, B-3001 Leuven, Belgium
10 #include <isl_ctx_private.h>
11 #include <isl_map_private.h>
12 #include "isl_sample.h"
13 #include "isl_sample_piplib.h"
17 #include "isl_equalities.h"
19 #include "isl_basis_reduction.h"
20 #include <isl_factorization.h>
21 #include <isl_point_private.h>
22 #include <isl_options_private.h>
23 #include <isl_vec_private.h>
25 static struct isl_vec
*empty_sample(struct isl_basic_set
*bset
)
29 vec
= isl_vec_alloc(bset
->ctx
, 0);
30 isl_basic_set_free(bset
);
34 /* Construct a zero sample of the same dimension as bset.
35 * As a special case, if bset is zero-dimensional, this
36 * function creates a zero-dimensional sample point.
38 static struct isl_vec
*zero_sample(struct isl_basic_set
*bset
)
41 struct isl_vec
*sample
;
43 dim
= isl_basic_set_total_dim(bset
);
44 sample
= isl_vec_alloc(bset
->ctx
, 1 + dim
);
46 isl_int_set_si(sample
->el
[0], 1);
47 isl_seq_clr(sample
->el
+ 1, dim
);
49 isl_basic_set_free(bset
);
53 static struct isl_vec
*interval_sample(struct isl_basic_set
*bset
)
57 struct isl_vec
*sample
;
59 bset
= isl_basic_set_simplify(bset
);
62 if (isl_basic_set_plain_is_empty(bset
))
63 return empty_sample(bset
);
64 if (bset
->n_eq
== 0 && bset
->n_ineq
== 0)
65 return zero_sample(bset
);
67 sample
= isl_vec_alloc(bset
->ctx
, 2);
72 isl_int_set_si(sample
->block
.data
[0], 1);
75 isl_assert(bset
->ctx
, bset
->n_eq
== 1, goto error
);
76 isl_assert(bset
->ctx
, bset
->n_ineq
== 0, goto error
);
77 if (isl_int_is_one(bset
->eq
[0][1]))
78 isl_int_neg(sample
->el
[1], bset
->eq
[0][0]);
80 isl_assert(bset
->ctx
, isl_int_is_negone(bset
->eq
[0][1]),
82 isl_int_set(sample
->el
[1], bset
->eq
[0][0]);
84 isl_basic_set_free(bset
);
89 if (isl_int_is_one(bset
->ineq
[0][1]))
90 isl_int_neg(sample
->block
.data
[1], bset
->ineq
[0][0]);
92 isl_int_set(sample
->block
.data
[1], bset
->ineq
[0][0]);
93 for (i
= 1; i
< bset
->n_ineq
; ++i
) {
94 isl_seq_inner_product(sample
->block
.data
,
95 bset
->ineq
[i
], 2, &t
);
96 if (isl_int_is_neg(t
))
100 if (i
< bset
->n_ineq
) {
101 isl_vec_free(sample
);
102 return empty_sample(bset
);
105 isl_basic_set_free(bset
);
108 isl_basic_set_free(bset
);
109 isl_vec_free(sample
);
113 static struct isl_mat
*independent_bounds(struct isl_basic_set
*bset
)
116 struct isl_mat
*dirs
= NULL
;
117 struct isl_mat
*bounds
= NULL
;
123 dim
= isl_basic_set_n_dim(bset
);
124 bounds
= isl_mat_alloc(bset
->ctx
, 1+dim
, 1+dim
);
128 isl_int_set_si(bounds
->row
[0][0], 1);
129 isl_seq_clr(bounds
->row
[0]+1, dim
);
132 if (bset
->n_ineq
== 0)
135 dirs
= isl_mat_alloc(bset
->ctx
, dim
, dim
);
137 isl_mat_free(bounds
);
140 isl_seq_cpy(dirs
->row
[0], bset
->ineq
[0]+1, dirs
->n_col
);
141 isl_seq_cpy(bounds
->row
[1], bset
->ineq
[0], bounds
->n_col
);
142 for (j
= 1, n
= 1; n
< dim
&& j
< bset
->n_ineq
; ++j
) {
145 isl_seq_cpy(dirs
->row
[n
], bset
->ineq
[j
]+1, dirs
->n_col
);
147 pos
= isl_seq_first_non_zero(dirs
->row
[n
], dirs
->n_col
);
150 for (i
= 0; i
< n
; ++i
) {
152 pos_i
= isl_seq_first_non_zero(dirs
->row
[i
], dirs
->n_col
);
157 isl_seq_elim(dirs
->row
[n
], dirs
->row
[i
], pos
,
159 pos
= isl_seq_first_non_zero(dirs
->row
[n
], dirs
->n_col
);
167 isl_int
*t
= dirs
->row
[n
];
168 for (k
= n
; k
> i
; --k
)
169 dirs
->row
[k
] = dirs
->row
[k
-1];
173 isl_seq_cpy(bounds
->row
[n
], bset
->ineq
[j
], bounds
->n_col
);
180 static void swap_inequality(struct isl_basic_set
*bset
, int a
, int b
)
182 isl_int
*t
= bset
->ineq
[a
];
183 bset
->ineq
[a
] = bset
->ineq
[b
];
187 /* Skew into positive orthant and project out lineality space.
189 * We perform a unimodular transformation that turns a selected
190 * maximal set of linearly independent bounds into constraints
191 * on the first dimensions that impose that these first dimensions
192 * are non-negative. In particular, the constraint matrix is lower
193 * triangular with positive entries on the diagonal and negative
195 * If "bset" has a lineality space then these constraints (and therefore
196 * all constraints in bset) only involve the first dimensions.
197 * The remaining dimensions then do not appear in any constraints and
198 * we can select any value for them, say zero. We therefore project
199 * out this final dimensions and plug in the value zero later. This
200 * is accomplished by simply dropping the final columns of
201 * the unimodular transformation.
203 static struct isl_basic_set
*isl_basic_set_skew_to_positive_orthant(
204 struct isl_basic_set
*bset
, struct isl_mat
**T
)
206 struct isl_mat
*U
= NULL
;
207 struct isl_mat
*bounds
= NULL
;
209 unsigned old_dim
, new_dim
;
215 isl_assert(bset
->ctx
, isl_basic_set_n_param(bset
) == 0, goto error
);
216 isl_assert(bset
->ctx
, bset
->n_div
== 0, goto error
);
217 isl_assert(bset
->ctx
, bset
->n_eq
== 0, goto error
);
219 old_dim
= isl_basic_set_n_dim(bset
);
220 /* Try to move (multiples of) unit rows up. */
221 for (i
= 0, j
= 0; i
< bset
->n_ineq
; ++i
) {
222 int pos
= isl_seq_first_non_zero(bset
->ineq
[i
]+1, old_dim
);
225 if (isl_seq_first_non_zero(bset
->ineq
[i
]+1+pos
+1,
229 swap_inequality(bset
, i
, j
);
232 bounds
= independent_bounds(bset
);
235 new_dim
= bounds
->n_row
- 1;
236 bounds
= isl_mat_left_hermite(bounds
, 1, &U
, NULL
);
239 U
= isl_mat_drop_cols(U
, 1 + new_dim
, old_dim
- new_dim
);
240 bset
= isl_basic_set_preimage(bset
, isl_mat_copy(U
));
244 isl_mat_free(bounds
);
247 isl_mat_free(bounds
);
249 isl_basic_set_free(bset
);
253 /* Find a sample integer point, if any, in bset, which is known
254 * to have equalities. If bset contains no integer points, then
255 * return a zero-length vector.
256 * We simply remove the known equalities, compute a sample
257 * in the resulting bset, using the specified recurse function,
258 * and then transform the sample back to the original space.
260 static struct isl_vec
*sample_eq(struct isl_basic_set
*bset
,
261 struct isl_vec
*(*recurse
)(struct isl_basic_set
*))
264 struct isl_vec
*sample
;
269 bset
= isl_basic_set_remove_equalities(bset
, &T
, NULL
);
270 sample
= recurse(bset
);
271 if (!sample
|| sample
->size
== 0)
274 sample
= isl_mat_vec_product(T
, sample
);
278 /* Return a matrix containing the equalities of the tableau
279 * in constraint form. The tableau is assumed to have
280 * an associated bset that has been kept up-to-date.
282 static struct isl_mat
*tab_equalities(struct isl_tab
*tab
)
287 struct isl_basic_set
*bset
;
292 bset
= isl_tab_peek_bset(tab
);
293 isl_assert(tab
->mat
->ctx
, bset
, return NULL
);
295 n_eq
= tab
->n_var
- tab
->n_col
+ tab
->n_dead
;
296 if (tab
->empty
|| n_eq
== 0)
297 return isl_mat_alloc(tab
->mat
->ctx
, 0, tab
->n_var
);
298 if (n_eq
== tab
->n_var
)
299 return isl_mat_identity(tab
->mat
->ctx
, tab
->n_var
);
301 eq
= isl_mat_alloc(tab
->mat
->ctx
, n_eq
, tab
->n_var
);
304 for (i
= 0, j
= 0; i
< tab
->n_con
; ++i
) {
305 if (tab
->con
[i
].is_row
)
307 if (tab
->con
[i
].index
>= 0 && tab
->con
[i
].index
>= tab
->n_dead
)
310 isl_seq_cpy(eq
->row
[j
], bset
->eq
[i
] + 1, tab
->n_var
);
312 isl_seq_cpy(eq
->row
[j
],
313 bset
->ineq
[i
- bset
->n_eq
] + 1, tab
->n_var
);
316 isl_assert(bset
->ctx
, j
== n_eq
, goto error
);
323 /* Compute and return an initial basis for the bounded tableau "tab".
325 * If the tableau is either full-dimensional or zero-dimensional,
326 * the we simply return an identity matrix.
327 * Otherwise, we construct a basis whose first directions correspond
330 static struct isl_mat
*initial_basis(struct isl_tab
*tab
)
336 tab
->n_unbounded
= 0;
337 tab
->n_zero
= n_eq
= tab
->n_var
- tab
->n_col
+ tab
->n_dead
;
338 if (tab
->empty
|| n_eq
== 0 || n_eq
== tab
->n_var
)
339 return isl_mat_identity(tab
->mat
->ctx
, 1 + tab
->n_var
);
341 eq
= tab_equalities(tab
);
342 eq
= isl_mat_left_hermite(eq
, 0, NULL
, &Q
);
347 Q
= isl_mat_lin_to_aff(Q
);
351 /* Compute the minimum of the current ("level") basis row over "tab"
352 * and store the result in position "level" of "min".
354 static enum isl_lp_result
compute_min(isl_ctx
*ctx
, struct isl_tab
*tab
,
355 __isl_keep isl_vec
*min
, int level
)
357 return isl_tab_min(tab
, tab
->basis
->row
[1 + level
],
358 ctx
->one
, &min
->el
[level
], NULL
, 0);
361 /* Compute the maximum of the current ("level") basis row over "tab"
362 * and store the result in position "level" of "max".
364 static enum isl_lp_result
compute_max(isl_ctx
*ctx
, struct isl_tab
*tab
,
365 __isl_keep isl_vec
*max
, int level
)
367 enum isl_lp_result res
;
368 unsigned dim
= tab
->n_var
;
370 isl_seq_neg(tab
->basis
->row
[1 + level
] + 1,
371 tab
->basis
->row
[1 + level
] + 1, dim
);
372 res
= isl_tab_min(tab
, tab
->basis
->row
[1 + level
],
373 ctx
->one
, &max
->el
[level
], NULL
, 0);
374 isl_seq_neg(tab
->basis
->row
[1 + level
] + 1,
375 tab
->basis
->row
[1 + level
] + 1, dim
);
376 isl_int_neg(max
->el
[level
], max
->el
[level
]);
381 /* Perform a greedy search for an integer point in the set represented
382 * by "tab", given that the minimal rational value (rounded up to the
383 * nearest integer) at "level" is smaller than the maximal rational
384 * value (rounded down to the nearest integer).
386 * Return 1 if we have found an integer point (if tab->n_unbounded > 0
387 * then we may have only found integer values for the bounded dimensions
388 * and it is the responsibility of the caller to extend this solution
389 * to the unbounded dimensions).
390 * Return 0 if greedy search did not result in a solution.
391 * Return -1 if some error occurred.
393 * We assign a value half-way between the minimum and the maximum
394 * to the current dimension and check if the minimal value of the
395 * next dimension is still smaller than (or equal) to the maximal value.
396 * We continue this process until either
397 * - the minimal value (rounded up) is greater than the maximal value
398 * (rounded down). In this case, greedy search has failed.
399 * - we have exhausted all bounded dimensions, meaning that we have
401 * - the sample value of the tableau is integral.
402 * - some error has occurred.
404 static int greedy_search(isl_ctx
*ctx
, struct isl_tab
*tab
,
405 __isl_keep isl_vec
*min
, __isl_keep isl_vec
*max
, int level
)
407 struct isl_tab_undo
*snap
;
408 enum isl_lp_result res
;
410 snap
= isl_tab_snap(tab
);
413 isl_int_add(tab
->basis
->row
[1 + level
][0],
414 min
->el
[level
], max
->el
[level
]);
415 isl_int_fdiv_q_ui(tab
->basis
->row
[1 + level
][0],
416 tab
->basis
->row
[1 + level
][0], 2);
417 isl_int_neg(tab
->basis
->row
[1 + level
][0],
418 tab
->basis
->row
[1 + level
][0]);
419 if (isl_tab_add_valid_eq(tab
, tab
->basis
->row
[1 + level
]) < 0)
421 isl_int_set_si(tab
->basis
->row
[1 + level
][0], 0);
423 if (++level
>= tab
->n_var
- tab
->n_unbounded
)
425 if (isl_tab_sample_is_integer(tab
))
428 res
= compute_min(ctx
, tab
, min
, level
);
429 if (res
== isl_lp_error
)
431 if (res
!= isl_lp_ok
)
432 isl_die(ctx
, isl_error_internal
,
433 "expecting bounded rational solution",
435 res
= compute_max(ctx
, tab
, max
, level
);
436 if (res
== isl_lp_error
)
438 if (res
!= isl_lp_ok
)
439 isl_die(ctx
, isl_error_internal
,
440 "expecting bounded rational solution",
442 } while (isl_int_le(min
->el
[level
], max
->el
[level
]));
444 if (isl_tab_rollback(tab
, snap
) < 0)
450 /* Given a tableau representing a set, find and return
451 * an integer point in the set, if there is any.
453 * We perform a depth first search
454 * for an integer point, by scanning all possible values in the range
455 * attained by a basis vector, where an initial basis may have been set
456 * by the calling function. Otherwise an initial basis that exploits
457 * the equalities in the tableau is created.
458 * tab->n_zero is currently ignored and is clobbered by this function.
460 * The tableau is allowed to have unbounded direction, but then
461 * the calling function needs to set an initial basis, with the
462 * unbounded directions last and with tab->n_unbounded set
463 * to the number of unbounded directions.
464 * Furthermore, the calling functions needs to add shifted copies
465 * of all constraints involving unbounded directions to ensure
466 * that any feasible rational value in these directions can be rounded
467 * up to yield a feasible integer value.
468 * In particular, let B define the given basis x' = B x
469 * and let T be the inverse of B, i.e., X = T x'.
470 * Let a x + c >= 0 be a constraint of the set represented by the tableau,
471 * or a T x' + c >= 0 in terms of the given basis. Assume that
472 * the bounded directions have an integer value, then we can safely
473 * round up the values for the unbounded directions if we make sure
474 * that x' not only satisfies the original constraint, but also
475 * the constraint "a T x' + c + s >= 0" with s the sum of all
476 * negative values in the last n_unbounded entries of "a T".
477 * The calling function therefore needs to add the constraint
478 * a x + c + s >= 0. The current function then scans the first
479 * directions for an integer value and once those have been found,
480 * it can compute "T ceil(B x)" to yield an integer point in the set.
481 * Note that during the search, the first rows of B may be changed
482 * by a basis reduction, but the last n_unbounded rows of B remain
483 * unaltered and are also not mixed into the first rows.
485 * The search is implemented iteratively. "level" identifies the current
486 * basis vector. "init" is true if we want the first value at the current
487 * level and false if we want the next value.
489 * At the start of each level, we first check if we can find a solution
490 * using greedy search. If not, we continue with the exhaustive search.
492 * The initial basis is the identity matrix. If the range in some direction
493 * contains more than one integer value, we perform basis reduction based
494 * on the value of ctx->opt->gbr
495 * - ISL_GBR_NEVER: never perform basis reduction
496 * - ISL_GBR_ONCE: only perform basis reduction the first
497 * time such a range is encountered
498 * - ISL_GBR_ALWAYS: always perform basis reduction when
499 * such a range is encountered
501 * When ctx->opt->gbr is set to ISL_GBR_ALWAYS, then we allow the basis
502 * reduction computation to return early. That is, as soon as it
503 * finds a reasonable first direction.
505 struct isl_vec
*isl_tab_sample(struct isl_tab
*tab
)
510 struct isl_vec
*sample
;
513 enum isl_lp_result res
;
517 struct isl_tab_undo
**snap
;
522 return isl_vec_alloc(tab
->mat
->ctx
, 0);
525 tab
->basis
= initial_basis(tab
);
528 isl_assert(tab
->mat
->ctx
, tab
->basis
->n_row
== tab
->n_var
+ 1,
530 isl_assert(tab
->mat
->ctx
, tab
->basis
->n_col
== tab
->n_var
+ 1,
537 if (tab
->n_unbounded
== tab
->n_var
) {
538 sample
= isl_tab_get_sample_value(tab
);
539 sample
= isl_mat_vec_product(isl_mat_copy(tab
->basis
), sample
);
540 sample
= isl_vec_ceil(sample
);
541 sample
= isl_mat_vec_inverse_product(isl_mat_copy(tab
->basis
),
546 if (isl_tab_extend_cons(tab
, dim
+ 1) < 0)
549 min
= isl_vec_alloc(ctx
, dim
);
550 max
= isl_vec_alloc(ctx
, dim
);
551 snap
= isl_alloc_array(ctx
, struct isl_tab_undo
*, dim
);
553 if (!min
|| !max
|| !snap
)
564 res
= compute_min(ctx
, tab
, min
, level
);
565 if (res
== isl_lp_error
)
567 if (res
!= isl_lp_ok
)
568 isl_die(ctx
, isl_error_internal
,
569 "expecting bounded rational solution",
571 if (isl_tab_sample_is_integer(tab
))
573 res
= compute_max(ctx
, tab
, max
, level
);
574 if (res
== isl_lp_error
)
576 if (res
!= isl_lp_ok
)
577 isl_die(ctx
, isl_error_internal
,
578 "expecting bounded rational solution",
580 if (isl_tab_sample_is_integer(tab
))
582 choice
= isl_int_lt(min
->el
[level
], max
->el
[level
]);
585 g
= greedy_search(ctx
, tab
, min
, max
, level
);
591 if (!reduced
&& choice
&&
592 ctx
->opt
->gbr
!= ISL_GBR_NEVER
) {
593 unsigned gbr_only_first
;
594 if (ctx
->opt
->gbr
== ISL_GBR_ONCE
)
595 ctx
->opt
->gbr
= ISL_GBR_NEVER
;
597 gbr_only_first
= ctx
->opt
->gbr_only_first
;
598 ctx
->opt
->gbr_only_first
=
599 ctx
->opt
->gbr
== ISL_GBR_ALWAYS
;
600 tab
= isl_tab_compute_reduced_basis(tab
);
601 ctx
->opt
->gbr_only_first
= gbr_only_first
;
602 if (!tab
|| !tab
->basis
)
608 snap
[level
] = isl_tab_snap(tab
);
610 isl_int_add_ui(min
->el
[level
], min
->el
[level
], 1);
612 if (isl_int_gt(min
->el
[level
], max
->el
[level
])) {
616 if (isl_tab_rollback(tab
, snap
[level
]) < 0)
620 isl_int_neg(tab
->basis
->row
[1 + level
][0], min
->el
[level
]);
621 if (isl_tab_add_valid_eq(tab
, tab
->basis
->row
[1 + level
]) < 0)
623 isl_int_set_si(tab
->basis
->row
[1 + level
][0], 0);
624 if (level
+ tab
->n_unbounded
< dim
- 1) {
633 sample
= isl_tab_get_sample_value(tab
);
636 if (tab
->n_unbounded
&& !isl_int_is_one(sample
->el
[0])) {
637 sample
= isl_mat_vec_product(isl_mat_copy(tab
->basis
),
639 sample
= isl_vec_ceil(sample
);
640 sample
= isl_mat_vec_inverse_product(
641 isl_mat_copy(tab
->basis
), sample
);
644 sample
= isl_vec_alloc(ctx
, 0);
659 static struct isl_vec
*sample_bounded(struct isl_basic_set
*bset
);
661 /* Compute a sample point of the given basic set, based on the given,
662 * non-trivial factorization.
664 static __isl_give isl_vec
*factored_sample(__isl_take isl_basic_set
*bset
,
665 __isl_take isl_factorizer
*f
)
668 isl_vec
*sample
= NULL
;
673 ctx
= isl_basic_set_get_ctx(bset
);
677 nparam
= isl_basic_set_dim(bset
, isl_dim_param
);
678 nvar
= isl_basic_set_dim(bset
, isl_dim_set
);
680 sample
= isl_vec_alloc(ctx
, 1 + isl_basic_set_total_dim(bset
));
683 isl_int_set_si(sample
->el
[0], 1);
685 bset
= isl_morph_basic_set(isl_morph_copy(f
->morph
), bset
);
687 for (i
= 0, n
= 0; i
< f
->n_group
; ++i
) {
688 isl_basic_set
*bset_i
;
691 bset_i
= isl_basic_set_copy(bset
);
692 bset_i
= isl_basic_set_drop_constraints_involving(bset_i
,
693 nparam
+ n
+ f
->len
[i
], nvar
- n
- f
->len
[i
]);
694 bset_i
= isl_basic_set_drop_constraints_involving(bset_i
,
696 bset_i
= isl_basic_set_drop(bset_i
, isl_dim_set
,
697 n
+ f
->len
[i
], nvar
- n
- f
->len
[i
]);
698 bset_i
= isl_basic_set_drop(bset_i
, isl_dim_set
, 0, n
);
700 sample_i
= sample_bounded(bset_i
);
703 if (sample_i
->size
== 0) {
704 isl_basic_set_free(bset
);
705 isl_factorizer_free(f
);
706 isl_vec_free(sample
);
709 isl_seq_cpy(sample
->el
+ 1 + nparam
+ n
,
710 sample_i
->el
+ 1, f
->len
[i
]);
711 isl_vec_free(sample_i
);
716 f
->morph
= isl_morph_inverse(f
->morph
);
717 sample
= isl_morph_vec(isl_morph_copy(f
->morph
), sample
);
719 isl_basic_set_free(bset
);
720 isl_factorizer_free(f
);
723 isl_basic_set_free(bset
);
724 isl_factorizer_free(f
);
725 isl_vec_free(sample
);
729 /* Given a basic set that is known to be bounded, find and return
730 * an integer point in the basic set, if there is any.
732 * After handling some trivial cases, we construct a tableau
733 * and then use isl_tab_sample to find a sample, passing it
734 * the identity matrix as initial basis.
736 static struct isl_vec
*sample_bounded(struct isl_basic_set
*bset
)
740 struct isl_vec
*sample
;
741 struct isl_tab
*tab
= NULL
;
747 if (isl_basic_set_plain_is_empty(bset
))
748 return empty_sample(bset
);
750 dim
= isl_basic_set_total_dim(bset
);
752 return zero_sample(bset
);
754 return interval_sample(bset
);
756 return sample_eq(bset
, sample_bounded
);
758 f
= isl_basic_set_factorizer(bset
);
762 return factored_sample(bset
, f
);
763 isl_factorizer_free(f
);
767 tab
= isl_tab_from_basic_set(bset
, 1);
768 if (tab
&& tab
->empty
) {
770 ISL_F_SET(bset
, ISL_BASIC_SET_EMPTY
);
771 sample
= isl_vec_alloc(bset
->ctx
, 0);
772 isl_basic_set_free(bset
);
776 if (!ISL_F_ISSET(bset
, ISL_BASIC_SET_NO_IMPLICIT
))
777 if (isl_tab_detect_implicit_equalities(tab
) < 0)
780 sample
= isl_tab_sample(tab
);
784 if (sample
->size
> 0) {
785 isl_vec_free(bset
->sample
);
786 bset
->sample
= isl_vec_copy(sample
);
789 isl_basic_set_free(bset
);
793 isl_basic_set_free(bset
);
798 /* Given a basic set "bset" and a value "sample" for the first coordinates
799 * of bset, plug in these values and drop the corresponding coordinates.
801 * We do this by computing the preimage of the transformation
807 * where [1 s] is the sample value and I is the identity matrix of the
808 * appropriate dimension.
810 static struct isl_basic_set
*plug_in(struct isl_basic_set
*bset
,
811 struct isl_vec
*sample
)
817 if (!bset
|| !sample
)
820 total
= isl_basic_set_total_dim(bset
);
821 T
= isl_mat_alloc(bset
->ctx
, 1 + total
, 1 + total
- (sample
->size
- 1));
825 for (i
= 0; i
< sample
->size
; ++i
) {
826 isl_int_set(T
->row
[i
][0], sample
->el
[i
]);
827 isl_seq_clr(T
->row
[i
] + 1, T
->n_col
- 1);
829 for (i
= 0; i
< T
->n_col
- 1; ++i
) {
830 isl_seq_clr(T
->row
[sample
->size
+ i
], T
->n_col
);
831 isl_int_set_si(T
->row
[sample
->size
+ i
][1 + i
], 1);
833 isl_vec_free(sample
);
835 bset
= isl_basic_set_preimage(bset
, T
);
838 isl_basic_set_free(bset
);
839 isl_vec_free(sample
);
843 /* Given a basic set "bset", return any (possibly non-integer) point
846 static struct isl_vec
*rational_sample(struct isl_basic_set
*bset
)
849 struct isl_vec
*sample
;
854 tab
= isl_tab_from_basic_set(bset
, 0);
855 sample
= isl_tab_get_sample_value(tab
);
858 isl_basic_set_free(bset
);
863 /* Given a linear cone "cone" and a rational point "vec",
864 * construct a polyhedron with shifted copies of the constraints in "cone",
865 * i.e., a polyhedron with "cone" as its recession cone, such that each
866 * point x in this polyhedron is such that the unit box positioned at x
867 * lies entirely inside the affine cone 'vec + cone'.
868 * Any rational point in this polyhedron may therefore be rounded up
869 * to yield an integer point that lies inside said affine cone.
871 * Denote the constraints of cone by "<a_i, x> >= 0" and the rational
872 * point "vec" by v/d.
873 * Let b_i = <a_i, v>. Then the affine cone 'vec + cone' is given
874 * by <a_i, x> - b/d >= 0.
875 * The polyhedron <a_i, x> - ceil{b/d} >= 0 is a subset of this affine cone.
876 * We prefer this polyhedron over the actual affine cone because it doesn't
877 * require a scaling of the constraints.
878 * If each of the vertices of the unit cube positioned at x lies inside
879 * this polyhedron, then the whole unit cube at x lies inside the affine cone.
880 * We therefore impose that x' = x + \sum e_i, for any selection of unit
881 * vectors lies inside the polyhedron, i.e.,
883 * <a_i, x'> - ceil{b/d} = <a_i, x> + sum a_i - ceil{b/d} >= 0
885 * The most stringent of these constraints is the one that selects
886 * all negative a_i, so the polyhedron we are looking for has constraints
888 * <a_i, x> + sum_{a_i < 0} a_i - ceil{b/d} >= 0
890 * Note that if cone were known to have only non-negative rays
891 * (which can be accomplished by a unimodular transformation),
892 * then we would only have to check the points x' = x + e_i
893 * and we only have to add the smallest negative a_i (if any)
894 * instead of the sum of all negative a_i.
896 static struct isl_basic_set
*shift_cone(struct isl_basic_set
*cone
,
902 struct isl_basic_set
*shift
= NULL
;
907 isl_assert(cone
->ctx
, cone
->n_eq
== 0, goto error
);
909 total
= isl_basic_set_total_dim(cone
);
911 shift
= isl_basic_set_alloc_space(isl_basic_set_get_space(cone
),
914 for (i
= 0; i
< cone
->n_ineq
; ++i
) {
915 k
= isl_basic_set_alloc_inequality(shift
);
918 isl_seq_cpy(shift
->ineq
[k
] + 1, cone
->ineq
[i
] + 1, total
);
919 isl_seq_inner_product(shift
->ineq
[k
] + 1, vec
->el
+ 1, total
,
921 isl_int_cdiv_q(shift
->ineq
[k
][0],
922 shift
->ineq
[k
][0], vec
->el
[0]);
923 isl_int_neg(shift
->ineq
[k
][0], shift
->ineq
[k
][0]);
924 for (j
= 0; j
< total
; ++j
) {
925 if (isl_int_is_nonneg(shift
->ineq
[k
][1 + j
]))
927 isl_int_add(shift
->ineq
[k
][0],
928 shift
->ineq
[k
][0], shift
->ineq
[k
][1 + j
]);
932 isl_basic_set_free(cone
);
935 return isl_basic_set_finalize(shift
);
937 isl_basic_set_free(shift
);
938 isl_basic_set_free(cone
);
943 /* Given a rational point vec in a (transformed) basic set,
944 * such that cone is the recession cone of the original basic set,
945 * "round up" the rational point to an integer point.
947 * We first check if the rational point just happens to be integer.
948 * If not, we transform the cone in the same way as the basic set,
949 * pick a point x in this cone shifted to the rational point such that
950 * the whole unit cube at x is also inside this affine cone.
951 * Then we simply round up the coordinates of x and return the
952 * resulting integer point.
954 static struct isl_vec
*round_up_in_cone(struct isl_vec
*vec
,
955 struct isl_basic_set
*cone
, struct isl_mat
*U
)
959 if (!vec
|| !cone
|| !U
)
962 isl_assert(vec
->ctx
, vec
->size
!= 0, goto error
);
963 if (isl_int_is_one(vec
->el
[0])) {
965 isl_basic_set_free(cone
);
969 total
= isl_basic_set_total_dim(cone
);
970 cone
= isl_basic_set_preimage(cone
, U
);
971 cone
= isl_basic_set_remove_dims(cone
, isl_dim_set
,
972 0, total
- (vec
->size
- 1));
974 cone
= shift_cone(cone
, vec
);
976 vec
= rational_sample(cone
);
977 vec
= isl_vec_ceil(vec
);
982 isl_basic_set_free(cone
);
986 /* Concatenate two integer vectors, i.e., two vectors with denominator
987 * (stored in element 0) equal to 1.
989 static struct isl_vec
*vec_concat(struct isl_vec
*vec1
, struct isl_vec
*vec2
)
995 isl_assert(vec1
->ctx
, vec1
->size
> 0, goto error
);
996 isl_assert(vec2
->ctx
, vec2
->size
> 0, goto error
);
997 isl_assert(vec1
->ctx
, isl_int_is_one(vec1
->el
[0]), goto error
);
998 isl_assert(vec2
->ctx
, isl_int_is_one(vec2
->el
[0]), goto error
);
1000 vec
= isl_vec_alloc(vec1
->ctx
, vec1
->size
+ vec2
->size
- 1);
1004 isl_seq_cpy(vec
->el
, vec1
->el
, vec1
->size
);
1005 isl_seq_cpy(vec
->el
+ vec1
->size
, vec2
->el
+ 1, vec2
->size
- 1);
1017 /* Give a basic set "bset" with recession cone "cone", compute and
1018 * return an integer point in bset, if any.
1020 * If the recession cone is full-dimensional, then we know that
1021 * bset contains an infinite number of integer points and it is
1022 * fairly easy to pick one of them.
1023 * If the recession cone is not full-dimensional, then we first
1024 * transform bset such that the bounded directions appear as
1025 * the first dimensions of the transformed basic set.
1026 * We do this by using a unimodular transformation that transforms
1027 * the equalities in the recession cone to equalities on the first
1030 * The transformed set is then projected onto its bounded dimensions.
1031 * Note that to compute this projection, we can simply drop all constraints
1032 * involving any of the unbounded dimensions since these constraints
1033 * cannot be combined to produce a constraint on the bounded dimensions.
1034 * To see this, assume that there is such a combination of constraints
1035 * that produces a constraint on the bounded dimensions. This means
1036 * that some combination of the unbounded dimensions has both an upper
1037 * bound and a lower bound in terms of the bounded dimensions, but then
1038 * this combination would be a bounded direction too and would have been
1039 * transformed into a bounded dimensions.
1041 * We then compute a sample value in the bounded dimensions.
1042 * If no such value can be found, then the original set did not contain
1043 * any integer points and we are done.
1044 * Otherwise, we plug in the value we found in the bounded dimensions,
1045 * project out these bounded dimensions and end up with a set with
1046 * a full-dimensional recession cone.
1047 * A sample point in this set is computed by "rounding up" any
1048 * rational point in the set.
1050 * The sample points in the bounded and unbounded dimensions are
1051 * then combined into a single sample point and transformed back
1052 * to the original space.
1054 __isl_give isl_vec
*isl_basic_set_sample_with_cone(
1055 __isl_take isl_basic_set
*bset
, __isl_take isl_basic_set
*cone
)
1059 struct isl_mat
*M
, *U
;
1060 struct isl_vec
*sample
;
1061 struct isl_vec
*cone_sample
;
1062 struct isl_ctx
*ctx
;
1063 struct isl_basic_set
*bounded
;
1069 total
= isl_basic_set_total_dim(cone
);
1070 cone_dim
= total
- cone
->n_eq
;
1072 M
= isl_mat_sub_alloc6(bset
->ctx
, cone
->eq
, 0, cone
->n_eq
, 1, total
);
1073 M
= isl_mat_left_hermite(M
, 0, &U
, NULL
);
1078 U
= isl_mat_lin_to_aff(U
);
1079 bset
= isl_basic_set_preimage(bset
, isl_mat_copy(U
));
1081 bounded
= isl_basic_set_copy(bset
);
1082 bounded
= isl_basic_set_drop_constraints_involving(bounded
,
1083 total
- cone_dim
, cone_dim
);
1084 bounded
= isl_basic_set_drop_dims(bounded
, total
- cone_dim
, cone_dim
);
1085 sample
= sample_bounded(bounded
);
1086 if (!sample
|| sample
->size
== 0) {
1087 isl_basic_set_free(bset
);
1088 isl_basic_set_free(cone
);
1092 bset
= plug_in(bset
, isl_vec_copy(sample
));
1093 cone_sample
= rational_sample(bset
);
1094 cone_sample
= round_up_in_cone(cone_sample
, cone
, isl_mat_copy(U
));
1095 sample
= vec_concat(sample
, cone_sample
);
1096 sample
= isl_mat_vec_product(U
, sample
);
1099 isl_basic_set_free(cone
);
1100 isl_basic_set_free(bset
);
1104 static void vec_sum_of_neg(struct isl_vec
*v
, isl_int
*s
)
1108 isl_int_set_si(*s
, 0);
1110 for (i
= 0; i
< v
->size
; ++i
)
1111 if (isl_int_is_neg(v
->el
[i
]))
1112 isl_int_add(*s
, *s
, v
->el
[i
]);
1115 /* Given a tableau "tab", a tableau "tab_cone" that corresponds
1116 * to the recession cone and the inverse of a new basis U = inv(B),
1117 * with the unbounded directions in B last,
1118 * add constraints to "tab" that ensure any rational value
1119 * in the unbounded directions can be rounded up to an integer value.
1121 * The new basis is given by x' = B x, i.e., x = U x'.
1122 * For any rational value of the last tab->n_unbounded coordinates
1123 * in the update tableau, the value that is obtained by rounding
1124 * up this value should be contained in the original tableau.
1125 * For any constraint "a x + c >= 0", we therefore need to add
1126 * a constraint "a x + c + s >= 0", with s the sum of all negative
1127 * entries in the last elements of "a U".
1129 * Since we are not interested in the first entries of any of the "a U",
1130 * we first drop the columns of U that correpond to bounded directions.
1132 static int tab_shift_cone(struct isl_tab
*tab
,
1133 struct isl_tab
*tab_cone
, struct isl_mat
*U
)
1137 struct isl_basic_set
*bset
= NULL
;
1139 if (tab
&& tab
->n_unbounded
== 0) {
1144 if (!tab
|| !tab_cone
|| !U
)
1146 bset
= isl_tab_peek_bset(tab_cone
);
1147 U
= isl_mat_drop_cols(U
, 0, tab
->n_var
- tab
->n_unbounded
);
1148 for (i
= 0; i
< bset
->n_ineq
; ++i
) {
1150 struct isl_vec
*row
= NULL
;
1151 if (isl_tab_is_equality(tab_cone
, tab_cone
->n_eq
+ i
))
1153 row
= isl_vec_alloc(bset
->ctx
, tab_cone
->n_var
);
1156 isl_seq_cpy(row
->el
, bset
->ineq
[i
] + 1, tab_cone
->n_var
);
1157 row
= isl_vec_mat_product(row
, isl_mat_copy(U
));
1160 vec_sum_of_neg(row
, &v
);
1162 if (isl_int_is_zero(v
))
1164 tab
= isl_tab_extend(tab
, 1);
1165 isl_int_add(bset
->ineq
[i
][0], bset
->ineq
[i
][0], v
);
1166 ok
= isl_tab_add_ineq(tab
, bset
->ineq
[i
]) >= 0;
1167 isl_int_sub(bset
->ineq
[i
][0], bset
->ineq
[i
][0], v
);
1181 /* Compute and return an initial basis for the possibly
1182 * unbounded tableau "tab". "tab_cone" is a tableau
1183 * for the corresponding recession cone.
1184 * Additionally, add constraints to "tab" that ensure
1185 * that any rational value for the unbounded directions
1186 * can be rounded up to an integer value.
1188 * If the tableau is bounded, i.e., if the recession cone
1189 * is zero-dimensional, then we just use inital_basis.
1190 * Otherwise, we construct a basis whose first directions
1191 * correspond to equalities, followed by bounded directions,
1192 * i.e., equalities in the recession cone.
1193 * The remaining directions are then unbounded.
1195 int isl_tab_set_initial_basis_with_cone(struct isl_tab
*tab
,
1196 struct isl_tab
*tab_cone
)
1199 struct isl_mat
*cone_eq
;
1200 struct isl_mat
*U
, *Q
;
1202 if (!tab
|| !tab_cone
)
1205 if (tab_cone
->n_col
== tab_cone
->n_dead
) {
1206 tab
->basis
= initial_basis(tab
);
1207 return tab
->basis
? 0 : -1;
1210 eq
= tab_equalities(tab
);
1213 tab
->n_zero
= eq
->n_row
;
1214 cone_eq
= tab_equalities(tab_cone
);
1215 eq
= isl_mat_concat(eq
, cone_eq
);
1218 tab
->n_unbounded
= tab
->n_var
- (eq
->n_row
- tab
->n_zero
);
1219 eq
= isl_mat_left_hermite(eq
, 0, &U
, &Q
);
1223 tab
->basis
= isl_mat_lin_to_aff(Q
);
1224 if (tab_shift_cone(tab
, tab_cone
, U
) < 0)
1231 /* Compute and return a sample point in bset using generalized basis
1232 * reduction. We first check if the input set has a non-trivial
1233 * recession cone. If so, we perform some extra preprocessing in
1234 * sample_with_cone. Otherwise, we directly perform generalized basis
1237 static struct isl_vec
*gbr_sample(struct isl_basic_set
*bset
)
1240 struct isl_basic_set
*cone
;
1242 dim
= isl_basic_set_total_dim(bset
);
1244 cone
= isl_basic_set_recession_cone(isl_basic_set_copy(bset
));
1248 if (cone
->n_eq
< dim
)
1249 return isl_basic_set_sample_with_cone(bset
, cone
);
1251 isl_basic_set_free(cone
);
1252 return sample_bounded(bset
);
1254 isl_basic_set_free(bset
);
1258 static struct isl_vec
*pip_sample(struct isl_basic_set
*bset
)
1261 struct isl_ctx
*ctx
;
1262 struct isl_vec
*sample
;
1264 bset
= isl_basic_set_skew_to_positive_orthant(bset
, &T
);
1269 sample
= isl_pip_basic_set_sample(bset
);
1271 if (sample
&& sample
->size
!= 0)
1272 sample
= isl_mat_vec_product(T
, sample
);
1279 static struct isl_vec
*basic_set_sample(struct isl_basic_set
*bset
, int bounded
)
1281 struct isl_ctx
*ctx
;
1287 if (isl_basic_set_plain_is_empty(bset
))
1288 return empty_sample(bset
);
1290 dim
= isl_basic_set_n_dim(bset
);
1291 isl_assert(ctx
, isl_basic_set_n_param(bset
) == 0, goto error
);
1292 isl_assert(ctx
, bset
->n_div
== 0, goto error
);
1294 if (bset
->sample
&& bset
->sample
->size
== 1 + dim
) {
1295 int contains
= isl_basic_set_contains(bset
, bset
->sample
);
1299 struct isl_vec
*sample
= isl_vec_copy(bset
->sample
);
1300 isl_basic_set_free(bset
);
1304 isl_vec_free(bset
->sample
);
1305 bset
->sample
= NULL
;
1308 return sample_eq(bset
, bounded
? isl_basic_set_sample_bounded
1309 : isl_basic_set_sample_vec
);
1311 return zero_sample(bset
);
1313 return interval_sample(bset
);
1315 switch (bset
->ctx
->opt
->ilp_solver
) {
1317 return pip_sample(bset
);
1319 return bounded
? sample_bounded(bset
) : gbr_sample(bset
);
1321 isl_assert(bset
->ctx
, 0, );
1323 isl_basic_set_free(bset
);
1327 __isl_give isl_vec
*isl_basic_set_sample_vec(__isl_take isl_basic_set
*bset
)
1329 return basic_set_sample(bset
, 0);
1332 /* Compute an integer sample in "bset", where the caller guarantees
1333 * that "bset" is bounded.
1335 struct isl_vec
*isl_basic_set_sample_bounded(struct isl_basic_set
*bset
)
1337 return basic_set_sample(bset
, 1);
1340 __isl_give isl_basic_set
*isl_basic_set_from_vec(__isl_take isl_vec
*vec
)
1344 struct isl_basic_set
*bset
= NULL
;
1345 struct isl_ctx
*ctx
;
1351 isl_assert(ctx
, vec
->size
!= 0, goto error
);
1353 bset
= isl_basic_set_alloc(ctx
, 0, vec
->size
- 1, 0, vec
->size
- 1, 0);
1356 dim
= isl_basic_set_n_dim(bset
);
1357 for (i
= dim
- 1; i
>= 0; --i
) {
1358 k
= isl_basic_set_alloc_equality(bset
);
1361 isl_seq_clr(bset
->eq
[k
], 1 + dim
);
1362 isl_int_neg(bset
->eq
[k
][0], vec
->el
[1 + i
]);
1363 isl_int_set(bset
->eq
[k
][1 + i
], vec
->el
[0]);
1369 isl_basic_set_free(bset
);
1374 __isl_give isl_basic_map
*isl_basic_map_sample(__isl_take isl_basic_map
*bmap
)
1376 struct isl_basic_set
*bset
;
1377 struct isl_vec
*sample_vec
;
1379 bset
= isl_basic_map_underlying_set(isl_basic_map_copy(bmap
));
1380 sample_vec
= isl_basic_set_sample_vec(bset
);
1383 if (sample_vec
->size
== 0) {
1384 struct isl_basic_map
*sample
;
1385 sample
= isl_basic_map_empty_like(bmap
);
1386 isl_vec_free(sample_vec
);
1387 isl_basic_map_free(bmap
);
1390 bset
= isl_basic_set_from_vec(sample_vec
);
1391 return isl_basic_map_overlying_set(bset
, bmap
);
1393 isl_basic_map_free(bmap
);
1397 __isl_give isl_basic_set
*isl_basic_set_sample(__isl_take isl_basic_set
*bset
)
1399 return isl_basic_map_sample(bset
);
1402 __isl_give isl_basic_map
*isl_map_sample(__isl_take isl_map
*map
)
1405 isl_basic_map
*sample
= NULL
;
1410 for (i
= 0; i
< map
->n
; ++i
) {
1411 sample
= isl_basic_map_sample(isl_basic_map_copy(map
->p
[i
]));
1414 if (!ISL_F_ISSET(sample
, ISL_BASIC_MAP_EMPTY
))
1416 isl_basic_map_free(sample
);
1419 sample
= isl_basic_map_empty_like_map(map
);
1427 __isl_give isl_basic_set
*isl_set_sample(__isl_take isl_set
*set
)
1429 return (isl_basic_set
*) isl_map_sample((isl_map
*)set
);
1432 __isl_give isl_point
*isl_basic_set_sample_point(__isl_take isl_basic_set
*bset
)
1437 dim
= isl_basic_set_get_space(bset
);
1438 bset
= isl_basic_set_underlying_set(bset
);
1439 vec
= isl_basic_set_sample_vec(bset
);
1441 return isl_point_alloc(dim
, vec
);
1444 __isl_give isl_point
*isl_set_sample_point(__isl_take isl_set
*set
)
1452 for (i
= 0; i
< set
->n
; ++i
) {
1453 pnt
= isl_basic_set_sample_point(isl_basic_set_copy(set
->p
[i
]));
1456 if (!isl_point_is_void(pnt
))
1458 isl_point_free(pnt
);
1461 pnt
= isl_point_void(isl_set_get_space(set
));