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"
16 #include "isl_equalities.h"
18 #include "isl_basis_reduction.h"
19 #include <isl_factorization.h>
20 #include <isl_point_private.h>
21 #include <isl_options_private.h>
22 #include <isl_vec_private.h>
24 #include <bset_from_bmap.c>
25 #include <set_to_map.c>
27 static __isl_give isl_vec
*empty_sample(__isl_take isl_basic_set
*bset
)
31 vec
= isl_vec_alloc(bset
->ctx
, 0);
32 isl_basic_set_free(bset
);
36 /* Construct a zero sample of the same dimension as bset.
37 * As a special case, if bset is zero-dimensional, this
38 * function creates a zero-dimensional sample point.
40 static __isl_give isl_vec
*zero_sample(__isl_take isl_basic_set
*bset
)
43 struct isl_vec
*sample
;
45 dim
= isl_basic_set_total_dim(bset
);
46 sample
= isl_vec_alloc(bset
->ctx
, 1 + dim
);
48 isl_int_set_si(sample
->el
[0], 1);
49 isl_seq_clr(sample
->el
+ 1, dim
);
51 isl_basic_set_free(bset
);
55 static struct isl_vec
*interval_sample(struct isl_basic_set
*bset
)
59 struct isl_vec
*sample
;
61 bset
= isl_basic_set_simplify(bset
);
64 if (isl_basic_set_plain_is_empty(bset
))
65 return empty_sample(bset
);
66 if (bset
->n_eq
== 0 && bset
->n_ineq
== 0)
67 return zero_sample(bset
);
69 sample
= isl_vec_alloc(bset
->ctx
, 2);
74 isl_int_set_si(sample
->block
.data
[0], 1);
77 isl_assert(bset
->ctx
, bset
->n_eq
== 1, goto error
);
78 isl_assert(bset
->ctx
, bset
->n_ineq
== 0, goto error
);
79 if (isl_int_is_one(bset
->eq
[0][1]))
80 isl_int_neg(sample
->el
[1], bset
->eq
[0][0]);
82 isl_assert(bset
->ctx
, isl_int_is_negone(bset
->eq
[0][1]),
84 isl_int_set(sample
->el
[1], bset
->eq
[0][0]);
86 isl_basic_set_free(bset
);
91 if (isl_int_is_one(bset
->ineq
[0][1]))
92 isl_int_neg(sample
->block
.data
[1], bset
->ineq
[0][0]);
94 isl_int_set(sample
->block
.data
[1], bset
->ineq
[0][0]);
95 for (i
= 1; i
< bset
->n_ineq
; ++i
) {
96 isl_seq_inner_product(sample
->block
.data
,
97 bset
->ineq
[i
], 2, &t
);
98 if (isl_int_is_neg(t
))
102 if (i
< bset
->n_ineq
) {
103 isl_vec_free(sample
);
104 return empty_sample(bset
);
107 isl_basic_set_free(bset
);
110 isl_basic_set_free(bset
);
111 isl_vec_free(sample
);
115 /* Find a sample integer point, if any, in bset, which is known
116 * to have equalities. If bset contains no integer points, then
117 * return a zero-length vector.
118 * We simply remove the known equalities, compute a sample
119 * in the resulting bset, using the specified recurse function,
120 * and then transform the sample back to the original space.
122 static __isl_give isl_vec
*sample_eq(__isl_take isl_basic_set
*bset
,
123 __isl_give isl_vec
*(*recurse
)(__isl_take isl_basic_set
*))
126 struct isl_vec
*sample
;
131 bset
= isl_basic_set_remove_equalities(bset
, &T
, NULL
);
132 sample
= recurse(bset
);
133 if (!sample
|| sample
->size
== 0)
136 sample
= isl_mat_vec_product(T
, sample
);
140 /* Return a matrix containing the equalities of the tableau
141 * in constraint form. The tableau is assumed to have
142 * an associated bset that has been kept up-to-date.
144 static struct isl_mat
*tab_equalities(struct isl_tab
*tab
)
149 struct isl_basic_set
*bset
;
154 bset
= isl_tab_peek_bset(tab
);
155 isl_assert(tab
->mat
->ctx
, bset
, return NULL
);
157 n_eq
= tab
->n_var
- tab
->n_col
+ tab
->n_dead
;
158 if (tab
->empty
|| n_eq
== 0)
159 return isl_mat_alloc(tab
->mat
->ctx
, 0, tab
->n_var
);
160 if (n_eq
== tab
->n_var
)
161 return isl_mat_identity(tab
->mat
->ctx
, tab
->n_var
);
163 eq
= isl_mat_alloc(tab
->mat
->ctx
, n_eq
, tab
->n_var
);
166 for (i
= 0, j
= 0; i
< tab
->n_con
; ++i
) {
167 if (tab
->con
[i
].is_row
)
169 if (tab
->con
[i
].index
>= 0 && tab
->con
[i
].index
>= tab
->n_dead
)
172 isl_seq_cpy(eq
->row
[j
], bset
->eq
[i
] + 1, tab
->n_var
);
174 isl_seq_cpy(eq
->row
[j
],
175 bset
->ineq
[i
- bset
->n_eq
] + 1, tab
->n_var
);
178 isl_assert(bset
->ctx
, j
== n_eq
, goto error
);
185 /* Compute and return an initial basis for the bounded tableau "tab".
187 * If the tableau is either full-dimensional or zero-dimensional,
188 * the we simply return an identity matrix.
189 * Otherwise, we construct a basis whose first directions correspond
192 static struct isl_mat
*initial_basis(struct isl_tab
*tab
)
198 tab
->n_unbounded
= 0;
199 tab
->n_zero
= n_eq
= tab
->n_var
- tab
->n_col
+ tab
->n_dead
;
200 if (tab
->empty
|| n_eq
== 0 || n_eq
== tab
->n_var
)
201 return isl_mat_identity(tab
->mat
->ctx
, 1 + tab
->n_var
);
203 eq
= tab_equalities(tab
);
204 eq
= isl_mat_left_hermite(eq
, 0, NULL
, &Q
);
209 Q
= isl_mat_lin_to_aff(Q
);
213 /* Compute the minimum of the current ("level") basis row over "tab"
214 * and store the result in position "level" of "min".
216 * This function assumes that at least one more row and at least
217 * one more element in the constraint array are available in the tableau.
219 static enum isl_lp_result
compute_min(isl_ctx
*ctx
, struct isl_tab
*tab
,
220 __isl_keep isl_vec
*min
, int level
)
222 return isl_tab_min(tab
, tab
->basis
->row
[1 + level
],
223 ctx
->one
, &min
->el
[level
], NULL
, 0);
226 /* Compute the maximum of the current ("level") basis row over "tab"
227 * and store the result in position "level" of "max".
229 * This function assumes that at least one more row and at least
230 * one more element in the constraint array are available in the tableau.
232 static enum isl_lp_result
compute_max(isl_ctx
*ctx
, struct isl_tab
*tab
,
233 __isl_keep isl_vec
*max
, int level
)
235 enum isl_lp_result res
;
236 unsigned dim
= tab
->n_var
;
238 isl_seq_neg(tab
->basis
->row
[1 + level
] + 1,
239 tab
->basis
->row
[1 + level
] + 1, dim
);
240 res
= isl_tab_min(tab
, tab
->basis
->row
[1 + level
],
241 ctx
->one
, &max
->el
[level
], NULL
, 0);
242 isl_seq_neg(tab
->basis
->row
[1 + level
] + 1,
243 tab
->basis
->row
[1 + level
] + 1, dim
);
244 isl_int_neg(max
->el
[level
], max
->el
[level
]);
249 /* Perform a greedy search for an integer point in the set represented
250 * by "tab", given that the minimal rational value (rounded up to the
251 * nearest integer) at "level" is smaller than the maximal rational
252 * value (rounded down to the nearest integer).
254 * Return 1 if we have found an integer point (if tab->n_unbounded > 0
255 * then we may have only found integer values for the bounded dimensions
256 * and it is the responsibility of the caller to extend this solution
257 * to the unbounded dimensions).
258 * Return 0 if greedy search did not result in a solution.
259 * Return -1 if some error occurred.
261 * We assign a value half-way between the minimum and the maximum
262 * to the current dimension and check if the minimal value of the
263 * next dimension is still smaller than (or equal) to the maximal value.
264 * We continue this process until either
265 * - the minimal value (rounded up) is greater than the maximal value
266 * (rounded down). In this case, greedy search has failed.
267 * - we have exhausted all bounded dimensions, meaning that we have
269 * - the sample value of the tableau is integral.
270 * - some error has occurred.
272 static int greedy_search(isl_ctx
*ctx
, struct isl_tab
*tab
,
273 __isl_keep isl_vec
*min
, __isl_keep isl_vec
*max
, int level
)
275 struct isl_tab_undo
*snap
;
276 enum isl_lp_result res
;
278 snap
= isl_tab_snap(tab
);
281 isl_int_add(tab
->basis
->row
[1 + level
][0],
282 min
->el
[level
], max
->el
[level
]);
283 isl_int_fdiv_q_ui(tab
->basis
->row
[1 + level
][0],
284 tab
->basis
->row
[1 + level
][0], 2);
285 isl_int_neg(tab
->basis
->row
[1 + level
][0],
286 tab
->basis
->row
[1 + level
][0]);
287 if (isl_tab_add_valid_eq(tab
, tab
->basis
->row
[1 + level
]) < 0)
289 isl_int_set_si(tab
->basis
->row
[1 + level
][0], 0);
291 if (++level
>= tab
->n_var
- tab
->n_unbounded
)
293 if (isl_tab_sample_is_integer(tab
))
296 res
= compute_min(ctx
, tab
, min
, level
);
297 if (res
== isl_lp_error
)
299 if (res
!= isl_lp_ok
)
300 isl_die(ctx
, isl_error_internal
,
301 "expecting bounded rational solution",
303 res
= compute_max(ctx
, tab
, max
, level
);
304 if (res
== isl_lp_error
)
306 if (res
!= isl_lp_ok
)
307 isl_die(ctx
, isl_error_internal
,
308 "expecting bounded rational solution",
310 } while (isl_int_le(min
->el
[level
], max
->el
[level
]));
312 if (isl_tab_rollback(tab
, snap
) < 0)
318 /* Given a tableau representing a set, find and return
319 * an integer point in the set, if there is any.
321 * We perform a depth first search
322 * for an integer point, by scanning all possible values in the range
323 * attained by a basis vector, where an initial basis may have been set
324 * by the calling function. Otherwise an initial basis that exploits
325 * the equalities in the tableau is created.
326 * tab->n_zero is currently ignored and is clobbered by this function.
328 * The tableau is allowed to have unbounded direction, but then
329 * the calling function needs to set an initial basis, with the
330 * unbounded directions last and with tab->n_unbounded set
331 * to the number of unbounded directions.
332 * Furthermore, the calling functions needs to add shifted copies
333 * of all constraints involving unbounded directions to ensure
334 * that any feasible rational value in these directions can be rounded
335 * up to yield a feasible integer value.
336 * In particular, let B define the given basis x' = B x
337 * and let T be the inverse of B, i.e., X = T x'.
338 * Let a x + c >= 0 be a constraint of the set represented by the tableau,
339 * or a T x' + c >= 0 in terms of the given basis. Assume that
340 * the bounded directions have an integer value, then we can safely
341 * round up the values for the unbounded directions if we make sure
342 * that x' not only satisfies the original constraint, but also
343 * the constraint "a T x' + c + s >= 0" with s the sum of all
344 * negative values in the last n_unbounded entries of "a T".
345 * The calling function therefore needs to add the constraint
346 * a x + c + s >= 0. The current function then scans the first
347 * directions for an integer value and once those have been found,
348 * it can compute "T ceil(B x)" to yield an integer point in the set.
349 * Note that during the search, the first rows of B may be changed
350 * by a basis reduction, but the last n_unbounded rows of B remain
351 * unaltered and are also not mixed into the first rows.
353 * The search is implemented iteratively. "level" identifies the current
354 * basis vector. "init" is true if we want the first value at the current
355 * level and false if we want the next value.
357 * At the start of each level, we first check if we can find a solution
358 * using greedy search. If not, we continue with the exhaustive search.
360 * The initial basis is the identity matrix. If the range in some direction
361 * contains more than one integer value, we perform basis reduction based
362 * on the value of ctx->opt->gbr
363 * - ISL_GBR_NEVER: never perform basis reduction
364 * - ISL_GBR_ONCE: only perform basis reduction the first
365 * time such a range is encountered
366 * - ISL_GBR_ALWAYS: always perform basis reduction when
367 * such a range is encountered
369 * When ctx->opt->gbr is set to ISL_GBR_ALWAYS, then we allow the basis
370 * reduction computation to return early. That is, as soon as it
371 * finds a reasonable first direction.
373 struct isl_vec
*isl_tab_sample(struct isl_tab
*tab
)
378 struct isl_vec
*sample
;
381 enum isl_lp_result res
;
385 struct isl_tab_undo
**snap
;
390 return isl_vec_alloc(tab
->mat
->ctx
, 0);
393 tab
->basis
= initial_basis(tab
);
396 isl_assert(tab
->mat
->ctx
, tab
->basis
->n_row
== tab
->n_var
+ 1,
398 isl_assert(tab
->mat
->ctx
, tab
->basis
->n_col
== tab
->n_var
+ 1,
405 if (tab
->n_unbounded
== tab
->n_var
) {
406 sample
= isl_tab_get_sample_value(tab
);
407 sample
= isl_mat_vec_product(isl_mat_copy(tab
->basis
), sample
);
408 sample
= isl_vec_ceil(sample
);
409 sample
= isl_mat_vec_inverse_product(isl_mat_copy(tab
->basis
),
414 if (isl_tab_extend_cons(tab
, dim
+ 1) < 0)
417 min
= isl_vec_alloc(ctx
, dim
);
418 max
= isl_vec_alloc(ctx
, dim
);
419 snap
= isl_alloc_array(ctx
, struct isl_tab_undo
*, dim
);
421 if (!min
|| !max
|| !snap
)
432 res
= compute_min(ctx
, tab
, min
, level
);
433 if (res
== isl_lp_error
)
435 if (res
!= isl_lp_ok
)
436 isl_die(ctx
, isl_error_internal
,
437 "expecting bounded rational solution",
439 if (isl_tab_sample_is_integer(tab
))
441 res
= compute_max(ctx
, tab
, max
, level
);
442 if (res
== isl_lp_error
)
444 if (res
!= isl_lp_ok
)
445 isl_die(ctx
, isl_error_internal
,
446 "expecting bounded rational solution",
448 if (isl_tab_sample_is_integer(tab
))
450 choice
= isl_int_lt(min
->el
[level
], max
->el
[level
]);
453 g
= greedy_search(ctx
, tab
, min
, max
, level
);
459 if (!reduced
&& choice
&&
460 ctx
->opt
->gbr
!= ISL_GBR_NEVER
) {
461 unsigned gbr_only_first
;
462 if (ctx
->opt
->gbr
== ISL_GBR_ONCE
)
463 ctx
->opt
->gbr
= ISL_GBR_NEVER
;
465 gbr_only_first
= ctx
->opt
->gbr_only_first
;
466 ctx
->opt
->gbr_only_first
=
467 ctx
->opt
->gbr
== ISL_GBR_ALWAYS
;
468 tab
= isl_tab_compute_reduced_basis(tab
);
469 ctx
->opt
->gbr_only_first
= gbr_only_first
;
470 if (!tab
|| !tab
->basis
)
476 snap
[level
] = isl_tab_snap(tab
);
478 isl_int_add_ui(min
->el
[level
], min
->el
[level
], 1);
480 if (isl_int_gt(min
->el
[level
], max
->el
[level
])) {
484 if (isl_tab_rollback(tab
, snap
[level
]) < 0)
488 isl_int_neg(tab
->basis
->row
[1 + level
][0], min
->el
[level
]);
489 if (isl_tab_add_valid_eq(tab
, tab
->basis
->row
[1 + level
]) < 0)
491 isl_int_set_si(tab
->basis
->row
[1 + level
][0], 0);
492 if (level
+ tab
->n_unbounded
< dim
- 1) {
501 sample
= isl_tab_get_sample_value(tab
);
504 if (tab
->n_unbounded
&& !isl_int_is_one(sample
->el
[0])) {
505 sample
= isl_mat_vec_product(isl_mat_copy(tab
->basis
),
507 sample
= isl_vec_ceil(sample
);
508 sample
= isl_mat_vec_inverse_product(
509 isl_mat_copy(tab
->basis
), sample
);
512 sample
= isl_vec_alloc(ctx
, 0);
527 static struct isl_vec
*sample_bounded(struct isl_basic_set
*bset
);
529 /* Compute a sample point of the given basic set, based on the given,
530 * non-trivial factorization.
532 static __isl_give isl_vec
*factored_sample(__isl_take isl_basic_set
*bset
,
533 __isl_take isl_factorizer
*f
)
536 isl_vec
*sample
= NULL
;
541 ctx
= isl_basic_set_get_ctx(bset
);
545 nparam
= isl_basic_set_dim(bset
, isl_dim_param
);
546 nvar
= isl_basic_set_dim(bset
, isl_dim_set
);
548 sample
= isl_vec_alloc(ctx
, 1 + isl_basic_set_total_dim(bset
));
551 isl_int_set_si(sample
->el
[0], 1);
553 bset
= isl_morph_basic_set(isl_morph_copy(f
->morph
), bset
);
555 for (i
= 0, n
= 0; i
< f
->n_group
; ++i
) {
556 isl_basic_set
*bset_i
;
559 bset_i
= isl_basic_set_copy(bset
);
560 bset_i
= isl_basic_set_drop_constraints_involving(bset_i
,
561 nparam
+ n
+ f
->len
[i
], nvar
- n
- f
->len
[i
]);
562 bset_i
= isl_basic_set_drop_constraints_involving(bset_i
,
564 bset_i
= isl_basic_set_drop(bset_i
, isl_dim_set
,
565 n
+ f
->len
[i
], nvar
- n
- f
->len
[i
]);
566 bset_i
= isl_basic_set_drop(bset_i
, isl_dim_set
, 0, n
);
568 sample_i
= sample_bounded(bset_i
);
571 if (sample_i
->size
== 0) {
572 isl_basic_set_free(bset
);
573 isl_factorizer_free(f
);
574 isl_vec_free(sample
);
577 isl_seq_cpy(sample
->el
+ 1 + nparam
+ n
,
578 sample_i
->el
+ 1, f
->len
[i
]);
579 isl_vec_free(sample_i
);
584 f
->morph
= isl_morph_inverse(f
->morph
);
585 sample
= isl_morph_vec(isl_morph_copy(f
->morph
), sample
);
587 isl_basic_set_free(bset
);
588 isl_factorizer_free(f
);
591 isl_basic_set_free(bset
);
592 isl_factorizer_free(f
);
593 isl_vec_free(sample
);
597 /* Given a basic set that is known to be bounded, find and return
598 * an integer point in the basic set, if there is any.
600 * After handling some trivial cases, we construct a tableau
601 * and then use isl_tab_sample to find a sample, passing it
602 * the identity matrix as initial basis.
604 static struct isl_vec
*sample_bounded(struct isl_basic_set
*bset
)
607 struct isl_vec
*sample
;
608 struct isl_tab
*tab
= NULL
;
614 if (isl_basic_set_plain_is_empty(bset
))
615 return empty_sample(bset
);
617 dim
= isl_basic_set_total_dim(bset
);
619 return zero_sample(bset
);
621 return interval_sample(bset
);
623 return sample_eq(bset
, sample_bounded
);
625 f
= isl_basic_set_factorizer(bset
);
629 return factored_sample(bset
, f
);
630 isl_factorizer_free(f
);
632 tab
= isl_tab_from_basic_set(bset
, 1);
633 if (tab
&& tab
->empty
) {
635 ISL_F_SET(bset
, ISL_BASIC_SET_EMPTY
);
636 sample
= isl_vec_alloc(isl_basic_set_get_ctx(bset
), 0);
637 isl_basic_set_free(bset
);
641 if (!ISL_F_ISSET(bset
, ISL_BASIC_SET_NO_IMPLICIT
))
642 if (isl_tab_detect_implicit_equalities(tab
) < 0)
645 sample
= isl_tab_sample(tab
);
649 if (sample
->size
> 0) {
650 isl_vec_free(bset
->sample
);
651 bset
->sample
= isl_vec_copy(sample
);
654 isl_basic_set_free(bset
);
658 isl_basic_set_free(bset
);
663 /* Given a basic set "bset" and a value "sample" for the first coordinates
664 * of bset, plug in these values and drop the corresponding coordinates.
666 * We do this by computing the preimage of the transformation
672 * where [1 s] is the sample value and I is the identity matrix of the
673 * appropriate dimension.
675 static struct isl_basic_set
*plug_in(struct isl_basic_set
*bset
,
676 struct isl_vec
*sample
)
682 if (!bset
|| !sample
)
685 total
= isl_basic_set_total_dim(bset
);
686 T
= isl_mat_alloc(bset
->ctx
, 1 + total
, 1 + total
- (sample
->size
- 1));
690 for (i
= 0; i
< sample
->size
; ++i
) {
691 isl_int_set(T
->row
[i
][0], sample
->el
[i
]);
692 isl_seq_clr(T
->row
[i
] + 1, T
->n_col
- 1);
694 for (i
= 0; i
< T
->n_col
- 1; ++i
) {
695 isl_seq_clr(T
->row
[sample
->size
+ i
], T
->n_col
);
696 isl_int_set_si(T
->row
[sample
->size
+ i
][1 + i
], 1);
698 isl_vec_free(sample
);
700 bset
= isl_basic_set_preimage(bset
, T
);
703 isl_basic_set_free(bset
);
704 isl_vec_free(sample
);
708 /* Given a basic set "bset", return any (possibly non-integer) point
711 static struct isl_vec
*rational_sample(struct isl_basic_set
*bset
)
714 struct isl_vec
*sample
;
719 tab
= isl_tab_from_basic_set(bset
, 0);
720 sample
= isl_tab_get_sample_value(tab
);
723 isl_basic_set_free(bset
);
728 /* Given a linear cone "cone" and a rational point "vec",
729 * construct a polyhedron with shifted copies of the constraints in "cone",
730 * i.e., a polyhedron with "cone" as its recession cone, such that each
731 * point x in this polyhedron is such that the unit box positioned at x
732 * lies entirely inside the affine cone 'vec + cone'.
733 * Any rational point in this polyhedron may therefore be rounded up
734 * to yield an integer point that lies inside said affine cone.
736 * Denote the constraints of cone by "<a_i, x> >= 0" and the rational
737 * point "vec" by v/d.
738 * Let b_i = <a_i, v>. Then the affine cone 'vec + cone' is given
739 * by <a_i, x> - b/d >= 0.
740 * The polyhedron <a_i, x> - ceil{b/d} >= 0 is a subset of this affine cone.
741 * We prefer this polyhedron over the actual affine cone because it doesn't
742 * require a scaling of the constraints.
743 * If each of the vertices of the unit cube positioned at x lies inside
744 * this polyhedron, then the whole unit cube at x lies inside the affine cone.
745 * We therefore impose that x' = x + \sum e_i, for any selection of unit
746 * vectors lies inside the polyhedron, i.e.,
748 * <a_i, x'> - ceil{b/d} = <a_i, x> + sum a_i - ceil{b/d} >= 0
750 * The most stringent of these constraints is the one that selects
751 * all negative a_i, so the polyhedron we are looking for has constraints
753 * <a_i, x> + sum_{a_i < 0} a_i - ceil{b/d} >= 0
755 * Note that if cone were known to have only non-negative rays
756 * (which can be accomplished by a unimodular transformation),
757 * then we would only have to check the points x' = x + e_i
758 * and we only have to add the smallest negative a_i (if any)
759 * instead of the sum of all negative a_i.
761 static struct isl_basic_set
*shift_cone(struct isl_basic_set
*cone
,
767 struct isl_basic_set
*shift
= NULL
;
772 isl_assert(cone
->ctx
, cone
->n_eq
== 0, goto error
);
774 total
= isl_basic_set_total_dim(cone
);
776 shift
= isl_basic_set_alloc_space(isl_basic_set_get_space(cone
),
779 for (i
= 0; i
< cone
->n_ineq
; ++i
) {
780 k
= isl_basic_set_alloc_inequality(shift
);
783 isl_seq_cpy(shift
->ineq
[k
] + 1, cone
->ineq
[i
] + 1, total
);
784 isl_seq_inner_product(shift
->ineq
[k
] + 1, vec
->el
+ 1, total
,
786 isl_int_cdiv_q(shift
->ineq
[k
][0],
787 shift
->ineq
[k
][0], vec
->el
[0]);
788 isl_int_neg(shift
->ineq
[k
][0], shift
->ineq
[k
][0]);
789 for (j
= 0; j
< total
; ++j
) {
790 if (isl_int_is_nonneg(shift
->ineq
[k
][1 + j
]))
792 isl_int_add(shift
->ineq
[k
][0],
793 shift
->ineq
[k
][0], shift
->ineq
[k
][1 + j
]);
797 isl_basic_set_free(cone
);
800 return isl_basic_set_finalize(shift
);
802 isl_basic_set_free(shift
);
803 isl_basic_set_free(cone
);
808 /* Given a rational point vec in a (transformed) basic set,
809 * such that cone is the recession cone of the original basic set,
810 * "round up" the rational point to an integer point.
812 * We first check if the rational point just happens to be integer.
813 * If not, we transform the cone in the same way as the basic set,
814 * pick a point x in this cone shifted to the rational point such that
815 * the whole unit cube at x is also inside this affine cone.
816 * Then we simply round up the coordinates of x and return the
817 * resulting integer point.
819 static struct isl_vec
*round_up_in_cone(struct isl_vec
*vec
,
820 struct isl_basic_set
*cone
, struct isl_mat
*U
)
824 if (!vec
|| !cone
|| !U
)
827 isl_assert(vec
->ctx
, vec
->size
!= 0, goto error
);
828 if (isl_int_is_one(vec
->el
[0])) {
830 isl_basic_set_free(cone
);
834 total
= isl_basic_set_total_dim(cone
);
835 cone
= isl_basic_set_preimage(cone
, U
);
836 cone
= isl_basic_set_remove_dims(cone
, isl_dim_set
,
837 0, total
- (vec
->size
- 1));
839 cone
= shift_cone(cone
, vec
);
841 vec
= rational_sample(cone
);
842 vec
= isl_vec_ceil(vec
);
847 isl_basic_set_free(cone
);
851 /* Concatenate two integer vectors, i.e., two vectors with denominator
852 * (stored in element 0) equal to 1.
854 static struct isl_vec
*vec_concat(struct isl_vec
*vec1
, struct isl_vec
*vec2
)
860 isl_assert(vec1
->ctx
, vec1
->size
> 0, goto error
);
861 isl_assert(vec2
->ctx
, vec2
->size
> 0, goto error
);
862 isl_assert(vec1
->ctx
, isl_int_is_one(vec1
->el
[0]), goto error
);
863 isl_assert(vec2
->ctx
, isl_int_is_one(vec2
->el
[0]), goto error
);
865 vec
= isl_vec_alloc(vec1
->ctx
, vec1
->size
+ vec2
->size
- 1);
869 isl_seq_cpy(vec
->el
, vec1
->el
, vec1
->size
);
870 isl_seq_cpy(vec
->el
+ vec1
->size
, vec2
->el
+ 1, vec2
->size
- 1);
882 /* Give a basic set "bset" with recession cone "cone", compute and
883 * return an integer point in bset, if any.
885 * If the recession cone is full-dimensional, then we know that
886 * bset contains an infinite number of integer points and it is
887 * fairly easy to pick one of them.
888 * If the recession cone is not full-dimensional, then we first
889 * transform bset such that the bounded directions appear as
890 * the first dimensions of the transformed basic set.
891 * We do this by using a unimodular transformation that transforms
892 * the equalities in the recession cone to equalities on the first
895 * The transformed set is then projected onto its bounded dimensions.
896 * Note that to compute this projection, we can simply drop all constraints
897 * involving any of the unbounded dimensions since these constraints
898 * cannot be combined to produce a constraint on the bounded dimensions.
899 * To see this, assume that there is such a combination of constraints
900 * that produces a constraint on the bounded dimensions. This means
901 * that some combination of the unbounded dimensions has both an upper
902 * bound and a lower bound in terms of the bounded dimensions, but then
903 * this combination would be a bounded direction too and would have been
904 * transformed into a bounded dimensions.
906 * We then compute a sample value in the bounded dimensions.
907 * If no such value can be found, then the original set did not contain
908 * any integer points and we are done.
909 * Otherwise, we plug in the value we found in the bounded dimensions,
910 * project out these bounded dimensions and end up with a set with
911 * a full-dimensional recession cone.
912 * A sample point in this set is computed by "rounding up" any
913 * rational point in the set.
915 * The sample points in the bounded and unbounded dimensions are
916 * then combined into a single sample point and transformed back
917 * to the original space.
919 __isl_give isl_vec
*isl_basic_set_sample_with_cone(
920 __isl_take isl_basic_set
*bset
, __isl_take isl_basic_set
*cone
)
924 struct isl_mat
*M
, *U
;
925 struct isl_vec
*sample
;
926 struct isl_vec
*cone_sample
;
928 struct isl_basic_set
*bounded
;
933 ctx
= isl_basic_set_get_ctx(bset
);
934 total
= isl_basic_set_total_dim(cone
);
935 cone_dim
= total
- cone
->n_eq
;
937 M
= isl_mat_sub_alloc6(ctx
, cone
->eq
, 0, cone
->n_eq
, 1, total
);
938 M
= isl_mat_left_hermite(M
, 0, &U
, NULL
);
943 U
= isl_mat_lin_to_aff(U
);
944 bset
= isl_basic_set_preimage(bset
, isl_mat_copy(U
));
946 bounded
= isl_basic_set_copy(bset
);
947 bounded
= isl_basic_set_drop_constraints_involving(bounded
,
948 total
- cone_dim
, cone_dim
);
949 bounded
= isl_basic_set_drop_dims(bounded
, total
- cone_dim
, cone_dim
);
950 sample
= sample_bounded(bounded
);
951 if (!sample
|| sample
->size
== 0) {
952 isl_basic_set_free(bset
);
953 isl_basic_set_free(cone
);
957 bset
= plug_in(bset
, isl_vec_copy(sample
));
958 cone_sample
= rational_sample(bset
);
959 cone_sample
= round_up_in_cone(cone_sample
, cone
, isl_mat_copy(U
));
960 sample
= vec_concat(sample
, cone_sample
);
961 sample
= isl_mat_vec_product(U
, sample
);
964 isl_basic_set_free(cone
);
965 isl_basic_set_free(bset
);
969 static void vec_sum_of_neg(struct isl_vec
*v
, isl_int
*s
)
973 isl_int_set_si(*s
, 0);
975 for (i
= 0; i
< v
->size
; ++i
)
976 if (isl_int_is_neg(v
->el
[i
]))
977 isl_int_add(*s
, *s
, v
->el
[i
]);
980 /* Given a tableau "tab", a tableau "tab_cone" that corresponds
981 * to the recession cone and the inverse of a new basis U = inv(B),
982 * with the unbounded directions in B last,
983 * add constraints to "tab" that ensure any rational value
984 * in the unbounded directions can be rounded up to an integer value.
986 * The new basis is given by x' = B x, i.e., x = U x'.
987 * For any rational value of the last tab->n_unbounded coordinates
988 * in the update tableau, the value that is obtained by rounding
989 * up this value should be contained in the original tableau.
990 * For any constraint "a x + c >= 0", we therefore need to add
991 * a constraint "a x + c + s >= 0", with s the sum of all negative
992 * entries in the last elements of "a U".
994 * Since we are not interested in the first entries of any of the "a U",
995 * we first drop the columns of U that correpond to bounded directions.
997 static int tab_shift_cone(struct isl_tab
*tab
,
998 struct isl_tab
*tab_cone
, struct isl_mat
*U
)
1002 struct isl_basic_set
*bset
= NULL
;
1004 if (tab
&& tab
->n_unbounded
== 0) {
1009 if (!tab
|| !tab_cone
|| !U
)
1011 bset
= isl_tab_peek_bset(tab_cone
);
1012 U
= isl_mat_drop_cols(U
, 0, tab
->n_var
- tab
->n_unbounded
);
1013 for (i
= 0; i
< bset
->n_ineq
; ++i
) {
1015 struct isl_vec
*row
= NULL
;
1016 if (isl_tab_is_equality(tab_cone
, tab_cone
->n_eq
+ i
))
1018 row
= isl_vec_alloc(bset
->ctx
, tab_cone
->n_var
);
1021 isl_seq_cpy(row
->el
, bset
->ineq
[i
] + 1, tab_cone
->n_var
);
1022 row
= isl_vec_mat_product(row
, isl_mat_copy(U
));
1025 vec_sum_of_neg(row
, &v
);
1027 if (isl_int_is_zero(v
))
1029 if (isl_tab_extend_cons(tab
, 1) < 0)
1031 isl_int_add(bset
->ineq
[i
][0], bset
->ineq
[i
][0], v
);
1032 ok
= isl_tab_add_ineq(tab
, bset
->ineq
[i
]) >= 0;
1033 isl_int_sub(bset
->ineq
[i
][0], bset
->ineq
[i
][0], v
);
1047 /* Compute and return an initial basis for the possibly
1048 * unbounded tableau "tab". "tab_cone" is a tableau
1049 * for the corresponding recession cone.
1050 * Additionally, add constraints to "tab" that ensure
1051 * that any rational value for the unbounded directions
1052 * can be rounded up to an integer value.
1054 * If the tableau is bounded, i.e., if the recession cone
1055 * is zero-dimensional, then we just use inital_basis.
1056 * Otherwise, we construct a basis whose first directions
1057 * correspond to equalities, followed by bounded directions,
1058 * i.e., equalities in the recession cone.
1059 * The remaining directions are then unbounded.
1061 int isl_tab_set_initial_basis_with_cone(struct isl_tab
*tab
,
1062 struct isl_tab
*tab_cone
)
1065 struct isl_mat
*cone_eq
;
1066 struct isl_mat
*U
, *Q
;
1068 if (!tab
|| !tab_cone
)
1071 if (tab_cone
->n_col
== tab_cone
->n_dead
) {
1072 tab
->basis
= initial_basis(tab
);
1073 return tab
->basis
? 0 : -1;
1076 eq
= tab_equalities(tab
);
1079 tab
->n_zero
= eq
->n_row
;
1080 cone_eq
= tab_equalities(tab_cone
);
1081 eq
= isl_mat_concat(eq
, cone_eq
);
1084 tab
->n_unbounded
= tab
->n_var
- (eq
->n_row
- tab
->n_zero
);
1085 eq
= isl_mat_left_hermite(eq
, 0, &U
, &Q
);
1089 tab
->basis
= isl_mat_lin_to_aff(Q
);
1090 if (tab_shift_cone(tab
, tab_cone
, U
) < 0)
1097 /* Compute and return a sample point in bset using generalized basis
1098 * reduction. We first check if the input set has a non-trivial
1099 * recession cone. If so, we perform some extra preprocessing in
1100 * sample_with_cone. Otherwise, we directly perform generalized basis
1103 static struct isl_vec
*gbr_sample(struct isl_basic_set
*bset
)
1106 struct isl_basic_set
*cone
;
1108 dim
= isl_basic_set_total_dim(bset
);
1110 cone
= isl_basic_set_recession_cone(isl_basic_set_copy(bset
));
1114 if (cone
->n_eq
< dim
)
1115 return isl_basic_set_sample_with_cone(bset
, cone
);
1117 isl_basic_set_free(cone
);
1118 return sample_bounded(bset
);
1120 isl_basic_set_free(bset
);
1124 static __isl_give isl_vec
*basic_set_sample(__isl_take isl_basic_set
*bset
,
1127 struct isl_ctx
*ctx
;
1133 if (isl_basic_set_plain_is_empty(bset
))
1134 return empty_sample(bset
);
1136 dim
= isl_basic_set_n_dim(bset
);
1137 isl_assert(ctx
, isl_basic_set_n_param(bset
) == 0, goto error
);
1138 isl_assert(ctx
, bset
->n_div
== 0, goto error
);
1140 if (bset
->sample
&& bset
->sample
->size
== 1 + dim
) {
1141 int contains
= isl_basic_set_contains(bset
, bset
->sample
);
1145 struct isl_vec
*sample
= isl_vec_copy(bset
->sample
);
1146 isl_basic_set_free(bset
);
1150 isl_vec_free(bset
->sample
);
1151 bset
->sample
= NULL
;
1154 return sample_eq(bset
, bounded
? isl_basic_set_sample_bounded
1155 : isl_basic_set_sample_vec
);
1157 return zero_sample(bset
);
1159 return interval_sample(bset
);
1161 return bounded
? sample_bounded(bset
) : gbr_sample(bset
);
1163 isl_basic_set_free(bset
);
1167 __isl_give isl_vec
*isl_basic_set_sample_vec(__isl_take isl_basic_set
*bset
)
1169 return basic_set_sample(bset
, 0);
1172 /* Compute an integer sample in "bset", where the caller guarantees
1173 * that "bset" is bounded.
1175 struct isl_vec
*isl_basic_set_sample_bounded(struct isl_basic_set
*bset
)
1177 return basic_set_sample(bset
, 1);
1180 __isl_give isl_basic_set
*isl_basic_set_from_vec(__isl_take isl_vec
*vec
)
1184 struct isl_basic_set
*bset
= NULL
;
1185 struct isl_ctx
*ctx
;
1191 isl_assert(ctx
, vec
->size
!= 0, goto error
);
1193 bset
= isl_basic_set_alloc(ctx
, 0, vec
->size
- 1, 0, vec
->size
- 1, 0);
1196 dim
= isl_basic_set_n_dim(bset
);
1197 for (i
= dim
- 1; i
>= 0; --i
) {
1198 k
= isl_basic_set_alloc_equality(bset
);
1201 isl_seq_clr(bset
->eq
[k
], 1 + dim
);
1202 isl_int_neg(bset
->eq
[k
][0], vec
->el
[1 + i
]);
1203 isl_int_set(bset
->eq
[k
][1 + i
], vec
->el
[0]);
1209 isl_basic_set_free(bset
);
1214 __isl_give isl_basic_map
*isl_basic_map_sample(__isl_take isl_basic_map
*bmap
)
1216 struct isl_basic_set
*bset
;
1217 struct isl_vec
*sample_vec
;
1219 bset
= isl_basic_map_underlying_set(isl_basic_map_copy(bmap
));
1220 sample_vec
= isl_basic_set_sample_vec(bset
);
1223 if (sample_vec
->size
== 0) {
1224 isl_vec_free(sample_vec
);
1225 return isl_basic_map_set_to_empty(bmap
);
1227 isl_vec_free(bmap
->sample
);
1228 bmap
->sample
= isl_vec_copy(sample_vec
);
1229 bset
= isl_basic_set_from_vec(sample_vec
);
1230 return isl_basic_map_overlying_set(bset
, bmap
);
1232 isl_basic_map_free(bmap
);
1236 __isl_give isl_basic_set
*isl_basic_set_sample(__isl_take isl_basic_set
*bset
)
1238 return isl_basic_map_sample(bset
);
1241 __isl_give isl_basic_map
*isl_map_sample(__isl_take isl_map
*map
)
1244 isl_basic_map
*sample
= NULL
;
1249 for (i
= 0; i
< map
->n
; ++i
) {
1250 sample
= isl_basic_map_sample(isl_basic_map_copy(map
->p
[i
]));
1253 if (!ISL_F_ISSET(sample
, ISL_BASIC_MAP_EMPTY
))
1255 isl_basic_map_free(sample
);
1258 sample
= isl_basic_map_empty(isl_map_get_space(map
));
1266 __isl_give isl_basic_set
*isl_set_sample(__isl_take isl_set
*set
)
1268 return bset_from_bmap(isl_map_sample(set_to_map(set
)));
1271 __isl_give isl_point
*isl_basic_set_sample_point(__isl_take isl_basic_set
*bset
)
1276 dim
= isl_basic_set_get_space(bset
);
1277 bset
= isl_basic_set_underlying_set(bset
);
1278 vec
= isl_basic_set_sample_vec(bset
);
1280 return isl_point_alloc(dim
, vec
);
1283 __isl_give isl_point
*isl_set_sample_point(__isl_take isl_set
*set
)
1291 for (i
= 0; i
< set
->n
; ++i
) {
1292 pnt
= isl_basic_set_sample_point(isl_basic_set_copy(set
->p
[i
]));
1295 if (!isl_point_is_void(pnt
))
1297 isl_point_free(pnt
);
1300 pnt
= isl_point_void(isl_set_get_space(set
));