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 static struct isl_vec
*empty_sample(struct isl_basic_set
*bset
)
28 vec
= isl_vec_alloc(bset
->ctx
, 0);
29 isl_basic_set_free(bset
);
33 /* Construct a zero sample of the same dimension as bset.
34 * As a special case, if bset is zero-dimensional, this
35 * function creates a zero-dimensional sample point.
37 static struct isl_vec
*zero_sample(struct isl_basic_set
*bset
)
40 struct isl_vec
*sample
;
42 dim
= isl_basic_set_total_dim(bset
);
43 sample
= isl_vec_alloc(bset
->ctx
, 1 + dim
);
45 isl_int_set_si(sample
->el
[0], 1);
46 isl_seq_clr(sample
->el
+ 1, dim
);
48 isl_basic_set_free(bset
);
52 static struct isl_vec
*interval_sample(struct isl_basic_set
*bset
)
56 struct isl_vec
*sample
;
58 bset
= isl_basic_set_simplify(bset
);
61 if (isl_basic_set_plain_is_empty(bset
))
62 return empty_sample(bset
);
63 if (bset
->n_eq
== 0 && bset
->n_ineq
== 0)
64 return zero_sample(bset
);
66 sample
= isl_vec_alloc(bset
->ctx
, 2);
71 isl_int_set_si(sample
->block
.data
[0], 1);
74 isl_assert(bset
->ctx
, bset
->n_eq
== 1, goto error
);
75 isl_assert(bset
->ctx
, bset
->n_ineq
== 0, goto error
);
76 if (isl_int_is_one(bset
->eq
[0][1]))
77 isl_int_neg(sample
->el
[1], bset
->eq
[0][0]);
79 isl_assert(bset
->ctx
, isl_int_is_negone(bset
->eq
[0][1]),
81 isl_int_set(sample
->el
[1], bset
->eq
[0][0]);
83 isl_basic_set_free(bset
);
88 if (isl_int_is_one(bset
->ineq
[0][1]))
89 isl_int_neg(sample
->block
.data
[1], bset
->ineq
[0][0]);
91 isl_int_set(sample
->block
.data
[1], bset
->ineq
[0][0]);
92 for (i
= 1; i
< bset
->n_ineq
; ++i
) {
93 isl_seq_inner_product(sample
->block
.data
,
94 bset
->ineq
[i
], 2, &t
);
95 if (isl_int_is_neg(t
))
99 if (i
< bset
->n_ineq
) {
100 isl_vec_free(sample
);
101 return empty_sample(bset
);
104 isl_basic_set_free(bset
);
107 isl_basic_set_free(bset
);
108 isl_vec_free(sample
);
112 /* Find a sample integer point, if any, in bset, which is known
113 * to have equalities. If bset contains no integer points, then
114 * return a zero-length vector.
115 * We simply remove the known equalities, compute a sample
116 * in the resulting bset, using the specified recurse function,
117 * and then transform the sample back to the original space.
119 static struct isl_vec
*sample_eq(struct isl_basic_set
*bset
,
120 struct isl_vec
*(*recurse
)(struct isl_basic_set
*))
123 struct isl_vec
*sample
;
128 bset
= isl_basic_set_remove_equalities(bset
, &T
, NULL
);
129 sample
= recurse(bset
);
130 if (!sample
|| sample
->size
== 0)
133 sample
= isl_mat_vec_product(T
, sample
);
137 /* Return a matrix containing the equalities of the tableau
138 * in constraint form. The tableau is assumed to have
139 * an associated bset that has been kept up-to-date.
141 static struct isl_mat
*tab_equalities(struct isl_tab
*tab
)
146 struct isl_basic_set
*bset
;
151 bset
= isl_tab_peek_bset(tab
);
152 isl_assert(tab
->mat
->ctx
, bset
, return NULL
);
154 n_eq
= tab
->n_var
- tab
->n_col
+ tab
->n_dead
;
155 if (tab
->empty
|| n_eq
== 0)
156 return isl_mat_alloc(tab
->mat
->ctx
, 0, tab
->n_var
);
157 if (n_eq
== tab
->n_var
)
158 return isl_mat_identity(tab
->mat
->ctx
, tab
->n_var
);
160 eq
= isl_mat_alloc(tab
->mat
->ctx
, n_eq
, tab
->n_var
);
163 for (i
= 0, j
= 0; i
< tab
->n_con
; ++i
) {
164 if (tab
->con
[i
].is_row
)
166 if (tab
->con
[i
].index
>= 0 && tab
->con
[i
].index
>= tab
->n_dead
)
169 isl_seq_cpy(eq
->row
[j
], bset
->eq
[i
] + 1, tab
->n_var
);
171 isl_seq_cpy(eq
->row
[j
],
172 bset
->ineq
[i
- bset
->n_eq
] + 1, tab
->n_var
);
175 isl_assert(bset
->ctx
, j
== n_eq
, goto error
);
182 /* Compute and return an initial basis for the bounded tableau "tab".
184 * If the tableau is either full-dimensional or zero-dimensional,
185 * the we simply return an identity matrix.
186 * Otherwise, we construct a basis whose first directions correspond
189 static struct isl_mat
*initial_basis(struct isl_tab
*tab
)
195 tab
->n_unbounded
= 0;
196 tab
->n_zero
= n_eq
= tab
->n_var
- tab
->n_col
+ tab
->n_dead
;
197 if (tab
->empty
|| n_eq
== 0 || n_eq
== tab
->n_var
)
198 return isl_mat_identity(tab
->mat
->ctx
, 1 + tab
->n_var
);
200 eq
= tab_equalities(tab
);
201 eq
= isl_mat_left_hermite(eq
, 0, NULL
, &Q
);
206 Q
= isl_mat_lin_to_aff(Q
);
210 /* Compute the minimum of the current ("level") basis row over "tab"
211 * and store the result in position "level" of "min".
213 * This function assumes that at least one more row and at least
214 * one more element in the constraint array are available in the tableau.
216 static enum isl_lp_result
compute_min(isl_ctx
*ctx
, struct isl_tab
*tab
,
217 __isl_keep isl_vec
*min
, int level
)
219 return isl_tab_min(tab
, tab
->basis
->row
[1 + level
],
220 ctx
->one
, &min
->el
[level
], NULL
, 0);
223 /* Compute the maximum of the current ("level") basis row over "tab"
224 * and store the result in position "level" of "max".
226 * This function assumes that at least one more row and at least
227 * one more element in the constraint array are available in the tableau.
229 static enum isl_lp_result
compute_max(isl_ctx
*ctx
, struct isl_tab
*tab
,
230 __isl_keep isl_vec
*max
, int level
)
232 enum isl_lp_result res
;
233 unsigned dim
= tab
->n_var
;
235 isl_seq_neg(tab
->basis
->row
[1 + level
] + 1,
236 tab
->basis
->row
[1 + level
] + 1, dim
);
237 res
= isl_tab_min(tab
, tab
->basis
->row
[1 + level
],
238 ctx
->one
, &max
->el
[level
], NULL
, 0);
239 isl_seq_neg(tab
->basis
->row
[1 + level
] + 1,
240 tab
->basis
->row
[1 + level
] + 1, dim
);
241 isl_int_neg(max
->el
[level
], max
->el
[level
]);
246 /* Perform a greedy search for an integer point in the set represented
247 * by "tab", given that the minimal rational value (rounded up to the
248 * nearest integer) at "level" is smaller than the maximal rational
249 * value (rounded down to the nearest integer).
251 * Return 1 if we have found an integer point (if tab->n_unbounded > 0
252 * then we may have only found integer values for the bounded dimensions
253 * and it is the responsibility of the caller to extend this solution
254 * to the unbounded dimensions).
255 * Return 0 if greedy search did not result in a solution.
256 * Return -1 if some error occurred.
258 * We assign a value half-way between the minimum and the maximum
259 * to the current dimension and check if the minimal value of the
260 * next dimension is still smaller than (or equal) to the maximal value.
261 * We continue this process until either
262 * - the minimal value (rounded up) is greater than the maximal value
263 * (rounded down). In this case, greedy search has failed.
264 * - we have exhausted all bounded dimensions, meaning that we have
266 * - the sample value of the tableau is integral.
267 * - some error has occurred.
269 static int greedy_search(isl_ctx
*ctx
, struct isl_tab
*tab
,
270 __isl_keep isl_vec
*min
, __isl_keep isl_vec
*max
, int level
)
272 struct isl_tab_undo
*snap
;
273 enum isl_lp_result res
;
275 snap
= isl_tab_snap(tab
);
278 isl_int_add(tab
->basis
->row
[1 + level
][0],
279 min
->el
[level
], max
->el
[level
]);
280 isl_int_fdiv_q_ui(tab
->basis
->row
[1 + level
][0],
281 tab
->basis
->row
[1 + level
][0], 2);
282 isl_int_neg(tab
->basis
->row
[1 + level
][0],
283 tab
->basis
->row
[1 + level
][0]);
284 if (isl_tab_add_valid_eq(tab
, tab
->basis
->row
[1 + level
]) < 0)
286 isl_int_set_si(tab
->basis
->row
[1 + level
][0], 0);
288 if (++level
>= tab
->n_var
- tab
->n_unbounded
)
290 if (isl_tab_sample_is_integer(tab
))
293 res
= compute_min(ctx
, tab
, min
, level
);
294 if (res
== isl_lp_error
)
296 if (res
!= isl_lp_ok
)
297 isl_die(ctx
, isl_error_internal
,
298 "expecting bounded rational solution",
300 res
= compute_max(ctx
, tab
, max
, level
);
301 if (res
== isl_lp_error
)
303 if (res
!= isl_lp_ok
)
304 isl_die(ctx
, isl_error_internal
,
305 "expecting bounded rational solution",
307 } while (isl_int_le(min
->el
[level
], max
->el
[level
]));
309 if (isl_tab_rollback(tab
, snap
) < 0)
315 /* Given a tableau representing a set, find and return
316 * an integer point in the set, if there is any.
318 * We perform a depth first search
319 * for an integer point, by scanning all possible values in the range
320 * attained by a basis vector, where an initial basis may have been set
321 * by the calling function. Otherwise an initial basis that exploits
322 * the equalities in the tableau is created.
323 * tab->n_zero is currently ignored and is clobbered by this function.
325 * The tableau is allowed to have unbounded direction, but then
326 * the calling function needs to set an initial basis, with the
327 * unbounded directions last and with tab->n_unbounded set
328 * to the number of unbounded directions.
329 * Furthermore, the calling functions needs to add shifted copies
330 * of all constraints involving unbounded directions to ensure
331 * that any feasible rational value in these directions can be rounded
332 * up to yield a feasible integer value.
333 * In particular, let B define the given basis x' = B x
334 * and let T be the inverse of B, i.e., X = T x'.
335 * Let a x + c >= 0 be a constraint of the set represented by the tableau,
336 * or a T x' + c >= 0 in terms of the given basis. Assume that
337 * the bounded directions have an integer value, then we can safely
338 * round up the values for the unbounded directions if we make sure
339 * that x' not only satisfies the original constraint, but also
340 * the constraint "a T x' + c + s >= 0" with s the sum of all
341 * negative values in the last n_unbounded entries of "a T".
342 * The calling function therefore needs to add the constraint
343 * a x + c + s >= 0. The current function then scans the first
344 * directions for an integer value and once those have been found,
345 * it can compute "T ceil(B x)" to yield an integer point in the set.
346 * Note that during the search, the first rows of B may be changed
347 * by a basis reduction, but the last n_unbounded rows of B remain
348 * unaltered and are also not mixed into the first rows.
350 * The search is implemented iteratively. "level" identifies the current
351 * basis vector. "init" is true if we want the first value at the current
352 * level and false if we want the next value.
354 * At the start of each level, we first check if we can find a solution
355 * using greedy search. If not, we continue with the exhaustive search.
357 * The initial basis is the identity matrix. If the range in some direction
358 * contains more than one integer value, we perform basis reduction based
359 * on the value of ctx->opt->gbr
360 * - ISL_GBR_NEVER: never perform basis reduction
361 * - ISL_GBR_ONCE: only perform basis reduction the first
362 * time such a range is encountered
363 * - ISL_GBR_ALWAYS: always perform basis reduction when
364 * such a range is encountered
366 * When ctx->opt->gbr is set to ISL_GBR_ALWAYS, then we allow the basis
367 * reduction computation to return early. That is, as soon as it
368 * finds a reasonable first direction.
370 struct isl_vec
*isl_tab_sample(struct isl_tab
*tab
)
375 struct isl_vec
*sample
;
378 enum isl_lp_result res
;
382 struct isl_tab_undo
**snap
;
387 return isl_vec_alloc(tab
->mat
->ctx
, 0);
390 tab
->basis
= initial_basis(tab
);
393 isl_assert(tab
->mat
->ctx
, tab
->basis
->n_row
== tab
->n_var
+ 1,
395 isl_assert(tab
->mat
->ctx
, tab
->basis
->n_col
== tab
->n_var
+ 1,
402 if (tab
->n_unbounded
== tab
->n_var
) {
403 sample
= isl_tab_get_sample_value(tab
);
404 sample
= isl_mat_vec_product(isl_mat_copy(tab
->basis
), sample
);
405 sample
= isl_vec_ceil(sample
);
406 sample
= isl_mat_vec_inverse_product(isl_mat_copy(tab
->basis
),
411 if (isl_tab_extend_cons(tab
, dim
+ 1) < 0)
414 min
= isl_vec_alloc(ctx
, dim
);
415 max
= isl_vec_alloc(ctx
, dim
);
416 snap
= isl_alloc_array(ctx
, struct isl_tab_undo
*, dim
);
418 if (!min
|| !max
|| !snap
)
429 res
= compute_min(ctx
, tab
, min
, level
);
430 if (res
== isl_lp_error
)
432 if (res
!= isl_lp_ok
)
433 isl_die(ctx
, isl_error_internal
,
434 "expecting bounded rational solution",
436 if (isl_tab_sample_is_integer(tab
))
438 res
= compute_max(ctx
, tab
, max
, level
);
439 if (res
== isl_lp_error
)
441 if (res
!= isl_lp_ok
)
442 isl_die(ctx
, isl_error_internal
,
443 "expecting bounded rational solution",
445 if (isl_tab_sample_is_integer(tab
))
447 choice
= isl_int_lt(min
->el
[level
], max
->el
[level
]);
450 g
= greedy_search(ctx
, tab
, min
, max
, level
);
456 if (!reduced
&& choice
&&
457 ctx
->opt
->gbr
!= ISL_GBR_NEVER
) {
458 unsigned gbr_only_first
;
459 if (ctx
->opt
->gbr
== ISL_GBR_ONCE
)
460 ctx
->opt
->gbr
= ISL_GBR_NEVER
;
462 gbr_only_first
= ctx
->opt
->gbr_only_first
;
463 ctx
->opt
->gbr_only_first
=
464 ctx
->opt
->gbr
== ISL_GBR_ALWAYS
;
465 tab
= isl_tab_compute_reduced_basis(tab
);
466 ctx
->opt
->gbr_only_first
= gbr_only_first
;
467 if (!tab
|| !tab
->basis
)
473 snap
[level
] = isl_tab_snap(tab
);
475 isl_int_add_ui(min
->el
[level
], min
->el
[level
], 1);
477 if (isl_int_gt(min
->el
[level
], max
->el
[level
])) {
481 if (isl_tab_rollback(tab
, snap
[level
]) < 0)
485 isl_int_neg(tab
->basis
->row
[1 + level
][0], min
->el
[level
]);
486 if (isl_tab_add_valid_eq(tab
, tab
->basis
->row
[1 + level
]) < 0)
488 isl_int_set_si(tab
->basis
->row
[1 + level
][0], 0);
489 if (level
+ tab
->n_unbounded
< dim
- 1) {
498 sample
= isl_tab_get_sample_value(tab
);
501 if (tab
->n_unbounded
&& !isl_int_is_one(sample
->el
[0])) {
502 sample
= isl_mat_vec_product(isl_mat_copy(tab
->basis
),
504 sample
= isl_vec_ceil(sample
);
505 sample
= isl_mat_vec_inverse_product(
506 isl_mat_copy(tab
->basis
), sample
);
509 sample
= isl_vec_alloc(ctx
, 0);
524 static struct isl_vec
*sample_bounded(struct isl_basic_set
*bset
);
526 /* Compute a sample point of the given basic set, based on the given,
527 * non-trivial factorization.
529 static __isl_give isl_vec
*factored_sample(__isl_take isl_basic_set
*bset
,
530 __isl_take isl_factorizer
*f
)
533 isl_vec
*sample
= NULL
;
538 ctx
= isl_basic_set_get_ctx(bset
);
542 nparam
= isl_basic_set_dim(bset
, isl_dim_param
);
543 nvar
= isl_basic_set_dim(bset
, isl_dim_set
);
545 sample
= isl_vec_alloc(ctx
, 1 + isl_basic_set_total_dim(bset
));
548 isl_int_set_si(sample
->el
[0], 1);
550 bset
= isl_morph_basic_set(isl_morph_copy(f
->morph
), bset
);
552 for (i
= 0, n
= 0; i
< f
->n_group
; ++i
) {
553 isl_basic_set
*bset_i
;
556 bset_i
= isl_basic_set_copy(bset
);
557 bset_i
= isl_basic_set_drop_constraints_involving(bset_i
,
558 nparam
+ n
+ f
->len
[i
], nvar
- n
- f
->len
[i
]);
559 bset_i
= isl_basic_set_drop_constraints_involving(bset_i
,
561 bset_i
= isl_basic_set_drop(bset_i
, isl_dim_set
,
562 n
+ f
->len
[i
], nvar
- n
- f
->len
[i
]);
563 bset_i
= isl_basic_set_drop(bset_i
, isl_dim_set
, 0, n
);
565 sample_i
= sample_bounded(bset_i
);
568 if (sample_i
->size
== 0) {
569 isl_basic_set_free(bset
);
570 isl_factorizer_free(f
);
571 isl_vec_free(sample
);
574 isl_seq_cpy(sample
->el
+ 1 + nparam
+ n
,
575 sample_i
->el
+ 1, f
->len
[i
]);
576 isl_vec_free(sample_i
);
581 f
->morph
= isl_morph_inverse(f
->morph
);
582 sample
= isl_morph_vec(isl_morph_copy(f
->morph
), sample
);
584 isl_basic_set_free(bset
);
585 isl_factorizer_free(f
);
588 isl_basic_set_free(bset
);
589 isl_factorizer_free(f
);
590 isl_vec_free(sample
);
594 /* Given a basic set that is known to be bounded, find and return
595 * an integer point in the basic set, if there is any.
597 * After handling some trivial cases, we construct a tableau
598 * and then use isl_tab_sample to find a sample, passing it
599 * the identity matrix as initial basis.
601 static struct isl_vec
*sample_bounded(struct isl_basic_set
*bset
)
604 struct isl_vec
*sample
;
605 struct isl_tab
*tab
= NULL
;
611 if (isl_basic_set_plain_is_empty(bset
))
612 return empty_sample(bset
);
614 dim
= isl_basic_set_total_dim(bset
);
616 return zero_sample(bset
);
618 return interval_sample(bset
);
620 return sample_eq(bset
, sample_bounded
);
622 f
= isl_basic_set_factorizer(bset
);
626 return factored_sample(bset
, f
);
627 isl_factorizer_free(f
);
629 tab
= isl_tab_from_basic_set(bset
, 1);
630 if (tab
&& tab
->empty
) {
632 ISL_F_SET(bset
, ISL_BASIC_SET_EMPTY
);
633 sample
= isl_vec_alloc(isl_basic_set_get_ctx(bset
), 0);
634 isl_basic_set_free(bset
);
638 if (!ISL_F_ISSET(bset
, ISL_BASIC_SET_NO_IMPLICIT
))
639 if (isl_tab_detect_implicit_equalities(tab
) < 0)
642 sample
= isl_tab_sample(tab
);
646 if (sample
->size
> 0) {
647 isl_vec_free(bset
->sample
);
648 bset
->sample
= isl_vec_copy(sample
);
651 isl_basic_set_free(bset
);
655 isl_basic_set_free(bset
);
660 /* Given a basic set "bset" and a value "sample" for the first coordinates
661 * of bset, plug in these values and drop the corresponding coordinates.
663 * We do this by computing the preimage of the transformation
669 * where [1 s] is the sample value and I is the identity matrix of the
670 * appropriate dimension.
672 static struct isl_basic_set
*plug_in(struct isl_basic_set
*bset
,
673 struct isl_vec
*sample
)
679 if (!bset
|| !sample
)
682 total
= isl_basic_set_total_dim(bset
);
683 T
= isl_mat_alloc(bset
->ctx
, 1 + total
, 1 + total
- (sample
->size
- 1));
687 for (i
= 0; i
< sample
->size
; ++i
) {
688 isl_int_set(T
->row
[i
][0], sample
->el
[i
]);
689 isl_seq_clr(T
->row
[i
] + 1, T
->n_col
- 1);
691 for (i
= 0; i
< T
->n_col
- 1; ++i
) {
692 isl_seq_clr(T
->row
[sample
->size
+ i
], T
->n_col
);
693 isl_int_set_si(T
->row
[sample
->size
+ i
][1 + i
], 1);
695 isl_vec_free(sample
);
697 bset
= isl_basic_set_preimage(bset
, T
);
700 isl_basic_set_free(bset
);
701 isl_vec_free(sample
);
705 /* Given a basic set "bset", return any (possibly non-integer) point
708 static struct isl_vec
*rational_sample(struct isl_basic_set
*bset
)
711 struct isl_vec
*sample
;
716 tab
= isl_tab_from_basic_set(bset
, 0);
717 sample
= isl_tab_get_sample_value(tab
);
720 isl_basic_set_free(bset
);
725 /* Given a linear cone "cone" and a rational point "vec",
726 * construct a polyhedron with shifted copies of the constraints in "cone",
727 * i.e., a polyhedron with "cone" as its recession cone, such that each
728 * point x in this polyhedron is such that the unit box positioned at x
729 * lies entirely inside the affine cone 'vec + cone'.
730 * Any rational point in this polyhedron may therefore be rounded up
731 * to yield an integer point that lies inside said affine cone.
733 * Denote the constraints of cone by "<a_i, x> >= 0" and the rational
734 * point "vec" by v/d.
735 * Let b_i = <a_i, v>. Then the affine cone 'vec + cone' is given
736 * by <a_i, x> - b/d >= 0.
737 * The polyhedron <a_i, x> - ceil{b/d} >= 0 is a subset of this affine cone.
738 * We prefer this polyhedron over the actual affine cone because it doesn't
739 * require a scaling of the constraints.
740 * If each of the vertices of the unit cube positioned at x lies inside
741 * this polyhedron, then the whole unit cube at x lies inside the affine cone.
742 * We therefore impose that x' = x + \sum e_i, for any selection of unit
743 * vectors lies inside the polyhedron, i.e.,
745 * <a_i, x'> - ceil{b/d} = <a_i, x> + sum a_i - ceil{b/d} >= 0
747 * The most stringent of these constraints is the one that selects
748 * all negative a_i, so the polyhedron we are looking for has constraints
750 * <a_i, x> + sum_{a_i < 0} a_i - ceil{b/d} >= 0
752 * Note that if cone were known to have only non-negative rays
753 * (which can be accomplished by a unimodular transformation),
754 * then we would only have to check the points x' = x + e_i
755 * and we only have to add the smallest negative a_i (if any)
756 * instead of the sum of all negative a_i.
758 static struct isl_basic_set
*shift_cone(struct isl_basic_set
*cone
,
764 struct isl_basic_set
*shift
= NULL
;
769 isl_assert(cone
->ctx
, cone
->n_eq
== 0, goto error
);
771 total
= isl_basic_set_total_dim(cone
);
773 shift
= isl_basic_set_alloc_space(isl_basic_set_get_space(cone
),
776 for (i
= 0; i
< cone
->n_ineq
; ++i
) {
777 k
= isl_basic_set_alloc_inequality(shift
);
780 isl_seq_cpy(shift
->ineq
[k
] + 1, cone
->ineq
[i
] + 1, total
);
781 isl_seq_inner_product(shift
->ineq
[k
] + 1, vec
->el
+ 1, total
,
783 isl_int_cdiv_q(shift
->ineq
[k
][0],
784 shift
->ineq
[k
][0], vec
->el
[0]);
785 isl_int_neg(shift
->ineq
[k
][0], shift
->ineq
[k
][0]);
786 for (j
= 0; j
< total
; ++j
) {
787 if (isl_int_is_nonneg(shift
->ineq
[k
][1 + j
]))
789 isl_int_add(shift
->ineq
[k
][0],
790 shift
->ineq
[k
][0], shift
->ineq
[k
][1 + j
]);
794 isl_basic_set_free(cone
);
797 return isl_basic_set_finalize(shift
);
799 isl_basic_set_free(shift
);
800 isl_basic_set_free(cone
);
805 /* Given a rational point vec in a (transformed) basic set,
806 * such that cone is the recession cone of the original basic set,
807 * "round up" the rational point to an integer point.
809 * We first check if the rational point just happens to be integer.
810 * If not, we transform the cone in the same way as the basic set,
811 * pick a point x in this cone shifted to the rational point such that
812 * the whole unit cube at x is also inside this affine cone.
813 * Then we simply round up the coordinates of x and return the
814 * resulting integer point.
816 static struct isl_vec
*round_up_in_cone(struct isl_vec
*vec
,
817 struct isl_basic_set
*cone
, struct isl_mat
*U
)
821 if (!vec
|| !cone
|| !U
)
824 isl_assert(vec
->ctx
, vec
->size
!= 0, goto error
);
825 if (isl_int_is_one(vec
->el
[0])) {
827 isl_basic_set_free(cone
);
831 total
= isl_basic_set_total_dim(cone
);
832 cone
= isl_basic_set_preimage(cone
, U
);
833 cone
= isl_basic_set_remove_dims(cone
, isl_dim_set
,
834 0, total
- (vec
->size
- 1));
836 cone
= shift_cone(cone
, vec
);
838 vec
= rational_sample(cone
);
839 vec
= isl_vec_ceil(vec
);
844 isl_basic_set_free(cone
);
848 /* Concatenate two integer vectors, i.e., two vectors with denominator
849 * (stored in element 0) equal to 1.
851 static struct isl_vec
*vec_concat(struct isl_vec
*vec1
, struct isl_vec
*vec2
)
857 isl_assert(vec1
->ctx
, vec1
->size
> 0, goto error
);
858 isl_assert(vec2
->ctx
, vec2
->size
> 0, goto error
);
859 isl_assert(vec1
->ctx
, isl_int_is_one(vec1
->el
[0]), goto error
);
860 isl_assert(vec2
->ctx
, isl_int_is_one(vec2
->el
[0]), goto error
);
862 vec
= isl_vec_alloc(vec1
->ctx
, vec1
->size
+ vec2
->size
- 1);
866 isl_seq_cpy(vec
->el
, vec1
->el
, vec1
->size
);
867 isl_seq_cpy(vec
->el
+ vec1
->size
, vec2
->el
+ 1, vec2
->size
- 1);
879 /* Give a basic set "bset" with recession cone "cone", compute and
880 * return an integer point in bset, if any.
882 * If the recession cone is full-dimensional, then we know that
883 * bset contains an infinite number of integer points and it is
884 * fairly easy to pick one of them.
885 * If the recession cone is not full-dimensional, then we first
886 * transform bset such that the bounded directions appear as
887 * the first dimensions of the transformed basic set.
888 * We do this by using a unimodular transformation that transforms
889 * the equalities in the recession cone to equalities on the first
892 * The transformed set is then projected onto its bounded dimensions.
893 * Note that to compute this projection, we can simply drop all constraints
894 * involving any of the unbounded dimensions since these constraints
895 * cannot be combined to produce a constraint on the bounded dimensions.
896 * To see this, assume that there is such a combination of constraints
897 * that produces a constraint on the bounded dimensions. This means
898 * that some combination of the unbounded dimensions has both an upper
899 * bound and a lower bound in terms of the bounded dimensions, but then
900 * this combination would be a bounded direction too and would have been
901 * transformed into a bounded dimensions.
903 * We then compute a sample value in the bounded dimensions.
904 * If no such value can be found, then the original set did not contain
905 * any integer points and we are done.
906 * Otherwise, we plug in the value we found in the bounded dimensions,
907 * project out these bounded dimensions and end up with a set with
908 * a full-dimensional recession cone.
909 * A sample point in this set is computed by "rounding up" any
910 * rational point in the set.
912 * The sample points in the bounded and unbounded dimensions are
913 * then combined into a single sample point and transformed back
914 * to the original space.
916 __isl_give isl_vec
*isl_basic_set_sample_with_cone(
917 __isl_take isl_basic_set
*bset
, __isl_take isl_basic_set
*cone
)
921 struct isl_mat
*M
, *U
;
922 struct isl_vec
*sample
;
923 struct isl_vec
*cone_sample
;
925 struct isl_basic_set
*bounded
;
930 ctx
= isl_basic_set_get_ctx(bset
);
931 total
= isl_basic_set_total_dim(cone
);
932 cone_dim
= total
- cone
->n_eq
;
934 M
= isl_mat_sub_alloc6(ctx
, cone
->eq
, 0, cone
->n_eq
, 1, total
);
935 M
= isl_mat_left_hermite(M
, 0, &U
, NULL
);
940 U
= isl_mat_lin_to_aff(U
);
941 bset
= isl_basic_set_preimage(bset
, isl_mat_copy(U
));
943 bounded
= isl_basic_set_copy(bset
);
944 bounded
= isl_basic_set_drop_constraints_involving(bounded
,
945 total
- cone_dim
, cone_dim
);
946 bounded
= isl_basic_set_drop_dims(bounded
, total
- cone_dim
, cone_dim
);
947 sample
= sample_bounded(bounded
);
948 if (!sample
|| sample
->size
== 0) {
949 isl_basic_set_free(bset
);
950 isl_basic_set_free(cone
);
954 bset
= plug_in(bset
, isl_vec_copy(sample
));
955 cone_sample
= rational_sample(bset
);
956 cone_sample
= round_up_in_cone(cone_sample
, cone
, isl_mat_copy(U
));
957 sample
= vec_concat(sample
, cone_sample
);
958 sample
= isl_mat_vec_product(U
, sample
);
961 isl_basic_set_free(cone
);
962 isl_basic_set_free(bset
);
966 static void vec_sum_of_neg(struct isl_vec
*v
, isl_int
*s
)
970 isl_int_set_si(*s
, 0);
972 for (i
= 0; i
< v
->size
; ++i
)
973 if (isl_int_is_neg(v
->el
[i
]))
974 isl_int_add(*s
, *s
, v
->el
[i
]);
977 /* Given a tableau "tab", a tableau "tab_cone" that corresponds
978 * to the recession cone and the inverse of a new basis U = inv(B),
979 * with the unbounded directions in B last,
980 * add constraints to "tab" that ensure any rational value
981 * in the unbounded directions can be rounded up to an integer value.
983 * The new basis is given by x' = B x, i.e., x = U x'.
984 * For any rational value of the last tab->n_unbounded coordinates
985 * in the update tableau, the value that is obtained by rounding
986 * up this value should be contained in the original tableau.
987 * For any constraint "a x + c >= 0", we therefore need to add
988 * a constraint "a x + c + s >= 0", with s the sum of all negative
989 * entries in the last elements of "a U".
991 * Since we are not interested in the first entries of any of the "a U",
992 * we first drop the columns of U that correpond to bounded directions.
994 static int tab_shift_cone(struct isl_tab
*tab
,
995 struct isl_tab
*tab_cone
, struct isl_mat
*U
)
999 struct isl_basic_set
*bset
= NULL
;
1001 if (tab
&& tab
->n_unbounded
== 0) {
1006 if (!tab
|| !tab_cone
|| !U
)
1008 bset
= isl_tab_peek_bset(tab_cone
);
1009 U
= isl_mat_drop_cols(U
, 0, tab
->n_var
- tab
->n_unbounded
);
1010 for (i
= 0; i
< bset
->n_ineq
; ++i
) {
1012 struct isl_vec
*row
= NULL
;
1013 if (isl_tab_is_equality(tab_cone
, tab_cone
->n_eq
+ i
))
1015 row
= isl_vec_alloc(bset
->ctx
, tab_cone
->n_var
);
1018 isl_seq_cpy(row
->el
, bset
->ineq
[i
] + 1, tab_cone
->n_var
);
1019 row
= isl_vec_mat_product(row
, isl_mat_copy(U
));
1022 vec_sum_of_neg(row
, &v
);
1024 if (isl_int_is_zero(v
))
1026 if (isl_tab_extend_cons(tab
, 1) < 0)
1028 isl_int_add(bset
->ineq
[i
][0], bset
->ineq
[i
][0], v
);
1029 ok
= isl_tab_add_ineq(tab
, bset
->ineq
[i
]) >= 0;
1030 isl_int_sub(bset
->ineq
[i
][0], bset
->ineq
[i
][0], v
);
1044 /* Compute and return an initial basis for the possibly
1045 * unbounded tableau "tab". "tab_cone" is a tableau
1046 * for the corresponding recession cone.
1047 * Additionally, add constraints to "tab" that ensure
1048 * that any rational value for the unbounded directions
1049 * can be rounded up to an integer value.
1051 * If the tableau is bounded, i.e., if the recession cone
1052 * is zero-dimensional, then we just use inital_basis.
1053 * Otherwise, we construct a basis whose first directions
1054 * correspond to equalities, followed by bounded directions,
1055 * i.e., equalities in the recession cone.
1056 * The remaining directions are then unbounded.
1058 int isl_tab_set_initial_basis_with_cone(struct isl_tab
*tab
,
1059 struct isl_tab
*tab_cone
)
1062 struct isl_mat
*cone_eq
;
1063 struct isl_mat
*U
, *Q
;
1065 if (!tab
|| !tab_cone
)
1068 if (tab_cone
->n_col
== tab_cone
->n_dead
) {
1069 tab
->basis
= initial_basis(tab
);
1070 return tab
->basis
? 0 : -1;
1073 eq
= tab_equalities(tab
);
1076 tab
->n_zero
= eq
->n_row
;
1077 cone_eq
= tab_equalities(tab_cone
);
1078 eq
= isl_mat_concat(eq
, cone_eq
);
1081 tab
->n_unbounded
= tab
->n_var
- (eq
->n_row
- tab
->n_zero
);
1082 eq
= isl_mat_left_hermite(eq
, 0, &U
, &Q
);
1086 tab
->basis
= isl_mat_lin_to_aff(Q
);
1087 if (tab_shift_cone(tab
, tab_cone
, U
) < 0)
1094 /* Compute and return a sample point in bset using generalized basis
1095 * reduction. We first check if the input set has a non-trivial
1096 * recession cone. If so, we perform some extra preprocessing in
1097 * sample_with_cone. Otherwise, we directly perform generalized basis
1100 static struct isl_vec
*gbr_sample(struct isl_basic_set
*bset
)
1103 struct isl_basic_set
*cone
;
1105 dim
= isl_basic_set_total_dim(bset
);
1107 cone
= isl_basic_set_recession_cone(isl_basic_set_copy(bset
));
1111 if (cone
->n_eq
< dim
)
1112 return isl_basic_set_sample_with_cone(bset
, cone
);
1114 isl_basic_set_free(cone
);
1115 return sample_bounded(bset
);
1117 isl_basic_set_free(bset
);
1121 static struct isl_vec
*basic_set_sample(struct isl_basic_set
*bset
, int bounded
)
1123 struct isl_ctx
*ctx
;
1129 if (isl_basic_set_plain_is_empty(bset
))
1130 return empty_sample(bset
);
1132 dim
= isl_basic_set_n_dim(bset
);
1133 isl_assert(ctx
, isl_basic_set_n_param(bset
) == 0, goto error
);
1134 isl_assert(ctx
, bset
->n_div
== 0, goto error
);
1136 if (bset
->sample
&& bset
->sample
->size
== 1 + dim
) {
1137 int contains
= isl_basic_set_contains(bset
, bset
->sample
);
1141 struct isl_vec
*sample
= isl_vec_copy(bset
->sample
);
1142 isl_basic_set_free(bset
);
1146 isl_vec_free(bset
->sample
);
1147 bset
->sample
= NULL
;
1150 return sample_eq(bset
, bounded
? isl_basic_set_sample_bounded
1151 : isl_basic_set_sample_vec
);
1153 return zero_sample(bset
);
1155 return interval_sample(bset
);
1157 return bounded
? sample_bounded(bset
) : gbr_sample(bset
);
1159 isl_basic_set_free(bset
);
1163 __isl_give isl_vec
*isl_basic_set_sample_vec(__isl_take isl_basic_set
*bset
)
1165 return basic_set_sample(bset
, 0);
1168 /* Compute an integer sample in "bset", where the caller guarantees
1169 * that "bset" is bounded.
1171 struct isl_vec
*isl_basic_set_sample_bounded(struct isl_basic_set
*bset
)
1173 return basic_set_sample(bset
, 1);
1176 __isl_give isl_basic_set
*isl_basic_set_from_vec(__isl_take isl_vec
*vec
)
1180 struct isl_basic_set
*bset
= NULL
;
1181 struct isl_ctx
*ctx
;
1187 isl_assert(ctx
, vec
->size
!= 0, goto error
);
1189 bset
= isl_basic_set_alloc(ctx
, 0, vec
->size
- 1, 0, vec
->size
- 1, 0);
1192 dim
= isl_basic_set_n_dim(bset
);
1193 for (i
= dim
- 1; i
>= 0; --i
) {
1194 k
= isl_basic_set_alloc_equality(bset
);
1197 isl_seq_clr(bset
->eq
[k
], 1 + dim
);
1198 isl_int_neg(bset
->eq
[k
][0], vec
->el
[1 + i
]);
1199 isl_int_set(bset
->eq
[k
][1 + i
], vec
->el
[0]);
1205 isl_basic_set_free(bset
);
1210 __isl_give isl_basic_map
*isl_basic_map_sample(__isl_take isl_basic_map
*bmap
)
1212 struct isl_basic_set
*bset
;
1213 struct isl_vec
*sample_vec
;
1215 bset
= isl_basic_map_underlying_set(isl_basic_map_copy(bmap
));
1216 sample_vec
= isl_basic_set_sample_vec(bset
);
1219 if (sample_vec
->size
== 0) {
1220 isl_vec_free(sample_vec
);
1221 return isl_basic_map_set_to_empty(bmap
);
1223 isl_vec_free(bmap
->sample
);
1224 bmap
->sample
= isl_vec_copy(sample_vec
);
1225 bset
= isl_basic_set_from_vec(sample_vec
);
1226 return isl_basic_map_overlying_set(bset
, bmap
);
1228 isl_basic_map_free(bmap
);
1232 __isl_give isl_basic_set
*isl_basic_set_sample(__isl_take isl_basic_set
*bset
)
1234 return isl_basic_map_sample(bset
);
1237 __isl_give isl_basic_map
*isl_map_sample(__isl_take isl_map
*map
)
1240 isl_basic_map
*sample
= NULL
;
1245 for (i
= 0; i
< map
->n
; ++i
) {
1246 sample
= isl_basic_map_sample(isl_basic_map_copy(map
->p
[i
]));
1249 if (!ISL_F_ISSET(sample
, ISL_BASIC_MAP_EMPTY
))
1251 isl_basic_map_free(sample
);
1254 sample
= isl_basic_map_empty(isl_map_get_space(map
));
1262 __isl_give isl_basic_set
*isl_set_sample(__isl_take isl_set
*set
)
1264 return (isl_basic_set
*) isl_map_sample((isl_map
*)set
);
1267 __isl_give isl_point
*isl_basic_set_sample_point(__isl_take isl_basic_set
*bset
)
1272 dim
= isl_basic_set_get_space(bset
);
1273 bset
= isl_basic_set_underlying_set(bset
);
1274 vec
= isl_basic_set_sample_vec(bset
);
1276 return isl_point_alloc(dim
, vec
);
1279 __isl_give isl_point
*isl_set_sample_point(__isl_take isl_set
*set
)
1287 for (i
= 0; i
< set
->n
; ++i
) {
1288 pnt
= isl_basic_set_sample_point(isl_basic_set_copy(set
->p
[i
]));
1291 if (!isl_point_is_void(pnt
))
1293 isl_point_free(pnt
);
1296 pnt
= isl_point_void(isl_set_get_space(set
));