**Open Journal of Discrete Mathematics** Vol.3 No.3(2013), Article ID:33846,11 pages DOI:10.4236/ojdm.2013.33020

The Poset Cover Problem

^{1}Department of Computer Science, Virginia Tech, Blacksburg, USA

^{2}Core Quantative Strategies, Goldman Sachs, India Pvt. Ltd., Bangalore, India

Email: heath@vt.edu, ajitnema@gmail.com

Copyright © 2013 Lenwood S. Heath, Ajit Kumar Nema. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received March 1, 2013; revised April 1, 2013; accepted May 15, 2013

**Keywords:** Linear Orders; Partial Orders; NP-Completeness; Algorithms

ABSTRACT

A partial order or poset on a (finite) base set determines the set of linear extensions of. The problem of computing, for a poset, the cardinality of is #P-complete. A set of posets on covers the set of linear orders that is the union of the. Given linear orders on, the Poset Cover problem is to determine the smallest number of posets that cover. Here, we show that the decision version of this problem is NP-complete. On the positive side, we explore the use of cover relations for finding posets that cover a set of linear orders and present a polynomial-time algorithm to find a partial poset cover.

1. Introduction

Finite partial orders or posets have numerous applications, including scheduling [1-8], molecular evolution [9-12], data mining [13-17], graph theory [18-23], and algebra [24-27]. Many applications implicitly or explicitly involve linear extensions of posets. For example, the solution of many scheduling problems requires a linearization of the jobs being scheduled consistent with some precedence constraints given by a poset. As the number of linear extensions of a poset may be exponential in the number of elements of the base set, many computational problems related to linear extensions are not solvable in polynomial time. Ruskey [28], West [29], Pruesse and Ruskey [30], Canfield and Williamson [31], Korsh and LaFollette [32], and Ono and Nakano [33] provide algorithms to generate all of the linear extensions of a finite poset. As the size of a solution may be exponentially large, these algorithms emphasize the ability to generate each successive linear extension in polynomial time, at least on average. Sampling the linear extensions of a poset is easier. Bubley and Dyer [34] use a rapidly mixing Markov chain to generate a random linear extension of a finite poset, sampled almost uniformly.

Problems in mining order information from databases of sequences (see, e.g., [16,17,35,36]) have an inverted character from that of many computational problems involving posets. Here, a problem instance is a set of linear orders of items from some universal set, while a solution is one or more posets that well explain, through their linear extensions, a significant number of the linear orders. An example from computational neuroscience [37] might go as follows. Each item is the firing of a neuron, while each linear order is a sequence of neuronal firings, ordered in time from an experiment. The solution is a neural circuit that explains a set of such linear orders. These novel problems are ripe for mathematical formalization and study. In this paper, we define and study one such problem. A problem instance is a set of permutations of a base set, and a solution covers the instance with linear extensions (Section 2). We prove that the Poset Cover problem (a decision problem) is NP-complete in Section 3. In Section 4, we explore how cover relations relate to poset covers. Finally, we develop a polynomial-time algorithm to find a partial cover in Section 5.

2. Preliminaries

In this section, we establish terminology and notation and prove some basic results.

A partial order or poset is an irreflexive, antisymmetric, and transitive binary relation defined on a finite set of cardinality. We write as the ordered pair. Equivalently, poset is a transitive directed acyclic graph (DAG), namely,

. If G is a DAG, then its transitive closure is a poset by this equivalence. The rank function is given by

. The empty poset is.

Let be distinct. Then and are comparable in, written, if or, while x and are incomparable, written, otherwise. Moreover, x is covered by y or y covers x, written, if and there is no such that. In this case, the ordered pair is a cover relation for. It is well-known that a (finite) poset is uniquely determined by its set of cover relations (see [38]).

If and are posets on the same set, then is an extension of, written, if, for all implies. The relation on posets of is reflexive, antisymmetric, and transitive.

A linear order on X is a poset L such that, for, either or holds. If is a linear order, then the rank function is a bijection. Setting, can be written as the sequence, which is a permutation of. Also, we write for the element of rank in. A linear extension of a poset is a linear order such that. The set of all linear extensions of is. Note that is the set of all linear orders on. Brightwell and Winkler [39] prove that the problem of determining for a poset is #P-complete.

Let be a set of posets on. This set covers the set of linear orders

A poset is maximal in if and there is no poset of such that, and. Let be a set of posets on, and let be a set of linear orders on. blankets if

Lemma 1 Let be the set of linear orders that is covered by a set of posets of cardinality. Then, there exists a cover of cardinality that also covers such that every poset in is maximal in.

Proof. We construct by examining each poset in. Let. If is maximal in, then add to. Otherwise, let be a poset of minimum cardinality (as a set of ordered pairs) such that and. Since is not maximal,. Moreover, any poset contained in of smaller cardinality will have. Add to.

The constructed has cardinality. Moreover, also covers and every poset in is maximal in. The lemma follows.

In this paper, we are interested in reversing the cover relationship by addressing the problem of finding a minimum set of posets that covers a given set of linear orders. As a decision problem, this is Poset Cover INSTANCE: A base set of cardinality; a nonempty set of linear orders over; and an integer.

QUESTION: Is there a set of posets on of cardinality that covers?

This problem is shown to be NP-complete in Section 3.

Let be a linear order on. For each satisfying, the i-swap of is the linear order.

Let. Evidently, , so the

-swap relation is symmetric, written. For pairs that are -swaps of each other, for some, we define the function. Note that the set differences of and, namely

and, each consist of a single ordered pair. In this case, the swap pair for and is the unordered pair

; otherwise,

. Two linear orders and differ by a swap, written, if, for some. Since if and only if, the

relation is also symmetric. If, and, then we write to mean that for some, where the elements swapped are and.

Let be a set of linear orders on. The swap graph of is the undirected graph

. An edge of

is labeled. Let be a poset on, and let. Then, is a partial cover of including if and. The swap graph is the same as the adjacent transposition graph of Pruesse and Ruskey [30]. The swap graph of is bipartite, since every edge connects an even permutation to an odd permutation. Moreover, the swap graph of the linear extensions of a single poset is connected (see [30]).

Let be the set of all posets on. Let be a set of ordered pairs. The up-set of is

is empty if and only if the directed graph contains cycles. Let

be a set of unordered pairs. The down-set of is

, and we always have the empty poset.

If, then define the minimal element in to be

The following properties of follow directly from the definitions.

Lemma 2 and

.

We have the following properties of up-sets and downsets.

Lemma 3 Let. If, then

. Let

. If, then

.

Proof. Suppose that. By the definition of upsets,

Now, suppose that. Then,

by the definition of down-sets.

3. NP-Completeness of Poset Cover

In this section, we show that PosetCover is NP-complete, in the process using the following known NP-complete decision problem [40].

Cubic Vertex Cover INSTANCE: A nonempty undirected graph that is cubic, that is, in which every vertex has degree 3; and an integer.

QUESTION: Is there a subset of cardinality such that every edge in is incident on at least one vertex in?

Theorem 4 Poset Cover is NP-complete.

Proof. We show that Poset Cover is in NP and that Cubic Vertex Cover reduces to Poset Cover in polynomial time.

We first show that Poset Cover is in NP. Let, , and constitute an instance of Poset Cover, and let be a set of posets on. First, it is easy to check whether in time polynomial in and; if, then return No. Second, if the cardinality check succeeds, check whether covers as follows. For each poset in turn, use the Korsh and LaFollette [32] algorithm to generate all the linear extensions of, one at a time, in constant time per linear extension. As each linear extension is generated, check whether. If not, then return No. If so, then mark that element of Covered. Note that the number of linear orders generated by a run of the Korsh and LaFollette algorithm before completion or returning No is at most. Hence, the worst-case time for one run of the algorithm, including the checking, is. Once all the posets and their linear extensions are processed, check whether every element of is marked Covered. If so, then return Yes; otherwise, return No. We find that the worstcase time to check whether covers is , since. This is polynomial in the size of the original instance. We conclude that Poset Cover is in NP.

Now, let and constitute an instance of Cubic Vertex Cover . Without loss of generality, assume that and that. Let , and let be an arbitrary labeling of the edges of. As a running example of our reduction, we provide the cubic graph in Figure 1, with vertices and edges. To complete the instance of Cubic Vertex Cover , set.

Let, and let be a base set of elements. Let, the base order, be the linear order on specified by

We view the elements of as consisting of adjacent, non-overlapping pairs. Specifically, the pairs are and, where. All elements of are obtained by a small set of swaps of such pairs, applied to.

Figure 1. A cubic graph as part of an instance of cubic vertex cover.

The first s pairs correspond to the s edges in a natural way. In particular, edge is associated with the edge order. Continuing the example, we set,

and, for example,

For each vertex, there are three edges incident on; let the indices of those edges be, and. For each pair and of these edges, we define the pair edge order to be

For the running example, there are 18 pair edge orders. For each triple, and, we define the triple edge order to be

For the running example, there are 6 triple edge orders.

The primary orders are the base, edge, pair edge, and triple edge orders. For primary pair edge order, there is a corresponding secondary pair edge order obtained by swapping and, which is

For primary triple edge order, there is a corresponding secondary triple edge order obtained by swapping and, which is

For the running example, there are 18 secondary pair edge orders and 6 secondary triple edge orders.

Collect the various orders into five sets, as follows:

We can now complete our instance of Poset Cover by setting

and setting the integer parameter. Note that. For the running example, and.

It remains to show that an instance of Cubic Vertex Cover is a Yes instance if and only if the corresponding instance of Poset Cover is a Yes instance.

Fix an instance and of Cubic Vertex Cover . Let, and constitute the corresponding instance of Poset Cover, as constructed above. By Lemma 1, we may assume that every element of a cover of is maximal in. Since the elements of must be blanketed by any cover, we may assume that the set

is a subset of. Similarly, since the elements of must be blanketed by any cover, we may assume that the set

is a subset of. Note that and that blankets.

First, assume that is a vertex cover of of cardinality at most. Define

Note that and that, by previous observations, it suffices to demonstrate that blankets and. Since is nonempty, , and. Therefore, is blanketed by each of the posets in corresponding to a vertex. For an edge, there is a incident on. Then, is blanketed by the poset

in, and hence every linear order in is blanketed by. We conclude that covers, as desired.

Now, assume that is a cover of of cardinality at most. By previous observations, we must have, for some set of cardinality at most. Since covers, must blanket and. Let be any edge of, incident on vertices and. Without loss of generality, we may assume that. There are two maximal posets that blanket and

. One of these posets must be in.

Moreover, we may assume that contains only orders of this form, since each such order blankets, and the only other orders for to blanket are the’s. Define

Because blankets all of the’s, we conclude that is a vertex cover of G of cardinality , as desired.

The theorem follows.

4. Cover Relations

In this section, we examine properties of cover relations in linear orders and their consequences for poset covers.

Let be a poset, thought of as a transitive DAG. Then, a topological sort of yields an order on such that implies. Assume that is not a linear order. Then there exist such that. There exists at least one topological sort of in which appears to the left of, and there exists at least one topological sort of in which appears to the left of. (This follows from alternate choices available to the depth-first search used to construct a topological sort. See [41].) Select a topological sort that makes and, where. In that case, we obtain a proper extension of in which by adding to the DAG and taking the transitive closure. Moreover, we have, since the existence of such that contradicts. We have just demonstrated the following.

Lemma 5 Let be a poset, and let satisfy. Let

Then is a poset, , and.

Theorem 6 Let be a poset that is not a linear order, and let satisfy. Then there exists a proper extension of such that.

Proof. First, suppose that there exists a that is incomparable to. By Lemma 5, there exists a poset such that and. Moreover, , so, by the definition of.

Second, the case of there being a that is incomparable to is handled analogously.

Finally, we have the case that no element is incomparable either to or to. Let be such that and. (Such a pair must exist, since is not a linear order.) If either and or and, then adding to the DAG for and taking the transitive closure gives us the desired poset. The case and (or vice versa) is impossible, since and. There are no other cases, since.

The theorem follows.

Theorem 7 Let be a poset, and let satisfy. Then there exists a linear order such that and. Moreover, for every linear extension of in which, there exists a unique linear extension

of such that and.

Proof. By Lemma 5, there exists a poset such that and. By applying Theorem 6 iteratively to, we ultimately obtain a linear order that is an extension of (and hence of) such that.

Now, let be a linear extension of in which. Let; then. Let

. Let

. Then is a poset on such that and such that and are incomparable in. Moreover, , so is a linear extension of in which.

The theorem follows.

Theorem 8 Let be a set of linear orders on. Let be an element of. Let satisfy. Let be a partial cover of including. If, then and are comparable in and.

Proof. Suppose that. First assume that and are comparable in. Then it must be true that, since covers in. For the same reason, there is no such that. Hence,.

It remains to show that and are comparable in. To obtain a contradiction, assume that and are incomparable in. By Theorem 7, there exists a unique linear extension of such that and. Necessarily,. Since but, we have a contradiction to the fact that is a partial cover of including. The contradiction establishes that and are comparable in. The theorem follows.

We next characterize a set of linear orders that is covered by a single poset. The ordered pair is a cover relation for if there exists an and an such that, , and. If is a cover relation for, then any poset P that partially covers including must satisfy. An cover sequence of length for is a sequence such that is a cover relation for, for. If there is an cover sequence for, then any poset that covers must satisfy.

Theorem 9 A set of linear orders is the set of linear extensions of a single poset if and only if, for every for which, exactly one of the following holds: 1) for some; 2) there is an cover sequence for; or 3) there is a cover sequence for.

Proof. For one direction, assume that there exists a poset such that is the set of linear extensions of. Let satisfy.

First, suppose. By Theorem 7, there exists a linear extension of for which and another linear extension of for which and. Then, 1) holds. Neither 2) nor 3) holds, since those imply that and are comparable in.

Now suppose that. (The case is symmetric). Then 1) does not hold, since that implies that. Also 3) does not hold, since that implies that. To demonstrate 2), it remains to construct an cover sequence for. The first case is. Then, by repeated application of Theorem 6, there exists a linear extension of such that. Let. Then,. Hence, is an cover sequence for. More generally, we can write for some such that for. Then is also an cover sequence for.

For the other direction, assume that, for every for which, exactly one of 1), 2), or 3) holds. Take to be the poset generated by all the ordered pairs such that is a cover sequence for. We need to show that equals the set of linear extensions of. There are two cases to consider for each linear order. Let.

Case 1.

. To obtain a contradiction, assume that is not a linear extension of. Then there exist and such that. Let satisfy. Then, is an cover sequence for and hence 2) holds for and, but not 1) or 3). Let. Since 1) does not hold, we have. But then is a cover sequence for, a contradiction to the fact that 3) does not hold. In this case, we conclude that is a linear extension of.

Case 2.

. Without loss of generality, we may assume that there exist and such that and . Since, we have that is a cover sequence for. Hence, and cannot be a linear extension of.

We conclude that is precisely the set of linear extensions of.

The theorem follows.

5. A Partial Cover Algorithm

In this section, we present an algorithm for finding a poset that is a partial cover with a maximal set of linear extensions.

5.1. Some Intuition

Intuition for designing an algorithm to find a partial poset cover for a set of linear orders is developed first. It suffices to take a single and identify a single poset that is a partial cover of including. Observe that is such a poset but is not satisfactory if we can construct a poset that covers more of. We use the swap graph to direct construction of a more satisfactory.

During the process of constructing, we maintain a specification for a set of posets, each of which covers a constructed set. We also maintain a set consisting of linear orders, including, that have already been chosen to be covered by the final constructed poset. This specification consists of two kinds of information: some relations and some relations. These relations must be consistent, that is, there must be at least one poset that satisfies them all. A bit more formally, the relations can be specified by a set of ordered pairs, while the relations can be specified by a set of unordered pairs. The specified set of posets is then.

will be maintained to satisfy the following property. Let be arbitrary, and let be any cover relation of. Let. If, then we require that. The rational for this requirement is that every poset that covers and does not cover satisfies the relation. As a side effect, every for which can be eliminated from further consideration for inclusion in.

will be maintained to satisfy the following property. Again, let be arbitrary, and let be any cover relation of. Let. If, then we require that. The rational for this requirement is that every poset that covers both and satisfies the relation. As a side effect, every for which the adjacency is not in can be eliminated from further consideration for inclusion in.

We will need these definitions. Let be distinct, and let be a linear order. The -interchange of L is the linear order that is the same as except a and b have been exchanged. Let be a sequence of linear orders such that, for, so that the sequence is a path in. Let be a subset of

. is -labeled if, for

. A path

in is the -mirror path of if, for, is the -interchange of.

5.2. The Algorithm

Figure 2 contains pseudocode for the algorithm PartialCover. It works by adding linear orders from to one at a time, while maintaining the required properties for A and B. The subroutine Trim in Figure 3 is used to ensure that the required property for is maintained. The addition of a linear order to (Step 9) can add at most one new unordered pair to B (Step 10).

We illustrate the algorithm with the example having

and. Figure 4 contains the swap graph.

The call to Trim in Step 5 finds that is not in, so any linear orders in for which 4 is less than 3 should be deleted. In this case, there is no such linear order in. After Step 6, and.

The first time that Step 8 is executed in Partial-Cover, and. (There are three choices for. This is just one of them.) Then

(Step 9) and (Step 10). The call to Trim in Step 11 finds that is not in. The resulting cover relation is not new, so remains. The while loop from Steps 13 to 21 has only the swap pair to work with. Linear order 32154 is missing its swap partner, 31254. Hence, 32154 is deleted from, which is now

The second time that Step 8 is executed, and L_{2} = 23145. Then (Step 9) and (Step 10). The call to Trim in Step 11 finds that 23415 is not in. The resulting cover relation is new, so A is extended to . None of the linear orders in has 4 less than 1, so the call to Trim does not change. The while loop from Steps 13 to 21 now has the swap pair to work with. Linear order is missing its swap partner,. Hence, is deleted from, which is now

The third time that Step 8 is executed, and. Then

(Step 9) and

(Step 10). The call to Trim in Step 11 finds that is not in. The resulting cover relation is new, so is extended to

. None of the linear orders in has 5 less than 3, so the call to Trim does not change. The while loop from Steps 13 to 21 now has the swap pair to work with. Linear orders 32145, 31245, and 13245 are missing their swap part-

Figure 2. Pseudocode for partial-cover.

Figure 3. Pseudocode for trim.

Figure 4. Swap graph for example.

ners. Hence, they are deleted from, which is now

The fourth time that Step 8 is executed, and. Then

(Step 9) and

(Step 10). The call to Trim in Step 11 finds that and are not in. The resulting cover relations are and, so is extended to. None of the linear orders in has 3 less than 2, so the call to Trim does not change. The while loop from Steps 13 to 21 has no new swap pairs to work with. Hence, there are no further linear orders to delete from, which remains

The fifth and last time that Step 8 is executed, and. Then

(Step 9) and

(Step 10). The call to Trim in Step 11 finds that and are not in. The resulting cover relations are, which is not new, and, which is new, so is extended to. None of the linear orders in has 3 less than 2 or 5 less than 1, so the call to Trim does not change. The while loop from Steps 13 to 21 has no new swap pairs to work with. Hence, there are no further linear orders to delete from, which remains

At this point,.

The resulting poset has the cover relations in, namely, , and. The set of linear extensions of is exactly the final value of, namely, .

5.3. Proof of Correctness

We assume that the following loop invariants hold each time that the test at the top of the while loop body (Step 7) is executed.

1).

2) is a connected graph, and is a connected graph.

3) The directed graph contains no cycles.

4) Every element of is a linear extension of, that is,.

5) The set equals the set of ordered pairs

for which there exists such that

.

6) and consequently .

7) The set equals the set of unordered pairs such that and such that there exist satisfying.

8) Let be a -labeled path of linear orders such that and and such that is a shortest -labeled path from to. Let be the -mirror path for. Then, all of the 's are in, and either all of the’s are in or none of them are.

Together, these invariants suffice to demonstrate the correctness of Partial-Cover, culminating in Theorem 11.

Every execution of subroutine Trim enforces Invariant 5.

Invariant 2 is guaranteed by Steps 6 and 22 and by the way that linear orders are selected for addition to (Step 8).

Invariants 1 and 2 guarantee that, whenever Step 8 is reached, there is a suitable pair to select.

The fact that is guaranteed through the initialization in Step 3 and the fact that any change to always selects a subset of.

Initialization. After initialization (Steps 2 through 6), all invariants are true for the first execution of Step 7, for the following reasons. We have and. The execution of Trim (Step 5) ensures that Invariants 4 and 5 hold, while maintaining (Invariant 1). Step 6 guarantees Invariant 2. Invariant 3 holds because the only order relations in are cover relations in. The fact that makes Invariants 6, 7, and 8 true vacuously.

Execution of the loop body. The fact that requires that the algorithm never deletes an element of from in Step 19 or in Trim. That fact also implies, since is initially put in (Step 2) and could only be deleted in Step 19 or in Trim.

The algorithm never deletes an element of from in Trim. To obtain a contradiction, assume that is deleted in Step 12 of Trim and that it is the first element of deleted. The deletion of is caused by a sequence such that, for, and. For each satisfying, there exists an

such that, and. There is a path in from to that does not contain an edge with swap pair, since contradicts. Consequently, and are in the same order in and in, which implies that. Taken together, these relations imply that, a contradiction to. We conclude that is, in fact, not deleted in Trim.

The algorithm never deletes an element of from in Step 19. The deletion of an element depends on the swap pairs in. More particularly, such a deletion would require a -labeled path in labeled from from to some that has a swap pair from that goes to a linear order outside. This cannot happen because of Invariant 8. We conclude that is, in fact, not deleted in Step 19.

Invariant 3 is maintained because the existence of a cycle in A implies that A and B are inconsistent.

Invariants 4 and 5 are maintained by Trim.

The consistency of A and B (Invariant 6) is maintained by Trim and the loop at Steps 15 through 21.

Invariant 7 is maintained by the way that elements are added to B (Step 10).

It remains to show that Invariant 8 holds; this is demonstrated in the following lemma.

Lemma 10 Each time that Step 8 is about to be executed, Invariant 8 holds.

Proof. Let be a -labeled path of linear orders such that, , and and such that is a shortest -labeled path from to. Let be the -mirror path for.

To obtain a contradiction, assume that there is some that is not in. Let be the first such. Then, so. Let. Since, cannot be in, since it would have been deleted in a previous iteration due to the swap pair being in. This is a contradiction. We conclude that all of the’s are in.

We next show that is not just a shortest - labeled path but is also a shortest path in. Let

. For any path from

to in, every must be the swap pair for some edge in the path. Consequently,. Moreover, there is a path in that uses swap pairs only from and each only once, so the length of every shortest path from to is. (Think about the swaps done by bubble sort; these give one such shortest path.) Since is a shortest - labeled path from to, it must be a -labeled path having. Note that, therefore, no swap pair occurs more than once in.

We next show that is a -labeled path. Since contains no swap pair more than once and since and, if there is a swap pair labeling an edge of, we must also have the swap pair labeling another edge of, and vice versa. More succinctly, if and only if. Let, for some satisfying

. If, then

. If, then

, which is in by the argument above. Similarly, if, then

, which is in by the argument above. We conclude that is a -labeled path and hence a -labeled path.

Finally, we show that either all of the’s are in or none of them are. To obtain a contradiction, suppose that and that, for some satisfying. (The case and will yield a contradiction by an analogous argument.) But, in this case, would have been deleted from in an earlier iteration, a contradiction. From this contradiction, we conclude that either all of the’s are in or none of them are.

Hence, Invariant 8 holds.

The correctness and time complexity of the algorithm are now in view.

Theorem 11 Algorithm Partial-Cover returns a set such that is a partial cover of including. The algorithm has time complexity

.

Proof. The correctness of the algorithm follows from the prior discussion of the loop invariants.

For the time complexity, we first note that

and.

We examine the subroutine Trim. Trim is executed once in Step 5; once for each addition to (Step 11; times in total); and once for each deletion in Step 19 (Step 20; times in total). Hence, Trim is executed times. The loop in Steps 5 through 9 requires time for one execution. The time complexity to test coverage in Step 11 requires time. Hence, the loop in Steps 10 through 13 requires time for one execution. The while loop is executed times, since each additional iteration of the loop because requires the reduction of the cardinality of by at least one. We conclude that the total time spent in trim is

.

It is easy to check that the complexity bound for all calls to Trim dominates the time complexity of the algorithm. Hence, the time complexity of Partial-Cover is

, as required.

REFERENCES

- B. S. Baker and E. G. Coffman, “Mutual Exclusion Scheduling,” Theoretical Computer Science, Vol. 162, No. 2, 1996, pp. 225-243. doi:10.1016/0304-3975(96)00031-X
- P. Barcia and J. O. Cerdeira, “The k-Track Assignment Problem on Partial Orders,” Journal of Scheduling, Vol. 8, No. 2, 2005, pp. 135-143. doi:10.1007/s10951-005-6363-6
- F. A. Chudak and D. B. Shmoys, “Approximation Algorithms for Precedence-Constrained Scheduling Problems on Parallel Machines That Run at Different Speeds,” Journal of Algorithms, Vol. 30, No. 2, 1999, pp. 323-343. doi:10.1006/jagm.1998.0987
- J. R. Correa and A. S. Schulz, “Single-Machine Scheduling with Precedence Constraints,” Mathematics of Operations Research, Vol. 30, No. 4, 2005, pp. 1005-1021. doi:10.1287/moor.1050.0158
- T. Kis, “Job-Shop Scheduling with Processing Alternatives,” European Journal of Operational Research, Vol. 151, No. 2, 2003, pp. 307-332. doi:10.1016/S0377-2217(02)00828-7
- M. Peter and G. Wambach, “n-Extendible Posets, and How to Minimize Total Weighted Completion Time,” Discrete Applied Mathematics, Vol. 99, No. 1-3, 2000, pp. 157- 167. doi:10.1016/S0166-218X(99)00131-6
- N. Policella, A. Oddi, S. F. Smith and A. Cesta, “Generating Robust Partial Order Schedules,” Proceedings of Principles and Practice of Constraint Programming, Vol. 3258, 2004, pp. 496-511.
- D. B. Shmoys, C. Stein and J. Wein, “Improved Approximation Algorithms for Shop Scheduling Problems,” SIAM Journal on Computing, Vol. 23, No. 3, 1994, pp. 617- 632. doi:10.1137/S009753979222676X
- C. Grasso and C. Lee, “Combining Partial Order Alignment and Progressive Multiple Sequence Alignment Increases Alignment Speed and Scalability to Very Large Alignment Problems,” Bioinformatics, Vol. 20, No. 10, 2004, pp. 1546-1556. doi:10.1093/bioinformatics/bth126
- S. K. Kannan and T. J. Warnow, “Tree Reconstruction from Partial Orders,” SIAM Journal on Computing, Vol. 24, No. 3, 1995, pp. 511-519. doi:10.1137/S0097539793252195
- S. Karlin and I. Ladunga, “Comparisons of Eukaryotic Genomic Sequences,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 91, No. 26, 1994, pp. 12832-12836. doi:10.1073/pnas.91.26.12832
- K. Miyakawa and H. Narushima, “Lattice-Theoretic Properties of MPR-Posets in Phylogeny,” Discrete Applied Mathematics, Vol. 134, No. 1-3, 2004, pp. 169-192. doi:10.1016/S0166-218X(03)00303-2
- P. L. Hammer, A. Kogan and M. A. Lejeune, “Modeling Country Risk Ratings Using Partial Orders,” European Journal of Operational Research, Vol. 175, No. 2, 2006, pp. 836-859. doi:10.1016/j.ejor.2005.06.040
- S. Holland and W. Kiessling, “User Preference Mining Techniques for Personalized Applications,” Wirtschaftsinformatik, Vol. 46, No. 6, 2004, pp. 439-445. doi:10.1007/BF03250961
- S. Holland, M. Ester and W. Kiessling, “Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications,” Proceedings of Knowledge Discovery in Databases: PKDD 2003, Vol. 2838, 2003, pp. 204-216.
- H. Mannila, H. Toivonen and A. I. Verkamo, “Discovery of Frequent Episodes in Event Sequences,” Data Mining and Knowledge Discovery, Vol. 1, No. 3, 1997, pp. 259- 289. doi:10.1023/A:1009748302351
- J. Pei, H. X. Wang, J. Liu, K. Wang, J. Y. Wang, and P. S. Yu, “Discovering Frequent Closed Partial Orders from Strings,” IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 11, 2006, pp. 1467-1481. doi:10.1109/TKDE.2006.172
- B. Bollobas and G. Brightwell, “The Structure of Random Graph Orders,” SIAM Journal on Discrete Mathematics, Vol. 10, No. 2, 1997, pp. 318-335. doi:10.1137/S0895480194281215
- S. Felsner and K. Reuter, “The Linear Extension Diameter of a Poset,” SIAM Journal on Discrete Mathematics, Vol. 12, No. 3, 1999, pp. 360-373. doi:10.1137/S0895480197326139
- S. Felsner and W. T. Trotter, “Posets and Planar Graphs,” Journal of Graph Theory, Vol. 49, No. 4, 2005, pp. 273- 284. doi:10.1002/jgt.20081
- P. C. Fishburn, P. J. Tanenbaum and A. N. Trenk, “Linear Discrepancy and Bandwidth,” Order, Vol. 18, No. 3, 2001, pp. 237-245. doi:10.1023/A:1012267732204
- L. S. Heath and S. V. Pemmaraju, “Stack and Queue Layouts of Posets,” SIAM Journal on Discrete Mathematics, Vol. 10, No. 4, 1997, pp. 599-625. doi:10.1137/S0895480193252380
- M. Naatz, “The Graph of Linear Extensions Revisited,” SIAM Journal on Discrete Mathematics, Vol. 13, No. 3, 2000, pp. 354-369. doi:10.1137/S0895480199352609
- G. Agnarsson, S. Felsner and W. T. Trotter, “The Maximum Number of Edges in a Graph of Bounded Dimension, with Applications to Ring Theory,” Discrete Mathematics, Vol. 201, No. 1-3, 1999, pp. 5-19. doi:10.1016/S0012-365X(98)00309-4
- M. Aguiar and W. F. Santos, “Galois Connections for Incidence Hopf Algebras of Partially Ordered Sets,” Advances in Mathematics, Vol. 151, No. 1, 2000, pp. 71-100. doi:10.1006/aima.1999.1864
- N. Bergeron and F. Sottile, “Hopf Algebras and EdgeLabeled Posets,” Journal of Algebra, Vol. 216, No. 2, 1999, pp. 641-651. doi:10.1006/jabr.1998.7794
- J. Konieczny, “Reduced Idempotents in the Semigroup of Boolean Matrices,” Journal of Symbolic Computation, Vol. 20, No. 4, 1995, pp. 471-482. doi:10.1006/jsco.1995.1059
- F. Ruskey, “Generating Linear Extensions of Posets by Transpositions,” Journal of Combinatorial Theory Series B, Vol. 54, No. 1, 1992, pp. 77-101. doi:10.1016/0095-8956(92)90067-8
- D. B. West, “Generating Linear Extensions by Adjacent Transpositions,” Journal of Combinatorial Theory Series B, Vol. 58, No. 1, 1993, pp. 58-64. doi:10.1006/jctb.1993.1031
- G. Pruesse and F. Ruskey, “Generating Linear Extensions Fast,” SIAM Journal on Computing, Vol. 23, No. 2, 1994, pp. 373-386. doi:10.1137/S0097539791202647
- E. R. Canfield and S. G. Williamson, “A Loop-Free Algorithm for Generating the Linear Extensions of a Poset,” Order, Vol. 12, No. 1, 1995, pp. 57-75.
- J. F. Korsh and P. S. LaFollette, “Loopless Generation of Linear Extensions of a Poset,” Order, Vol. 19, No. 2, 2002, pp. 115-126. doi:10.1023/A:1016548222238
- A. Ono and S. Nakano, “Constant Time Generation of Linear Extensions,” Proceedings of Fundamentals of Computational Theory, Vol. 3623, 2005, pp. 445-453.
- R. Bubley and M. Dyer, “Faster Random Generation of Linear Extensions,” Discrete Mathematics, Vol. 201, No. 1-3, 1999, pp. 81-88. doi:10.1016/S0012-365X(98)00333-1
- P. L. Fernandez, L. S. Heath, N. Ramakrishnan, M. Tan and J. P. C. Vergara, “Mining Posets from Linear Orders,” Discrete Mathematics, Algorithms and Applications, 2013, in press.
- P. L. Fernandez, L. S. Heath, N. Ramakrishnan and J. P. C. Vergara, “Reconstructing Partial Orders from Linear Extensions,” Proceedings of the Fourth SIGKDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data, Philadelphia, 20 August 2006, p. 4.
- A. K. Lee and M. A. Wilson, “A Combinatorial Method for Analyzing Sequential Firing Patterns Involving an Arbitrary Number of Neurons Based on Relative Time Order,” Journal of Neurophysiology, Vol. 92, No. 4, 2004, pp. 2555-2573. doi:10.1152/jn.01030.2003
- B. A. Davey and H. A. Priestley, “Introduction to Lattices and Order,” 2nd Edition, Cambridge University Press, Cambridge, 2002. doi:10.1017/CBO9780511809088
- G. Brightwell and P. Winkler, “Counting Linear Extensions,” Order, Vol. 8, No. 3, 1991, pp. 225-242. doi:10.1007/BF00383444
- M. R. Garey, D. S. Johnson and L. Stockmeyer, “Some Simplified NP-Complete Graph problems,” Theoretical Computer Science, Vol. 1, No. 3, 1976, pp. 237-267. doi:10.1016/0304-3975(76)90059-1
- T. M. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms,” 2nd Edition, The MIT Press, Cambridge, 2001.