Extreme Witnesses and Their Applications
Abstract
We study the problem of computing the so called minimum and maximum witnesses for Boolean vector convolution. We also consider a generalization of the problem which is to determine for each positive value at a coordinate of the convolution vector, q smallest (largest) witnesses, where q is the minimum of a parameter k and the number of witnesses for this coordinate. We term this problem the smallest kwitness problem or the largest kwitness problem, respectively. We also study the corresponding smallest and largest kwitness problems for Boolean matrix product. First, we present an \(\tilde{O}(n^{1.5}k^{0.5})\)time algorithm for the smallest or largest kwitness problem for the Boolean convolution of two ndimensional vectors, where the notation \(\tilde{O}(\ )\) suppresses polylogarithmic in n factors. In consequence, we obtain new upper time bounds on reporting positions of mismatches in potential string alignments and on computing restricted cases of the \((\min , +)\) vector convolution. Next, we present a fast (substantially subcubic in n and linear in k) algorithm for the smallest or largest kwitness problem for the Boolean matrix product of two \(n\times n\) Boolean matrices. It yields fast algorithms for reporting k lightest (heaviest) triangles in a vertexweighted graph.
Keywords
Boolean vector convolution Boolean matrix product String matching Witnesses Minimum and maximum witnesses Lightest triangles Time complexity1 Introduction
For a potential alignment of a pattern string with a text string over the same alphabet, a position in the alignment where the pattern symbol is different from the text symbol is a witness to the symbol mismatch while a position where the pattern and text symbol are equal is a witness to the symbol match.
Similarly, if A and B are two \(n\times n\) Boolean matrices and C is their Boolean matrix product then for any entry \(C[i,j]=1\) of C, a witness is an index m such that \(A[i,m]\wedge B[m,j]=1.\) The smallest (or, largest) possible witness is called the minimum witness (or, maximum witness, respectively).
The problems of finding “witnesses” have been extensively studied for several decades, at the beginning independently within stringology and graph algorithms relying on matrix computations. In string matching, witnesses for symbol mismatches or matches in potential alignments of two strings are sought [4, 9, 17] while in graph algorithms, witnesses for the Boolean matrix product are typically sought, originally in order to solve shortest path problems in graphs [2, 3]. In both cases, highly nontrivial efficient algorithmic solutions have been presented [2, 3, 4, 17].
Also in both areas, useful generalizations and/or specializations of the problems of finding witnesses have been studied. A natural generalization introduced for string matching in [17] is to request up to k witnesses instead of a single one. It has been efficiently solved by using concepts from group testing in [4] and conveyed to Boolean matrix product in [4, 14]. A natural specialization is to request minimum or maximum witnesses. This specialization has been introduced and efficiently solved in [10] in the context of finding lowest common ancestors in directed acyclic graphs and it found many other applications since then (cf. [8, 18, 21]).
In analogy to witnesses for Boolean matrix product, if a and b are two ndimensional Boolean vectors and c is their Boolean convolution then for any coordinate \(c_i=1\) of c, a witness is an index l such that \(a_l\wedge b_{il}=1\). In contrast to string matching and Boolean matrix product, the problem of computing the witnesses of Boolean vector convolution does not seem to be explicitly studied in the literature. On the other hand, Boolean vector convolution is very much related to string matching [12], and hence the algorithms for reporting witness or more generally up to k witnesses can be easily conveyed from stringology to Boolean vector convolution (see Proposition 3.1).
In this paper, we study the problem of computing minimum and maximum witnesses for Boolean vector convolution. We also consider a generalization of the problem which is to determine for each positive (value at a)^{1} coordinate of the convolution vector, q smallest (largest) witnesses, where q is the minimum of a parameter k and the number of witnesses for this coordinate. We term this problem the smallest kwitness problem or the largest kwitness problem, respectively. We also study the corresponding generalization for Boolean matrix product.

an \(\tilde{O}(n^{1.5})\)time algorithm for reporting minimum and maximum witnesses for the Boolean convolution of two ndimensional vectors, and more generally, an \(\tilde{O}(n^{1.5}k^{0.5})\)time algorithm for the smallest or largest kwitness problem for the convolution;

as corollaries, \(\tilde{O}(n^{1.5}k^{0.5})\) time bounds for the smallest or largest kwitness problems in string matching;

in part as corollaries, several upper time bounds on computing the \((\min ,+)\) integer vector convolution in restricted cases, summarized in Table 1;

an \(O(n^{2 + \lambda }k)\)time algorithm for the smallest or largest kwitness problem for the Boolean matrix product of two \(n\times n\) Boolean matrices, where \(\lambda \) is a solution to the equation \(\omega (1, \lambda , 1) = 1 + 2 \, \lambda + \log _n k\);

as a corollary, an \(O(n^{2 + \lambda }k)\) time bound for the problem of reporting for each edge of a vertexweighted graph k lightest (heaviest) triangles containing it, where \(\lambda \) satisfies the aforementioned equation; also, an \(O(\min \{n^{\omega }k+n^{2+o(1)}k, n^{2 + \lambda }k\})\) time bound for the problem of reporting k lightest (heaviest) triangles in the input vertexweighted graph.
Our upper time bounds for computing the \((\min ,+)\) convolution of two ndimensional integer vectors either with coordinates having a bounded number of different values, or decomposable into a number of nondecreasing or nonincreasing subsequences, or just monotone subsequences
Vector a/vector b  \(c_b\) dif. values  \(m_b\) nondecr. subs.  \(m_b\) nonincr. subs. 

\(c_a\) different values  \(\tilde{O}(c_ac_bn)\)  \(\tilde{O}(c_am_bn^{1.5})\)  \(\tilde{O}(c_am_bn^{1.5})\) 
\(m_a\) nondecr. subs.  \(\tilde{O}(m_ac_bn^{1.5})\)  \(\tilde{O}(m_am_bn^{1.5})\)  ? 
\(m_a\) nonincr. subs.  \(\tilde{O}(m_ac_bn^{1.5})\)  ?  \(\tilde{O}(m_am_bn^{1.5})\) 
\(m_a\) mon. subs.  \(\tilde{O}(m_ac_bn^{1.5})\)  ?  ? 
arbitrary  \(\tilde{O}(c_bn^{1.844})\)  ?  ? 
2 Preliminaries
For two ndimensional vectors \(a=(a_0,\ldots ,a_{n1})\) and \(b=(b_0,\ldots ,b_{n1})\) over a semiring \((\mathbb {U},\oplus , \odot )\), their convolution over the semiring is a vector \(c=(c_0,\ldots ,c_{2n2})\), where \(c_i=\bigoplus _{l=\max \{in+1,0\}}^{\min \{ i,n1\}}a_l\odot b_{il}\) for \(i=0,\ldots ,2n2.\) Similarly, for a \(p\times q\) matrix A and a \(q\times r\) matrix B over the semiring, their matrix product over the semiring is a \(p\times r\) matrix C such that \(C[i,j]=\bigoplus _{m=1}^{q}A[i,m]\odot B[m,j]\) for \(1\le i\le p\) and \(1\le j\le r.\) In particular, for the semirings \((\mathbb {Z},+,\times ),\) \((\mathbb {Z},\min ,+),\) \((\mathbb {Z},\max ,+),\) and \((\{0,1\},\vee , \wedge )\), we obtain the arithmetic, \((\min ,+),\) \((\max ,+),\) and the Boolean convolutions or matrix products, respectively.
We shall use the unitcost RAM computational model [1] with computer word of length logarithmic in the maximum of the size of the input and the value of the largest input integer.
The following fact is well known (cf. [12]).
Fact 2.1
Let p and q be two ndimensional integer vectors. The arithmetic convolution of p and q can be computed in \(\tilde{O}(n)\) time. Hence, also the Boolean convolution of two ndimensional vectors can be computed in \(\tilde{O}(n)\) time.
For a sequence S of integers, we shall denote the minimum number of monotone subsequences into which S can be decomposed by mon(S).
Fact 2.2
[13, 23] A sequence of n integers can be decomposed into \(O(mon(S)\log n)\) monotone subsequences in \(O(n^{1.5}\log n)\) time.
Fact 2.3
(see Theorem 10 in [5]) The problem of computing the convolution of two ndimensional vectors over a semiring can be reduced to computing \(O(\sqrt{n})\) products of two \(O(\sqrt{n}) \times O(\sqrt{n})\) matrices over the semiring. Importantly, the matrices can be constructed in \(O(n^{1.5})\) time in total and their entries are appropriately filled with the coordinates of the vectors.
Fact 2.4
(Theorem 3.2 in [7]) Let A and B be two \(n\times n\) integer matrices where the entries of A range over at most c different integers. The \((\min ,+)\) matrix product of A and B can be computed in \(O(cn^{2.688})\) time.
Fact 2.5
[11] A lightest (heaviest) triangle in an undirected vertex weighted graph on n vertices can be found in \(O(n^{\omega }+n^{2+o(1)})\) time.
3 Extreme Witnesses for Boolean Convolution
Let \(c=(c_0,\ldots ,c_{2n2})\) be the Boolean convolution of two ndimensional Boolean vectors a and b. A witness of \(c_i=1\) is any \(l\in [ \max \{in+1,0\}, \min \{ i,n1\}]\) such that \(a_l\wedge b_{il}=1.\) A minimum witness (or maximum witness) of \(c_i=1\) is the smallest (or, the largest, respectively) witness of \(c_i.\) The witnesses problem (or minimum witness problem, or maximum witness problem) for the Boolean convolution of two ndimensional Boolean vectors is to determine witnesses (or, the minimum witnesses or the maximum witnesses, respectively) for all nonzero coordinates of the Boolean convolution of the vectors. The kwitness problem (or, the smallest k witness problem or the largest kwitness problem) for the Boolean convolution of two ndimensional Boolean vectors is to determine for each nonzero coordinate of the convolution q witnesses (or, q smallest witnesses or q largest witnesses, respectively), where q is the minimum of k and the number of witnesses for this coordinate.
The Boolean vector convolution is very much related to string matching problems [12]. The corresponding problems of reporting a symbol mismatch or match, or up to k such mismatches or matches for each potential alignments of the pattern with the text have been studied in the so called nonstandard stringology [4, 17]. Also, the focus of this paper is on extreme witnesses. For these reasons and on the other hand, for the completeness sake, we just state a proposition and its generalization on standard witnesses for Boolean vector convolution that can be obtained analogously as the well known corresponding facts on string matching or Boolean matrix product.
Proposition 3.1
(Analogous to [3]) The witnesses problem for Boolean convolution of two ndimensional vectors can be solved in \(\tilde{O}(n)\) time.
Proof
sketch. The witnesses for the Boolean convolution c of two ndimensional vectors a and b can be computed analogously as the witnesses for the Boolean matrix product [3]. The first observation is that for all coordinates of c that have a single witness, their witnesses can be obtained by computing the arithmetic convolution of a with the vector \(b'\) resulting from replacing each 1 in b with the number of the respective coordinate. The next idea is to dilute the other vector b gradually so the number of witnesses for each positive coordinate of c decreases finally to zero but in most cases passing through 1 first. For instance, if \(c_i\) has l witnesses and in each phase each coordinate of b is set to 0 with probability \(\frac{1}{2}\) then after a logarithmic number of such phases there is a positive probability that exactly one witness will remain. By iterating the process a logarithmic number of times witnesses for all positive coordinates of c can be determined with high probability.
In order to remove the randomness, we can use small cwise \(\epsilon \)bias sample spaces analogously as Alon and Naor in their deterministic algorithm for witnesses of Boolean matrix product [3].
The algorithm, its analysis and derandomization are totally analogous to those of the algorithm of Alon and Naor for witnesses of Boolean matrix product [3]. We refer the reader for the technical details to their paper. It is sufficient to replace matrices with vectors, entries with coordinates and Boolean matrix product with Boolean vector convolution in their proof. \(\square \)
Following [4] and [14], one can also generalize Proposition 1 to include an algorithmic solution to the kwitness problem for Boolean convolution of two ndimensional vectors in \(\tilde{O}(nk)\) time.
With a moderate technical effort, the minimum or maximum witness problem for Boolean convolution could be solved by combining the known \(O(n^{2.575})\)time algorithm for the corresponding problem of minimum or maximum witnesses of Boolean matrix product [10] with the known reduction of vector convolution over an arbitrary semiring to matrix product over the semiring described in Fact 2.3 [5]. The combination results in an \(O(n^{1.787})\)time solution to the extreme witness problem for Boolean convolution. We shall show that a substantially more efficient solution can be obtained directly.
Theorem 3.2
The minimum witness problem (maximum witness problem, respectively) for Boolean convolution of two ndimensional vectors can be solved in \(\tilde{O}(n^{1.5})\) time.
Proof
Let a and b be two ndimensional vectors. Let r be an integer parameter between 1 and n. For \(p=1,\ldots ,\lceil n/r \rceil ,\) let \(a^p\) be the Boolean ndimensional vector resulting from setting to zero all coordinates of a with indices not exceeding \((p1)r\) and those with indices greater than pr. We compute, for each \(p=1,\ldots ,\lceil n/r \rceil ,\) the Boolean convolution \(c^p\) of \(a^p\) and b. Next, for each \(i=0,\ldots ,2n2,\) we determine the smallest p such that \(c^p_i=1.\) Then, if such a p exists, we determine the interval of the implicants \(a_l\wedge b_{il}\) of \(c^{p}_i\) that potentially can have a nonzero value, i.e., where \(l\in ((p1)r, pr],\) and return the smallest l in the interval for which \(a_l\wedge b_{il}=1.\) The \(\lceil n/r \rceil \) computations of Boolean convolutions \(c^p\) takes \(\tilde{O}(n^2/r)\) time. The total time taken by the determination of the smallest p is \(O(n\times n/r)\). To determine the smallest l for a given i and p requires examining the value of O(r) implicants and hence it takes O(nr) time in total. By setting \(r=\lceil \sqrt{n} \rceil ,\) we obtain the claimed time complexity. \(\square \)
The method of Theorem 3.2 can be generalized to include the smallest kwitness problem and the largest kwitness problem.
Theorem 3.3
The smallest kwitness problem as well as the largest kwitness problem for Boolean convolution of two ndimensional vectors can be solved in \(\tilde{O}(n^{1.5}k^{0.5})\) time.
Proof
Let a and b be two input ndimensional vectors. Let r be an integer parameter between 1 and n. Analogously as in the proof of Theorem 3.2, for \(p=1,\ldots ,\lceil n/r \rceil ,\) we let \(a^p\) denote the Boolean ndimensional vector resulting from setting to zero all coordinates of a with indices not exceeding \((p1)r\) and those with indices greater than pr. Next, we compute for each \(p=1,\ldots ,\lceil n/r \rceil ,\) the arithmetic convolution \(w^p\) of \(a^l\) and b by interpreting these vectors as \(01\) ones. The arithmetic convolutions provide us with the number of witnesses in each interval \(((p1)r,pr]\) for each coordinate \(c_i\) of the Boolean convolution c of a and b. Their coordinatewise sum provides us with the total number of witnesses for each coordinate of c. In order to solve the smallest kwitness problem, for \(p=1,\ldots ,\lceil n/r \rceil ,\) and for \(i=0,\ldots ,2n2,\) whenever \(w^p_i>0\) and the number of witnesses for \(c_i\) found so far is less than the minimum of k and the number of witnesses of \(c_i,\) we search through the interval \(((p1)r,pr]\) from the left to the right for further witnesses. For details see the algorithm depicted in Fig. 1. In the worst case, for each \(i=0,\ldots ,2n2,\) we need to search through k of such intervals. The total cost of the searches becomes \(O(n\times \frac{n}{r} +n\times k\times r),\) see lines 15–19 in the algorithm depicted in Fig. 1. On the other hand, the \(\lceil n/r \rceil \) computations of the arithmetic convolutions \(w^p\) takes \(\tilde{O}(n^2/r)\) time. By setting \(r=\lceil \sqrt{\frac{n}{k}} \rceil ,\) we obtain the claimed time complexity for the smallest kwitness problem.
The largest kwitness problem can be solved analogously in the same asymptotic time by considering the intervals in the opposite order and searching them from the right to the left instead. \(\square \)
3.1 String Matching
Fisher and Patterson showed already in 1974 [12] that several string matching problems can be efficiently reduced to Boolean vector convolution.
Suppose we are given two strings \(\tau =\tau _{m1}\tau _{m2}...\tau _{0}\) and \(\rho =\rho _0\rho _1...\rho _{n1},\) where \(m<n,\) over a finite alphabet \(\varSigma .\) Following [12], for \(\gamma \in \varSigma ,\) let \(H_{\gamma }(\ )\) be a function from \(\varSigma \) to \(\{\) true, false \(\}\) such that \(H_{\gamma }(x )=true\) if and only if \(x=\gamma .\) If \(i+m\le n,\) the question of whether \(\tau _{m1}\tau _{m2}...\tau _{0}\) matches \(\rho _i\rho _{i+1}...\rho _{i+m1}\) is equivalent to a conjunction of the negations of terms \(\bigvee ^{m1}_{l=0} H_{\alpha }(\rho _{i+l})\wedge H_{\beta }(\tau _{m1l}),\) where \(\alpha ,\beta \in \varSigma \) and \(\alpha \ne \beta .\) Note that whenever such a term is true, the matching cannot take place as at some position \(\alpha \) clashes with \(\beta .\) In this way, the standard string matching problem for \(\tau \) and \(\rho \) easily reduces to \(O(\varSigma ^2)\) Boolean convolutions of two Boolean vectors of length at most n.
Observe now that witnesses for the aforementioned Boolean convolutions yield positions of the clashes, in other words, symbol mismatches. If we modify the terms to \(\bigvee ^{m1}_{l=0} H_{\alpha }(\rho _{i+l})\wedge H_{\alpha }(\tau _{m1l}),\) for \(\alpha \in \varSigma ,\) the witnesses for the \(O(\varSigma )\) Boolean convolutions yield positions of two sided matches with \(\alpha \in \varSigma .\) Hence, we obtain the following theorem as a corollary from Theorem 3.3.
Theorem 3.4
Consider the string matching problem for a text string of length n and a pattern string of length \(m<n,\) both over a finite alphabet. For each alignment of the pattern with the text, we can provide locations of the k earliest symbol mismatches and/or the k earliest symbol matches as well as locations of the k latest symbol mismatches and the k latest symbol matches in the alignments in \(\tilde{O}(n^{1.5}k^{0.5})\) time in total. In particular, we can also provide positions of the earliest and/or latest twoside symbol matches with a given alphabet symbol (cf. ones problem in [17]) in the alignments in \(\tilde{O}(n^{1.5}k^{0.5})\) time in total.
3.2 \((\min , +)\) Convolution
The correctness of the algorithm depicted in Fig. 2 relies on the following straightforward lemma.
Lemma 3.5
In the algorithm depicted in Fig. 2, the following equivalence holds: \(d_k\ne 0\) in line 13 if and only if \(\min \{a_l+b_ml+m=k \wedge a_l\in a^i \wedge b_m\in b^j \}\) is equal to the first argument of the minimum in this line.
Theorem 3.6
Let a and b be two ndimensional integer vectors such that the coordinates of a range over at most \(c_a\) different values while the sequence of the consecutive coordinates of b can be decomposed into \(m_b\) monotone subsequences. The algorithm depicted in Fig. 2 computes their \((\min ,+)\) convolution in \(\tilde{O}(c_am_bn^{1.5})\) steps.
Proof
The decomposition of the vector a into \(c_a\) constant subsequences in line 1 trivially takes O(n) time. Next, the decomposition of the vector b into \(\tilde{O}(m_a)\) monotone subsequences in line 5 takes \(O(n^{1.5}\log n)\) time by Fact 2.2. The forming of the vectors \(char(a^i)\) in lines 2–3 and \(char(b^j)\) in lines 6–7 take \(\tilde{O}(c_an+m_bn)\) time in total. The \(\tilde{O}(c_am_b)\) computations of the minimum and maximum witnesses of the Boolean convolution d in lines 9–11 take \(\tilde{O}(c_am_bn^{1.5})\) time in total by Theorem 3.2. Finally, the line 13 is executed \(\tilde{O}(c_am_bn)\) times. The bound \(\tilde{O}(c_am_bn^{1.5})\) follows.
If we are given decompositions of the two input ndimensional vectors a and b into monotone subsequences that are either all nondecreasing or all nonincreasing then we can use the algorithm depicted in Fig. 3 which is analogous to that depicted in Fig. 2, in order to compute the \((\min ,+)\) convolution of a and b. Thus, first for each subsequence \(a^i\) of a and each subsequence \(b^j\) of b, we compute the Boolean vectors \(char(a^i)\) and \(char(b^j)\) indicating with ones the coordinates of a or b covered by \(a^i\) or \(b^j,\) respectively. Next, depending if the subsequences are nondecreasing or nonincreasing, for each pair of such subsequences \(a^i\) and \(b^j\), we compute the minimum witnesses of the Boolean convolution of \(char(a^i)\) and \(char(b^j)\) or the maximum witnesses of this convolution, respectively. We use the extreme witnesses to update the current coordinates of the computed \((\min ,+)\) convolution analogously as in the algorithm depicted in Fig. 2. Hence, we obtain the following theorem.
Theorem 3.7
Let a and b be two ndimensional integer vectors given with the decompositions of the sequences of their consecutive coordinates into \(m_a\) and \(m_b\) monotone subsequences respectively such that all the subsequences are either nondecreasing or nonincreasing. The algorithm depicted in Fig. 3 computes the \((\min ,+)\) convolution of a and b in \(\tilde{O}(m_am_bn^{1.5})\) time.
Proof
The proof of the correctness of the algorithm depicted in Fig. 3 is analogous to that of the correctness of the algorithm depicted in Fig. 2. The time complexity analysis of the former algorithm is also similar to that of the latter algorithm. The main difference is that the decompositions of a and b into subsequences are given and that the \(O(n^{1.5})\)time algorithm for minimum or maximum witnesses of Boolean convolution is run \(m_am_b\) times instead of \(c_am_b\) times. \(\square \)
By combining Fact 2.3 with Fact 2.4, we also obtain the following bound.
Theorem 3.8
Let a and b be two ndimensional integer vectors such that the coordinates of a range over at most \(c_a\) different values. The \((\min ,+)\) convolution of a and b can be computed in \(\tilde{O}(c_an^{1.844})\) time.
We can also consider the problem of computing the \((\min ,+)\) integer vector convolution of the input vectors a and b, when their coordinates range over \(c_a\) and \(c_b\) different integers, respectively. We can use the algorithm depicted in Fig. 4, analogous to that depicted in Fig. 2. The first difference is that the subsequences \(b^j\) on the side of b are also constant. It follows that for any pair of such constant subsequences \(a^i\) and \(b^j,\) the value of the sum of any element from \(a^i\) with any element from \(b^j\) is constant and it can be trivially computed as \(a^i_1+b^j_1\) a priori. For this reason, it is sufficient to compute the Boolean convolution d of \(char(a^i)\) and \(char(b^j)\) for each pair \(a^i\) and \(b^j\). Then, for any nonzero coordinate of d, we need to update the corresponding coordinate of the computed \((\min ,+)\) convolution of a and b by taking the minimum of the coordinate and \(a^i_1+b^j_1.\) By Fact 2.1, we obtain the following theorem.
Theorem 3.9
Let a and b be two ndimensional integer vectors such that their coordinates range over at most \(c_a\) or \(c_b\) different values, respectively. The algorithm depicted in Fig. 4 computes the \((\min ,+)\) convolution of a and b in \(\tilde{O}(c_ac_bn)\) time.
4 Extreme Witnesses for Boolean Matrix Product
For two \(n\times n\) Boolean matrices A and B, a witness of a C[i, j] entry of the Boolean matrix product of A and B is any index m such that \(A[i,m]\wedge B[m,j]=1.\) Next, the minimum witness and maximum witness for an entry of C as well as the witness problem, the minimum and maximum witness problems, the kwitness problem, and the smallest kwitness and largest kwitness problems for Boolean matrix product of A and B are defined analogously as those for Boolean vector convolution.
In this section, we shall present a generalization of the algorithm for minimum and maximum witnesses for Boolean matrix product from [10] to include the smallest and largest kwitness problems.
Let \(\ell \) be a positive integer smaller than n. We may assume w.l.o.g. that n is divisible by \(\ell \). Partition the matrix A into \(n \times \ell \) submatrices \(A_p\) and the matrix B into \(\ell \times n\) submatrices \(B_p\), such that \(1 \le p \le n/\ell \), and the submatrix \(A_p\) covers the columns \((p1) \, \ell + 1\) through \(p \, \ell \) of A whereas the submatrix \(B_p\) covers the rows \((p1) \, \ell + 1\) through \(p \, \ell \) of B.
For \(p = 1, \ldots , n/\ell \), let \(W_p\) be the arithmetic product of \(A_p\) and \(B_p\) treated as \(01\) matrices. On the other hand, let C denote the Boolean matrix product of A and B. Then, \(W_p[i,j] =q\) if and only if there are exactly q witnesses of C[i, j] in the interval \(((p1) \, \ell , p \, \ell ]\). Consequently, the total number of witnesses of C[i, j] is given by \(\sum _{p=1}^{n/\ell } W_p[i,j]\). Therefore, the following lemma follows.
Lemma 4.1
Suppose that a C[i, j] entry of the Boolean product C of A and B is positive. Let q be the minimum of k and the total number of witnesses of C[i, j]. Next, let \(p'\) be the minimum value of p such that \(\sum _{u=1}^{p} W_u[i,j]\) is not less than q. The smallest q witnesses of C[i, j] belong to the interval \([1, p' \, \ell ]\).
By this lemma, after computing all the matrix products \(W_p = A_p \cdot B_p\), \(1 \le p\le n/\ell \), we need \(O(n/\ell + k\ell )\) time per positive entry of C to find up to k smallest witnesses: \(O(n/\ell )\) time to determine \(p'\) and then \(O(k\ell )\) time to locate the up to k smallest witnesses. See Fig. 5 for our algorithm for the smallest kwitness problem.
Theorem 4.2
Let \(\lambda \) be such that \(\omega (1, \lambda , 1) = 1 + 2 \, \lambda + \log _n k\). The smallest kwitness problem as well as the largest kwitness problem for the Boolean matrix product of two \(n \times n\) Boolean matrices can be solved in \(O(n^{2 + \lambda }k)\) time.
Le Gall has recently substantially improved upper time bounds on rectangular matrix multiplication in [16]. In consequence, he could show that for the equation \(\omega (1, \mu , 1) = 1 + 2 \, \mu \), \(\mu < 0.5302.\) This in particular improves the upper time bound for the minimum and maximum witness problems from \(O(n^{2.575})\) to \(O(n^{2.5302})\). It follows that for \(k\gg 1,\) \(\lambda \) in Theorem 4.2 is substantially smaller than 0.5302.
4.1 Lightest Triangles
By generalizing the reduction of the problem of reporting for each edge of a vertexweighted graph a heaviest triangle containing it to the maximum witness problem for Boolean matrix product from [21] to include reporting k heaviest triangles and the largest kwitness problem, we obtain the following theorem as a corollary from Theorem 4.2.
Theorem 4.3
Let G be an undirected vertex weighted graph on n vertices and let k be a natural number not exceeding n. Next, let \(\lambda \) be such that \(\omega (1, \lambda , 1) = 1 + 2 \, \lambda + \log _n k\). We can list for each edge \(\{u,v\}\) of G, \(q_e\) lightest (heaviest) triangles \(\{u,v,w\}\) in G, where \(q_e\) is the minimum of k and the number of triangles \(\{u,v,w\}\) in G, in \(O(n^{2 + \lambda }k)\) time.
Proof
Number the vertices of G in nondecreasing vertexweight order. Next, solve the smallest (largest) kwitness problem for the Boolean matrix product C of the adjacency matrix of G with itself. For each edge \(e=\{i,j\}\) of G, the up to k smallest (or, largest) witnesses of C[i, j] yield the \(q_e\) lightest (or, heaviest, respectively) triangles in G including e. Theorem 4.2 yields the claimed upper bound. \(\square \)
As for the problem of finding k lightest (heaviest) triangles in a vertexweighted graph, iterating the \(O(n^{\omega }+n^{2+o(1)})\)time algorithm for finding a lightest or heaviest triangle described in Fact 2.5 seems to be a better choice for up to moderate values of k. Before each next iteration, we remove the three vertices of the lastly reported triangle. After k iterations, we stop and find among the reported triangles and no more than \(3(k1)n^2\) other triangles incident to the removed vertices, the k lightest (heaviest) triangles if possible. The method takes \(O(n^{\omega }k+n^{2+o(1)}k+n^2k),\) i.e., \(O(n^{\omega }k+n^{2+o(1)}k)\) time.
Theorem 4.4
Let G be an undirected vertex weighted graph on n vertices and let k be a natural number not exceeding n. Next, let \(\lambda \) be such that \(\omega (1, \lambda , 1) = 1 + 2 \, \lambda + \log _n k\). We can list q lightest (heaviest) triangles in G, where q is the minimum of k and the number of triangles in G, in \(O(\min \{n^{\omega }k+n^{2+o(1)}k, n^{2 + \lambda }k\})\) time.
Finding or detecting triangles of extreme weight in vertexweighted graphs has a number of applications . First of all, it can be used to solve the corresponding general problem of finding or detecting subgraphs or induced subgraphs of extreme weigh [11, 20, 21]. Vassilevska and Williams list also two other applications in [19]: a general variant of the 3SUM problem and a general buyerseller problem in computational economy.
5 Final Remarks
It is an interesting open problem if any of our upper time bounds on minimum and maximum witnesses for Boolean vector convolution and the extreme kwitness problems both for Boolean vector convolution and Boolean matrix product can be substantially improved? Note here that so far the \(O(n^{2+\lambda })\) time bound (where \(\omega (1, \lambda , 1) = 1 + 2\lambda \)) on minimum and maximum witnesses of Boolean matrix product established one decade ago [10] couldn’t be improved (see also [8]).
The problems of Boolean vector convolution and Boolean matrix product seem to be similar but there are some substantial differences between them. The former problem admits almost a linear in the input size algorithm while for the latter problem the current upper time bound is substantially nonlinear [15, 22]. There is a moderately efficient reduction of vector convolution to matrix product described in Fact 2.3 while such a reverse reduction is not known. Our upper time bounds for minimum and maximum witnesses of Boolean vector convolution show that a direct approach to Boolean vector convolution can yield better upper time bounds than those obtained by conveying known upper time bounds for the witness problems for Boolean matrix product via Fact 2.3 to those corresponding for Boolean vector convolution.
The extreme kwitness problems for Boolean matrix product presumably admit several other applications often corresponding to generalizations of the applications for minimum and maximum witnesses of Boolean matrix product [18, 21] and/or the applications of the kwitness problem for Boolean matrix product [4], e.g., the allpairs kbottleneck paths.
Finally, a potentially interesting direction for further research is to consider approximation variants of the extreme witnesses problems and the \((\min , +)\) vector convolution.
Footnotes
 1.
For brevity, we shall identify the ith coordinate of a vector v with its value \(v_i\) in the continuation.
Notes
Acknowledgements
We thank Mirosław Kowaluk and anonymous reviewers of the preliminary version of this paper for valuable comments. This research has been supported in part by Swedish Research Council Grant 62120116179.
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