Fault-Tolerant Matroid Bases
Abstract
We investigate the problem of constructing fault-tolerant bases in matroids. Given a matroid and a redundancy parameter , a -fault-tolerant basis is a minimum-size set of elements such that, even after the removal of any elements, the remaining subset still spans the entire ground set. Since matroids generalize linear independence across structures such as vector spaces, graphs, and set systems, this problem unifies and extends several fault-tolerant concepts appearing in prior research.
Our main contribution is a fixed-parameter tractable () algorithm for the -fault-tolerant basis problem, parameterized by both and the rank of the matroid. This two-variable parameterization by is shown to be tight in the following sense. On the one hand, the problem is already -hard for . On the other hand, it is -hard for and polynomial-time solvable for .
Keywords and phrases:
Parameterized Complexity, matroids, robust basesFunding:
Matthias Bentert: Supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 819416) and ERC Consolidator grant AdjustNet (agreement No. 864228).Copyright and License:
2012 ACM Subject Classification:
Mathematics of computing Combinatorial algorithms ; Theory of computation Fixed parameter tractabilityFunding:
The research leading to these results have been supported by the Research Council of Norway via the Franco-Norwegian AURORA project (grant no. 349476).Editors:
Anne Benoit, Haim Kaplan, Sebastian Wild, and Grzegorz HermanSeries and Publisher:
Leibniz International Proceedings in Informatics, Schloss Dagstuhl – Leibniz-Zentrum für Informatik
1 Introduction
A basis in a -dimensional vector space is a set of exactly linearly independent vectors . Any vector then has a unique representation However, if certain basis vectors are lost or corrupted – for instance, in a distributed storage system where vectors are stored across multiple servers that can malfunction – it may be challenging or impossible to accurately recover . The algorithmic question we address in this paper is: For an expected number of failures, what is the minimum-size -fault-tolerant set of vectors that still guarantees data reconstructability?
A natural way to capture the notions of basis and independence is through matroids, which generalize linear independence to a variety of combinatorial settings (like graphs, set systems, vector spaces). Let be a matroid, and let be a nonnegative integer. We say that is a -fault-tolerant basis of if is a minimum-size set such that, for every subset of size at most , we have . (All necessary matroid definitions appear in the Preliminaries section.) Equivalently, this condition means that , where denotes matroid closure. Note that this concept generalizes the standard basis definition: a -fault-tolerant basis is simply a basis of . However, while every matroid has a basis, it may not admit a -fault-tolerant basis for . We investigate the following problem.
Fault-Tolerant Basis
| Input: | A matroid and nonnegative integer . |
| Task: | Find a -fault-tolerant basis or correctly decide that none exists. |
Let us give some examples of Fault-Tolerant Basis.
Linear Matroids.
A linear matroid over a field can be represented by a set of vectors in a vector space . The rank of the matroid corresponds to . A basis here is simply a set of linearly independent vectors. A set of vectors is -fault-tolerant if, after removing up to vectors, the remaining set still spans the entire subspace . Such concepts naturally arise in distributed storage – where vectors might be stored on different servers [11] – and in coding theory, where losing some symbols or vectors should not destroy the ability to recover the entire space [13].
Graphic Matroids.
For a connected graph , its graphic matroid has ground set , and a set is independent if it forms a forest. A basis is thus a spanning tree of . In this matroid, a set of edges is -fault-tolerant if, upon removing any edges, the subgraph remains connected. Thus, a -fault-tolerant basis is a -edge connected spanning subgraph with minimum number of edges. This problem is known in the literature as -Edge-Connected Spanning Subgraph [8, 14, 17]. It is motivated by network applications as its task is to compute a minimum-cost sub-network that is resilient against up to link failures (see also [5]).
Gammoids.
A gammoid is derived from a directed (or undirected) graph . Let and be two distinguished sets of vertices of . Within the set , define a subset to be independent if there exist vertex-disjoint paths in originating from vertices in and ending in the vertices of . The basis of the gammoid is the maximum set of vertices from that can be reached by vertex-disjoint paths from . In this case, a vertex set is a -fault-tolerant basis if upon removing any vertices from , the remaining vertices are still linkable from sources in without losing rank. This problem is related to the problem of finding fault-tolerant disjoint paths studied by Adjiashvili et al. [1].
Transversal Matroids.
A transversal matroid is described via a bipartite graph between a ground set and a set of “target positions” . A set is independent if it can be matched injectively to distinct elements in . The basis of the transversal matroid is a maximum-size subset that can be perfectly matched into . Finding a -fault-tolerant basis for a transversal matroids is naturally related to robust matching problems, where one wants to preserve a perfect (or maximum) matching under the loss of a few vertices or edges [12, 18].
1.1 Our Contribution
We investigate the parameterized complexity of Fault-Tolerant Basis under natural parameterizations by and the rank of the input matroid. (We refer to the textbook by Cygan et al. [10] for an introduction to parameterized complexity.) Our main result is that the problem is when parameterized by both and .
Theorem 1.
Fault-Tolerant Basis can be solved in time on -element matroids of rank at most given by independence oracles.
To prove Theorem 1, we design an algorithm that recursively decomposes the input matroid and identifies a small core set containing any possible -fault-tolerant basis, thereby restricting the problem to a bounded search space. More precisely, the algorithm locates a set of important elements with bounded size such that, if the input matroid admits a -fault-tolerant basis, then there is one contained entirely in . The construction of is guided by the observation (see Observation 7) that if is a rank- set with elements such that every -element subset of is independent, then is already a -fault-tolerant basis. Hence, finding such an would immediately solve the problem. Otherwise, we identify an inclusion-maximal rank- set (of bounded size) such that for every -element subset , the rank of is . The crucial insight here is that the closure of can be expressed as the union of over all -element subsets of . We then proceed recursively, selecting elements of by searching for analogous sets in the closures of each , applying the same approach used to choose . Once we have computed , we determine whether a -fault-tolerant basis exists within by enumerating all candidate subsets via a brute-force algorithm. If such a basis is found, it is also a valid solution for the original matroid.
We then show that the result of Theorem 1 is tight. First, by adapting the results of Fomin et al. [15] for our purposes, we observe the following lower bound.
Proposition 2.
It is -hard for the parameterization by to decide whether a given linear matroid has a -fault-tolerant basis.
For the parameterization of Fault-Tolerant Basis by , we remind that Fault-Tolerant Basis for graphic matroids is equivalent to finding a -edge connected spanning subgraph with the minimum number of edges. As was shown by Fernandes [14], an -vertex graph has a 2-edge connected spanning subgraph with at most edges if and only if has a Hamiltonian cycle. Thus, Fault-Tolerant Basis is intractable already for . Taking into account the inapproximability lower bounds for higher connectivities established by Gabow et al. [17], we obtain the following observation.
Observation 3.
For every integer , it is -hard to decide whether a graphic matroid given with an integer has a -fault-tolerant basis of size at most .
For the parameterization by the rank, we establish a dichotomy – for any fixed , Fault-Tolerant Basis is intractable, but for , the problem can be solved in polynomial time. In fact, for , we solve the more general weighted variant of Fault-Tolerant Basis. In Weighted Fault-Tolerant Basis, we are given a matroid together with a weight function , and the task is to find a set of minimum total weight such that for any set of size at most , . Our result is summarized in the following theorem.
Theorem 4.
For any integer , it is -hard to decide whether a linear matroid of rank over rationals given together with integers and has a -fault-tolerant basis of size at most . For , Weighted Fault-Tolerant Basis can be solved in on matroids given by an independence oracle.
In particular, one can find a -fault-tolerant basis of vectors in in polynomial time but the problem becomes -hard in . To obtain the hardness for , we use the result of Froese et al. [16] stating that, given a set of points on the plane and a positive integer , it is NP-hard to decide whether contains at least points in general position. To establish the claim for , it is convenient to show a more general result for partition matroids which may be of independent interest.
Proposition 5.
Given an -element partition matroid with unit capacities together with a weight function and integers , one can find in time either a set of minimum weight such that, for any set of size at most , it holds that or correctly decide that such a set does not exist.
1.2 Related Work
Adjiashvili, Stiller, and Zenklusen [2] introduced a Bulk-Robustness model of combinatorial optimization. They studied several instances of this framework, including the Bulk-Robust Minimum Matroid Basis problem, which is most relevant to our work. In this setting, for a matroid , we are given a collection of interdiction sets where for each . The goal is to find a cheapest subset such that contains a basis of for every . Adjiashvili, Stiller, and Zenklusen provided an -approximation algorithm for Bulk-Robust Minimum Matroid Basis, where is the rank of the matroid. The problem Fault-Tolerant Basis can be viewed as a special case of Bulk-Robust Minimum Matroid Basis when consists of all subsets of of size .
There are several prior works investigating fault-tolerance for classic optimization problems. The model of --Path and --Flow problems with safe and vulnerable edges was introduced by Adjiashvili et al. [1], who studied the approximability of these problems. Subsequently, generalizations were also studied [3, 7, 9]. Bentert et al. [5] studied the parameterized complexity of computing a fault-tolerant spanning tree. Approximation algorithms and inapproximability lower bounds for -Edge-Connected Spanning Subgraph have been considered in [8, 14, 17, 19].
More generally, Fault-Tolerant Basis belongs to robust optimization, a branch of optimization that adapts classic optimization-theoretic tools to settings with uncertainty. For a comprehensive introduction, see the survey by Bertsimas et al. [6].
2 Preliminaries
We refer to the book of Oxley [21] for a detailed introduction to matroids. A pair , where is a finite ground set and is a family of subsets of the ground set, called independent sets of , is a matroid if it satisfies the following conditions, called independence axioms:
-
(I1)
.
-
(I2)
If and then .
-
(I3)
If and , then there is such that .
We use and to denote the ground set and the set of independent sets, respectively. An inclusion-maximal independent set is called a basis of . We use to denote the set of bases of . All the bases of have the same size called the rank of and denoted by . The rank of a subset , denoted by , is the maximum size of an independent set ; the function is the rank function of . A set spans an element if . The closure (or span) of is the set . Closures satisfy the following properties, called closure axioms:
-
(CL1)
For every , .
-
(CL2)
If , then .
-
(CL3)
For every , .
-
(CL4)
For every , every , and every , .
For , the matroid obtained by deleting , denoted by , is the matroid such that and . Let be an integer. The -truncation of a matroid is the matroid with such that if and only if and . A matroid is a partition matroid if there is a partition of the ground set and a -tuple of positive integers, called capacities, such that a set is independent if and only if for every . A -matrix over a field is a representation of a matroid over if there is a one-to-one correspondence between and the columns of such that for any , it holds that if and only if the columns in are linearly independent (as vectors of ). If has such a representation, then has a representation over . A matroid admitting a representation over is said to be linear; a matroid is binary if it has a representation over .
In our algorithms for general matroids, we assume that input matroids are given by independence oracles. Such an oracle for a matroid , takes as input a set and correctly answers in a constant time whether . We note that some matroids could be given explicitly, for example, by their representations.
3 Basic Observations
In this section, we prove a couple of simple results that will be helpful in the later discussion. Recall that for an integer , is a -fault-tolerant basis of a matroid if is a subset of minimum cardinality such that for every subset of size at most . We use the following bounds on the size of a -fault-tolerant basis.
Proposition 6.
Let be a matroid with and let be an integer. Then for a -fault-tolerant basis of , it holds that
Proof.
The lower bound immediately follows from the definition of a -fault-tolerant basis. To show the upper bound, assume for the sake of contradiction that . We iteratively construct sets where is an inclusion-maximal independent set in , and for each , is an inclusion-maximal independent set in . Since and , such sets exist. Set . Consider an arbitrary of size at most . By the pigeonhole principle, there is an index such that . By construction, it holds that . Hence, . Because is a -fault-tolerant basis, . Thus,
Since was chosen arbitrarily, we obtain that is a -fault-tolerant basis of . However, since , this contradicts the choice of .
We next argue that the bounds in Proposition 6 are tight. First, note that the lower bound is tight as the matroid proves. We next argue that the upper bound is also tight. Let be a basis of . Let and be integers such that . Consider the linear matroid with . It is easy to see that any -fault-tolerant basis of has to contain at least vectors of for each . Thus, the size of a -fault-tolerant basis is at least . From the other side, we have that, for , for any of size at most . Thus, is a -fault-tolerant basis of size of .
Let be a matroid. For a positive integer , we say that a set is -uniform if and for every subset of size . The definition and Proposition 6 yield the following.
Observation 7.
Let be a matroid of rank , be an integer, and be a set of size . Then, is a -fault-tolerant basis of if and only if is -uniform.
Proof.
If is a -fault-tolerant basis, then, for every set of size at most , . Since , this implies that, for every set of size , , that is, is -uniform. For the opposite direction, if is -uniform, then, for any of size , . Since , it holds for any of size at most that . This completes the proof.
We conclude this section by proving Proposition 2 using the results of Fomin et al. [15]. In particular, they studied Rank -Reduction. Here, the input is a binary matroid given by its representation over and two positive integers and . The task is to decide whether there is a set of size at most such that .
Proposition 2. [Restated, see original statement.]
It is -hard for the parameterization by to decide whether a given linear matroid has a -fault-tolerant basis.
Proof.
We reduce from Rank -Reduction parameterized by , which is known to be W[1]-hard [15]. Let be an instance of Rank -Reduction where is a binary matroid of rank . We define to be the -truncation of . Notice that is a linear matroid and its representation (over a different field) can be constructed in polynomial time by the result of Lokshtanov et al. [20]. We claim that has no -fault-tolerant basis if and only if is a yes-instance of Rank -Reduction.
To this end, note that has no -fault-tolerant basis if and only if there is a set of size at most such that . Since if and only if , has no -fault-tolerant basis if and only if there is of size at most such that . This concludes the proof.
We remark that since the hardness for Rank -Reduction was proven by Fomin et al. [15] via a polynomial-time reduction from Clique, it is -hard to decide whether a linear matroid has a -fault-tolerant basis. We also note that the problem is in XP since we can decide in time whether an -element matroid has a -fault-tolerant basis – simply check for each subset of size whether .
4 An FPT algorithm for the parameterization by rank and
In this section, we prove Theorem 1. We construct a recursive branching algorithm that finds a set of important elements of bounded size with the property that, if the input matroid has a -fault-tolerant basis, then there is a -fault-tolerant basis . Note that a -fault-tolerant basis has minimum size by definition. Then, we select a -fault-tolerant basis (if it exists) in using brute force. The construction of is inspired by Observation 7, which indicates that -uniform sets are preferable in the construction of -fault-tolerant bases. The following lemma is crucial for constructing .
Lemma 8.
Let be a matroid of rank and be an integer. Let be an -uniform set of size at least for some . Then, for any -fault-tolerant basis of , there is a -fault-tolerant basis such that
-
(i)
and
-
(ii)
.
Proof.
Let be a -fault-tolerant basis of . If , then the claim is straightforward because in this case, and . We can therefore select to be the set of arbitrary elements of by Observation 7. We hence assume from now on that .
Consider a -fault-tolerant basis satisfying (i), that is, , such that the size of is minimum. We claim that satisfies (ii), that is, . Assume towards a contradiction that and there is an element . Let . Note that is not a -fault-tolerant basis of . Hence, there is a set of size at most such that . Notice that implies . Denote by the family of all sets of size at most such that . For each , let be an inclusion-maximal independent set in and let . Note that and therefore for each . By Proposition 6, it holds that . Hence, . We next show that for each .
To this end, assume towards a contradiction that . Since is -uniform, we obtain that . Then and, in particular, . However, in this case, . Since is a -fault-tolerant basis, we have that contradicting .
Recall that and has at least one element outside . Then, and therefore by Proposition 6. Since and, for each , , a simple counting argument shows that there exists an element such that for all . Let . Note that by definition, (i) , (ii) , and (iii) . We next show that is a -fault-tolerant basis. Afterwards, we will show that this contradicts our choice of .
Towards the former, let be of size at most . We prove that . If , then . So we assume from now on that . Then, as shown above. Assume towards a contradiction that . Since , we have that is a subset of of size at most . Since is a -fault-tolerant basis, . However, and , contradicting our assumption that . Hence, and therefore . Recall that . This implies that and . Since by the choice of , . Thus, .
Since for every of size at most and , we have that is a -fault-tolerant basis. However, we also obtain that (i) and (ii) contradicting the choice of . This shows that and completes the proof.
Now we show how to construct a set of important elements of bounded size.
Lemma 9.
There is an algorithm that, given a matroid of rank with for each and an integer , outputs a set of at most elements in time such that has a -fault-tolerant basis if and only if has a -fault-tolerant basis .
Proof.
Let be a matroid of rank such that for any and let be a non-negative integer. We construct a recursive branching algorithm that takes as input a matroid and a non-empty independent set , and outputs a set of size at most with the property that for any -fault-tolerant basis of , there is a -fault-tolerant basis such that
-
(i)
and
-
(ii)
.
Let . The base case is , where we do the following:
-
Compute .
-
If , then set and output it.
-
Otherwise, define to be the set of arbitrary elements of and output it.
For , we do the following:
-
Compute .
-
If , then set , output it, and stop.
-
Find a -uniform set that either has size or is an inclusion-maximal -uniform set of size at most .
-
If , then set and output it.
-
If is an inclusion-maximal -uniform set of size at most , then output , where is the output of .
To compute an -uniform set , we apply the following greedy procedure:
-
Initially, set .
-
While , do the following:
-
–
For every , check whether is -uniform and set if this holds.
-
–
If is not -uniform for all , then output and stop.
-
–
-
If , then output .
The crucial property of the algorithm is given in the following claim whose proof combines Lemma 8 and induction on . Here and further, the proofs of the statements labeled () are omited in this extended abstract and can be found in the full version of the paper [4].
Claim 10 ().
outputs a set with the property that for any -fault-tolerant basis of , there is a -fault-tolerant basis such that
-
(i)
and
-
(ii)
.
It remains to analyze the size of and the running time. We next show an upper bound on the size of of . We prove this via induction on .
For , note that . For , consider any independent set of size . The algorithm outputs the set , which by the induction hypothesis, has size at most . By construction, we have
Observe first that
| (1) |
For the second option, note that
Combining this equation with Equation 1, we obtain the required upper bound for .
Finally, we evaluate the running time of Important and show that runs in time. To this end, notice that can be constructed in polynomial time in the oracle model. To construct the -uniform set of size , we use the greedy procedure where for each for the considered , we go over all subsets of of size and check whether is independent using the oracle. As , the construction of can be done in time. The algorithm makes recursive calls and the depth of the search tree is at most . Summarizing, we obtain that the overall running time is .
To complete the proof and construct , we call for an arbitrary basis of and set for the output set of the algorithm. Note that a basis can be found in polynomial time using the independence oracle. Then Claim 10 implies that if has a -fault-tolerant basis, then it also has one in . Note that the other direction is trivial as . This concludes the proof.
We are now ready to prove Theorem 1, which we restate here for convenience.
Theorem 1. [Restated, see original statement.]
Fault-Tolerant Basis can be solved in time on -element matroids of rank at most given by independence oracles.
Proof.
Let be a matroid of rank and let be an integer. Fault-Tolerant Basis is trivial for and we can assume that . Notice that loops of , that is, elements such that are irrelevant – a loop is not included in any -fault-tolerant basis and for any set . Hence, we can preprocess and delete the loops. From now on, we assume that for every .
We apply the algorithm from Lemma 9, and in time , find a set of size at most such that whenever has a -fault-tolerant basis, has a -fault-tolerant basis . We consider all candidate subsets of with using the upper bound for the size of a -fault-tolerant basis from Proposition 6. This can be done in time. For each candidate set of size at most , we verify whether is a -fault-tolerant basis as follows. In time, we check whether for every subset of size at most . Among all candidate sets satisfying the above property, we select a set of minimum size which is a -fault-tolerant basis of . The overall running time is . This concludes the proof.
5 Partition matroids
In this section, we prove Proposition 5. The proof is based on the following structural lemma.
Lemma 11.
Let be a partition matroid with unit capacities for a partition of the ground set. Let and let and be integers. Then, the following properties hold.
-
(i)
If there is an integer such that and for every , then for any set of size at most , .
-
(ii)
If is an inclusion-minimal set such that for any set of size at most , , then there is an integer such that and for every .
Proof.
To show (i), suppose that and for each for some integer . Consider any subset of size at most . Since for every , it holds that and hence for at least sets . Since is a partition matroid with unit capacities, this is also a lower bound on and therefore
This proves that and for any set of size at most .
To prove (ii), suppose that is an inclusion-minimal set with such that for any set of size at most , holds. We assume without loss of generality that .
If , then we set . Trivially, and for every . We next show that . Assume towards a contradiction that . Any subset of size satisfies for every . Hence, for any set of size at most by (i). This contradicts the the minimality of and shows .
If , then and we set . We will next show that and for every . Let and . By definition of , .
If , then , contradicting the choice of . Thus, and . There exist such that for each , and such that . Consider . By definition, for every . Then, by (i), we have that and for any set of size at most , . By the minimality of , we conclude that . This proves (ii).
We are now ready to prove Proposition 5, which we restate here.
Proposition 5. [Restated, see original statement.]
Given an -element partition matroid with unit capacities together with a weight function and integers , one can find in time either a set of minimum weight such that, for any set of size at most , it holds that or correctly decide that such a set does not exist.
Proof.
Consider a weighted partition matroid with unit capacities and a weight function . Let and be integers. Let also be the partition of defining . If , then the claim of the theorem is trivial as is a solution. Thus, we can assume that .
For an integer , we say that is feasible if there exists a set with such that for every . For each integer , we check whether is feasible. This can be done by checking whether . If there is no feasible integer, then we conclude that there is no with the property that for any subset of size at most by Lemma 11. Otherwise, for every feasible , we greedily choose a set of minimum weight such that and for every . Notice that the selection of such a set is equivalent to finding a minimum weight independent set of size in the partition matroid for with the capacities where for each . By the well-known matroid properties [21], the greedy algorithm finds such a set . By Lemma 11, we conclude that is a minimum weight set such that for any set of size at most , . This concludes the description of our algorithm and its correctness proof.
To evaluate the running time, notice that the elements of can be sorted by their weight in time. Then we have at most choices of and for each , the greedy algorithm works in time. Thus, the overall running time is in , concluding the proof.
As a corollary, we obtain the following for general matroids of rank at most two.
Corollary 12 ().
Weighted Fault-Tolerant Basis can be solved in on matroids of rank at most two given by an independence oracle.
6 Complexity dichotomy for the parameterization by rank
In this section, we prove Theorem 4 which we restate here for convenience.
Theorem 4. [Restated, see original statement.]
For any integer , it is -hard to decide whether a linear matroid of rank over rationals given together with integers and has a -fault-tolerant basis of size at most . For , Weighted Fault-Tolerant Basis can be solved in on matroids given by an independence oracle.
Proof.
The claim for is proven in Corollary 12. Thus, it remains to show the computational lower bound for . We show the claim for and then explain how to extend it for any . We reduce from General Position Subset Selection. Recall that a set of points on the Euclidean plane is said to be in general position if there are no three points on the same line on the plane. General Position Subset Selection is defined as follows. Given a set of point on the plane and an integer , decide whether there is a subset of at least points in general position. The problem was shown to be -hard by Froese et al. [16] and the result holds for points with rational coordinates.
Let be an instance of General Position Subset Selection. We assume without loss of generality that as any set of at most two points is in general position. We set , construct the set of vectors, and define to be the linear matroid with the ground set over rationals.
Note that and that three distinct points , , and of are not on the same line if and only if . This implies that for any , the points of are in general position if and only if is a -uniform set for . Hence, has a subset of at least points in general position if and only if there is a -uniform subset of of size at least . By Proposition 6, a -fault-tolerant basis of has size at least . By Observation 7, has a -fault-tolerant basis of size if and only if is a yes-instance of General Position Subset Selection. This concludes the proof for .
To see the claim for , we perform the same reduction but in dimensions. The constructed vectors have a 0 in all but the first three dimensions (where they have the entries as constructed above). For each , we then add vectors with zero-entries everywhere except for dimension , where the entry is . Note that any -fault-tolerant basis of the linear matroid over the constructed vectors has to contain all newly added vectors. Moreover, it has to contain a set of vectors such that after removing any of them, the first three dimensions have to be spanned by the remaining vectors. This is equivalent to the case and concludes the proof.
7 Conclusion
In this paper, we initiate the study of the parameterized complexity of Fault-Tolerant Basis. Our main result is that the problem is fixed-parameter tractable when parameterized by both and rank of the input matroid. This positive algorithmic result is complemented by computational lower bounds showing -hardness for constant values for any one of the two parameters alone. Our results lead to several open questions.
First, we do not know whether our result from Theorem 1 could be extended for Weighted Fault-Tolerant Basis. Our approach based on the choice of -uniform sets is tailored for the unweighted case, and one may need different techniques in the presence of weights.
Second, is it possible to extend our result to the non-uniform model introduced by Adjiashvili et al. [1]? Here, we assume that the set of elements of a matroid is partitioned into two subsets and of safe and vulnerable elements, respectively. Then, the task is to either find a set of minimum size such that for any sets of size at most , or correctly report that such a set does not exist. Fault-Tolerant Basis is the special case of this problem with .
Third, can our results be extended to the model introduced by Adjiashvili et al. [2] where not arbitrary sets of elements can fail but possible failure scenarios are part of the input?
Finally, observe that our computational lower bounds from Proposition 2, Observation 3, and Theorem 4 do not exclude efficient algorithms for Fault-Tolerant Basis on special classes of matroids. In particular, we proved in Proposition 5 that Weighted Fault-Tolerant Basis can be solved in polynomial time on truncations of partition matroids with unit capacities. While it is straightforward to see that Weighted Fault-Tolerant Basis can be solved in polynomial time for partition matroids with arbitrary capacities,111Given a partition matroid defined by a partition of the ground set and a -tuple of capacities , to solve Weighted Fault-Tolerant Basis, we have to choose elements of minimum weight from each . it is not clear whether the problem can be solved in polynomial time on their truncations. The problem complexity for other fundamental classes of matroids, like transversal matroids and gammoids, is another interesting open question.
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