The \(k\) -Strong Conflict-Free ( \(k\) -SCF colouring problem seeks to find a colouring of the vertices of a hypergraph \(H\) using minimum number of colours so that in every hyperedge \(e\) of \(H\) , there are at least \(\min\{|e|,k\}\) vertices whose colours are different from that of all other vertices in \(e\) . In the case of interval hypergraphs, we present an exact \({\mathsf{P}}\) -time algorithm for the \(k\) -SCF problem thus solving an open problem posed in 2014. We achieve our results by showing that for any hypergraph, a \(k\) -SCF colouring is a proper colouring of a related simple graph which we refer to as a co-occurrence graph . We then show that a co-occurrence graph is obtained by identifying an induced subgraph of a second simple graph that we introduce, which we refer to as the conflict graph . For interval hypergraphs, we show that each co-occurrence graph and the conflict graph are perfect graphs. This property plays a crucial role in our polynomial time algorithm. Second, we show that for an interval hypergraph, the \(1\) -SCF colouring number is the minimum partition of its intervals into sets such that each set has an exact hitting set (a hitting set in which each interval is hit exactly once).

Erdős and West (Discrete Mathematics’85) considered the class of n vertex intersection graphs which have a d-dimensional t-representation (also called a t,d −intersection representation), that is, each vertex of a graph in the class has an associated set consisting of at most t d -dimensional axis-parallel boxes. In particular, for a graph G and for each d ≥ 1, they consider i d ( G ) to be the minimum t for which G has such a representation. For fixed t and d , they consider the class of n vertex labeled graphs for which i d ( G ) ≤ t , and prove an upper bound of $\left( {2nt + \frac{1}{2}} \right)d\log n - \left( {n - \frac{1}{2}} \right)d\log (4\pi t)$ on the logarithm of size of the class. In this work, for fixed t and d we consider the class of n vertex unlabeled graphs which have a d -dimensional t -representation, denoted by ${\mathcal{G}_{t,d}}$ . We address the problem of designing a succinct data structure for the class ${\mathcal{G}_{t,d}}$ in an attempt to generalize the relatively recent results on succinct data structures for interval graphs (Algorithmica’21). Let ${\mathcal{G}_t}{\text{and}}{\mathcal{G}_d}$ be the class of graphs with bounded interval number and bounded boxicity obtained by setting d = 1 and t = 1 in ${\mathcal{G}_{t,d}}$ , respectively. We have the following results:

We describe a new algorithm for vertex cover with runtime $O^*(1.25284^k)$, where $k$ is the size of the desired solution and $O^*$ hides polynomial factors in the input size. This improves over previous runtime of $O^*(1.2738^k)$ due to Chen, Kanj, & Xia (2010) standing for more than a decade. The key to our algorithm is to use a potential function which simultaneously tracks $k$ as well as the optimal value $λ$ of the vertex cover LP relaxation. This approach also allows us to make use of prior algorithms for Maximum Independent Set in bounded-degree graphs and Above-Guarantee Vertex Cover. The main step in the algorithm is to branch on high-degree vertices, while ensuring that both $k$ and $μ= k - λ$ are decreased at each step. There can be local obstructions in the graph that prevent $μ$ from decreasing in this process; we develop a number of novel branching steps to handle these situations.

Capacitated Vehicle Routing Problem (CVRP) is an important combinatorial optimization problem, which is also NP-hard. A wide array of heuristics have been proposed in the literature to obtain an approximate solution to CVRP. To improve the execution time, parallel methods have been developed for accelerating metaheuristics-based algorithms, genetic algorithms, and evolutionary algorithms for CVRP. Despite these advances, our experiments with the state-of-the-art parallel solutions indicate that their run times are too high to be practically useful. The combinatorial explosion is so high that the execution time is prohibitively large even on mid-sized CVRP instances having a few hundred customers. In this work, we propose a novel technique which combines local search and randomization for solving CVRP faster with reasonable accuracy, even on large problem instances. Our usage of randomization enables searching a large space of candidate solutions. We experimentally compare our proposed method with the state-of-the-art GPU implementations on diverse input instances and demonstrate the efficacy of our approach. Our sequential and shared-memory parallel implementations are on an average 36--1189× faster than the state-of-the-art GPU-parallel genetic algorithms while also achieving a superior solution quality. Furthermore, our reported solutions are close to the current best-known solutions from CVRPLIB.

We consider the problem of designing space-efficient data structures for unlabelled path graphs with$n$vertices while supporting basic navigational queries such as degree, adjacency, and neighborhood queries efficiently. We provide two solutions for this problem. Our first data structure is succinct and occupies$n\log n+o(n\log n)$bits while answering adjacency query in$O(\log n)$time, and neighborhood and degree queries in$O(d\log^{2}n)$time where$d$is the degree of the queried vertex. Our second data structure answers all these queries faster at the expense of slightly more space. More specifically, it consumes$O(n\log^{2}n)$bits while answering adjacency and degree queries in constant time and neighborhood query in$O(d\log n)$time. Central to our data structures is the usage of the classical heavy path decomposition, followed by a careful bookkeeping using an orthogonal range search data structure among others, which may be of independent interest for designing succinct data structures for other graphs.

We study the Budgeted Dominating Set (BDS) problem on uncertain graphs, namely, graphs with a probability distribution p associated with the edges, such that an edge e exists in the graph with probability p(e). The input to the problem consists of a vertex-weighted uncertain graph 𝒢 = (V, E, p, ω) and an integer budget (or solution size) k, and the objective is to compute a vertex set S of size k that maximizes the expected total domination (or total weight) of vertices in the closed neighborhood of S. We refer to the problem as the Probabilistic Budgeted Dominating Set (PBDS) problem. In this article, we present the following results on the complexity of the PBDS problem. 1) We show that the PBDS problem is NP-complete even when restricted to uncertain trees of diameter at most four. This is in sharp contrast with the well-known fact that the BDS problem is solvable in polynomial time in trees. We further show that PBDS is 𝖶[1]-hard for the budget parameter k, and under the Exponential time hypothesis it cannot be solved in n^o(k) time. 2) We show that if one is willing to settle for (1-ε) approximation, then there exists a PTAS for PBDS on trees. Moreover, for the scenario of uniform edge-probabilities, the problem can be solved optimally in polynomial time. 3) We consider the parameterized complexity of the PBDS problem, and show that Uni-PBDS (where all edge probabilities are identical) is 𝖶[1]-hard for the parameter pathwidth. On the other hand, we show that it is FPT in the combined parameters of the budget k and the treewidth. 4) Finally, we extend some of our parameterized results to planar and apex-minor-free graphs. Our first hardness proof (Thm. 1) makes use of the new problem of k-Subset Σ-Π Maximization (k-SPM), which we believe is of independent interest. We prove its NP-hardness by a reduction from the well-known k-SUM problem, presenting a close relationship between the two problems.

Ridesharing is an effective, affordable and environment-friendly way of transportation. This paper proposes a Hybrid Genetic Algorithm(HGA) using elements from Simulated Annealing(SA) and Local search(LS) which is very suitable for Ridesharing related applications. The designed algorithm efficiently handles advanced constraints like timing windows for pick-up, detour time(or distance), and waiting-time minimization.

Given a hypergraph H, the conflict-free colouring problem is to colour vertices of H using minimum colours so that in every hyperedge e of H, there is a vertex whose colour is different from that of all other vertices in e. Our results are on a variant of the conflict-free colouring problem considered by Cheilaris et al.[Cheilaris et al., 2014], known as the 1-Strong Conflict-Free (1-SCF) colouring problem, for which they presented a polynomial time 2-approximation algorithm for interval hypergraphs. We show that an optimum 1-SCF colouring for interval hypergraphs can be computed in polynomial time. Our results are obtained by considering a different view of conflict-free colouring which we believe could be useful in general. For interval hypergraphs, this different view brings a connection to the theory of perfect graphs which is useful in coming up with an LP formulation to select the vertices that could be coloured to obtain an optimum conflict-free colouring. The perfect graph connection again plays a crucial role in finding a minimum colouring for the vertices selected by the LP formulation.

Finding low-cost spanning subgraphs with given degree and connectivity requirements is a fundamental problem in the area of network design. We consider the problem of finding d-regular spanning subgraphs (or d-factors) of minimum weight with connectivity requirements. For the case of k-edge-connectedness, we present approximation algorithms that achieve constant approximation ratios for all d≥2⋅⌈k/2⌉. For the case of k-vertex-connectedness, we achieve constant approximation ratios for d≥2k−1. Our algorithms also work for arbitrary degree sequences if the minimum degree is at least 2⋅⌈k/2⌉ (for k-edge-connectivity) or 2k−1 (for k-vertex-connectivity). To complement our approximation algorithms, we prove that the problem with simple connectivity cannot be approximated better than the traveling salesman problem. In particular, the problem is A P X-hard.

We study the complexity of the Hitting Set problem in set systems (hypergraphs) that avoid certain sub-structures. In particular, we characterize the classical and parameterized complexity of the problem when the Vapnik-Chervonenkis dimension (VC-dimension) of the input is small. VC-dimension is a natural measure of complexity of set systems. Several tractable instances of Hitting Set with a geometric or graph-theoretical flavor are known to have low VC-dimension. In set systems of bounded VC-dimension, Hitting Set is known to admit efficient and almost optimal approximation algorithms (Brönnimann and Goodrich, 1995; Even, Rawitz, and Shahar, 2005; Agarwal and Pan, 2014). In contrast to these approximation-results, a low VC-dimension does not necessarily imply tractability in the parameterized sense. In fact, we show that Hitting Set is W[1]-hard already on inputs with VC-dimension 2, even if the VC-dimension of the dual set system is also 2. Thus, Hitting Set is very unlikely to be fixed-parameter tractable even in this arguably simple case. This answers an open question raised by King in 2010. For set systems whose (primal or dual) VC-dimension is 1, we show that Hitting Set is solvable in polynomial time. To bridge the gap in complexity between the classes of inputs with VC-dimension 1 and 2, we use a measure that is more fine-grained than VC-dimension. In terms of this measure, we identify a sharp threshold where the complexity of Hitting Set transitions from polynomial-time-solvable to NP-hard. The tractable class that lies just under the threshold is a generalization of Edge Cover, and thus extends the domain of polynomial-time tractability of Hitting Set.

Given an n-vertex graph G and a function f:V(G) -> {0, ..., n-1}, an f-factor is a subgraph H of G such that deg_H(v)=f(v) for every vertex v in V(G); we say that H is a connected f-factor if, in addition, the subgraph H is connected. A classical result of Tutte (1954) is the polynomial time algorithm to check whether a given graph has a specified f-factor. However, checking for the presence of a connected f-factor is easily seen to generalize Hamiltonian Cycle and hence is NP-complete. In fact, the Connected f-Factor problem remains NP-complete even when f(v) is at least n^epsilon for each vertex v and epsilon<1; on the other side of the spectrum, the problem was known to be polynomial-time solvable when f(v) is at least n/3 for every vertex v. In this paper, we extend this line of work and obtain new complexity results based on restricting the function f. In particular, we show that when f(v) is required to be at least n/(log n)^c, the problem can be solved in quasi-polynomial time in general and in randomized polynomial time if c <= 1. We also show that when c>1, the problem is NP-intermediate.

We investigate the parameterized complexity of Vertex Cover parameterized by the difference between the size of the optimal solution and the value of the linear programming (LP) relaxation of the problem. By carefully analyzing the change in the LP value in the branching steps, we argue that combining previously known preprocessing rules with the most straightforward branching algorithm yields an O *(2.618 k ) algorithm for the problem. Here, k is the excess of the vertex cover size over the LP optimum, and we write O *( f ( k )) for a time complexity of the form O ( f ( k ) n O (1) ). We proceed to show that a more sophisticated branching algorithm achieves a running time of O *(2.3146 k ). Following this, using previously known as well as new reductions, we give O *(2.3146 k ) algorithms for the parameterized versions of Above Guarantee Vertex Cover , Odd Cycle Transversal , Split Vertex Deletion, and Almost 2-SAT , and O *(1.5214 k ) algorithms for König Vertex Deletion and Vertex Cover parameterized by the size of the smallest odd cycle transversal and König vertex deletion set. These algorithms significantly improve the best known bounds for these problems. The most notable improvement among these is the new bound for Odd Cycle Transversal —this is the first algorithm that improves on the dependence on k of the seminal O *(3 k ) algorithm of Reed, Smith, and Vetta. Finally, using our algorithm, we obtain a kernel for the standard parameterization of Vertex Cover with at most 2 k − c log k vertices. Our kernel is simpler than previously known kernels achieving the same size bound.

We investigate the parameterized complexity of Vertex Cover parameterized above the optimum value of the linear programming (LP) relaxation of the integer linear programming formulation of the problem. By carefully analyzing the change in the LP value in the branching steps, we argue that even the most straightforward branching algorithm (after some preprocessing) results in an O^*(2.6181^r) algorithm for the problem where r is the excess of the vertex cover size over the LP optimum. We write O^*(f(k)) for a time complexity of the form O(f(k)n^{O(1)}), where f(k) grows exponentially with k. Then, using known and new reductions, we give O^*(2.6181^k) algorithms for the parameterized versions of Above Guarantee Vertex Cover, Odd Cycle Transversal, Split Vertex Deletion and Almost 2-SAT, and an O^*(1.6181^k) algorithm for Konig Vertex Deletion, Vertex Cover Param by OCT and Vertex Cover Param by KVD. These algorithms significantly improve the best known bounds for these problems. The notable improvement is the bound for Odd Cycle Transversal for which this is the first major improvement after the first algorithm that showed it fixed-parameter tractable in 2003. We also observe that using our algorithm, one can obtain a simple kernel for the classical vertex cover problem with at most 2k-O(log k) vertices.