Sharp bounds of the Zagreb indices of k-trees

For a graph G, the first Zagreb index M ₁ is equal to the sum of squares of the vertex degrees, and the second Zagreb index M ₂ is equal to the sum of the products of degrees of pairs of adjacent vertices. The Zagreb indices have been the focus of considerable research in computational chemistry dating back to Gutman and Trinajsti? in 1972. In 2004, Das and Gutman determined sharp upper and lower bounds for M ₁ and M ₂ values for trees along with the unique trees that obtain the minimum and maximum M ₁ and M ₂ values respectively. In this paper, we generalize the results of Das and Gutman to the generalized tree, the k-tree, where the results of Das and Gutman are for k=1. Also by showing that maximal outerplanar graphs are 2-trees, we also extend a result of Hou, Li, Song, and Wei who determined sharp upper and lower bounds for M ₁ and M ₂ values for maximal outerplanar graphs.     

Sharp bounds for Zagreb indices of maximal outerplanar graphs

For a (molecular) graph, the first Zagreb index M ₁ is equal to the sum of squares of the vertex degrees, and the second Zagreb index M ₂ is equal to the sum of products of degrees of pairs of adjacent vertices. In this paper, we investigate the first and the second Zagreb indices of maximal outerplanar graph. We determine sharp upper and lower bounds for M ₁-, M ₂-values among the n-vertex maximal outerplanar graphs. As well we determine sharp upper and lower bounds of Zagreb indices for n-vertex outerplanar graphs (resp. maximal outerplanar graphs) with perfect matchings.     

New bounds for locally irregular chromatic index of bipartite and subcubic graphs

A graph is locally irregular if the neighbors of every vertex v have degrees distinct from the degree of v. A locally irregular edge-coloring of a graph G is an (improper) edge-coloring such that the graph induced on the edges of any color class is locally irregular. It is conjectured that three colors suffice for a locally irregular edge-coloring. In the paper, we develop a method using which we prove four colors are enough for a locally irregular edge-coloring of any subcubic graph admiting such a coloring. We believe that our method can be further extended to prove the tight bound of three colors for such graphs. Furthermore, using a combination of existing results, we present an improvement of the bounds for bipartite graphs and general graphs, setting the best upper bounds to 7 and 220, respectively.     

Note on power propagation time and lower bounds for the power domination number

We present a counterexample to a lower bound for the power domination number given in Liao (J Comb Optim 31:725–742, 2016). We also define the power propagation time, using the power domination propagation ideas in Liao and the (zero forcing) propagation time in Hogben et al. (Discrete Appl Math 160:1994–2005, 2012).     

On the lower bounds of random Max 3 and 4-SAT

A k-CNF formula is said to be p-satisfiable if there exists a truth assignment satisfying a fraction of \(1-2^{-k}+p2^{-k}\) of its clauses. We obtain better lower bounds for random 3 and 4-SAT to be p-satisfiable. The technique we use is a delicate weighting scheme of the second moment method, where for every clause we give appropriate weight to truth assignments according to their number of satisfied literal occurrences.     

The upper bounds on the Steiner k-Wiener index in terms of minimum and maximum degrees

Journal of Combinatorial Optimization - For $$k in {mathbb {N}},$$ Ali et al. (Discrete Appl Math 160:1845-1850, 2012) introduce the Steiner k-Wiener index $$SW_{k}(G)=sum _{Sin V(G)} d(S),$$...     

Flexible multi-manned assembly line balancing problem: Model,heuristic procedure,and lower bounds for line length minimization

Assembly lines dedicated to the production of large products often allow multiple workers to perform tasks simultaneously on the product. Previous works on such multi-manned lines define workstations with fixed, discrete, and restrictive frontiers, despite commonly considering continuous paced line control. This paper proposes flexible station frontiers for multi-manned lines and shows that such innovation allows significantly shorter line lengths. A new Mixed Integer Linear Programming model and a novel model-based heuristic procedure are presented to describe and optimize lines. Algorithmic lower bounds are also introduced for the problem. The formulation was compared to a literature benchmark of regular multi-manned solutions. These experiments showed that flexible multi-manned formulations can lead to line length reductions of up to 42%. Such reductions were obtained for most instances (81 out of 88), with an average value of 18%. The relationship between cycle time and minimal line length is also analyzed, demonstrating that efficient solution sets can be continuous or discrete, depending on the instance.     

Cacti with the smallest,second smallest,and third smallest Gutman index

The Gutman index (also known as Schultz index of the second kind) of a graph \(G\) is defined as \(Gut(G)=\sum \nolimits _{u,v\in V(G)}d(u)d(v)d(u, v)\). A graph \(G\) is called a cactus if each block of \(G\) is either an edge or a cycle. Denote by \(\mathcal {C}(n, k)\) the set of connected cacti possessing \(n\) vertices and \(k\) cycles. In this paper, we give the first three smallest Gutman indices among graphs in \(\mathcal {C}(n, k)\), the corresponding extremal graphs are characterized as well.     

More bounds for the Grundy number of graphs

A coloring of a graph \(G=(V,E)\) is a partition \(\{V_1, V_2, \ldots , V_k\}\) of V into independent sets or color classes. A vertex \(v\in V_i\) is a Grundy vertex if it is adjacent to at least one vertex in each color class \(V_j\) for every \(j<i\). A coloring is a Grundy coloring if every vertex is a Grundy vertex, and the Grundy number \(\Gamma (G)\) of a graph G is the maximum number of colors in a Grundy coloring. We provide two new upper bounds on Grundy number of a graph and a stronger version of the well-known Nordhaus-Gaddum theorem. In addition, we give a new characterization for a \(\{P_{4}, C_4\}\)-free graph by supporting a conjecture of Zaker, which says that \(\Gamma (G)\ge \delta (G)+1\) for any \(C_4\)-free graph G.     

Upper bounds for the total rainbow connection of graphs

A two-agent scheduling problem on parallel machines is considered. Our objective is to minimize the makespan for agent A, subject to an upper bound on the makespan for agent B. When the number of machines, denoted by \(m\), is chosen arbitrarily, we provide an \(O(n)\) algorithm with performance ratio \(2-\frac{1}{m}\), i.e., the makespan for agent A given by the algorithm is no more than \(2-\frac{1}{m}\) times the optimal value, while the makespan for agent B is no more than \(2-\frac{1}{m}\) times the threshold value. This ratio is proved to be tight. Moreover, when \(m=2\), we present an \(O(nlogn)\) algorithm with performance ratio \(\frac{1+\sqrt{17}}{4}\approx 1.28\) which is smaller than \(\frac{3}{2}\). The ratio is weakly tight.     

Sum-of-squares rank upper bounds for matching problems

The matching problem is one of the most studied combinatorial optimization problems in the context of extended formulations and convex relaxations. In this paper we provide upper bounds for the rank of the sum-of-squares/Lasserre hierarchy for a family of matching problems. In particular, we show that when the problem formulation is strengthened by incorporating the objective function in the constraints, the hierarchy requires at most \(\left\lceil \frac{k}{2} \right\rceil \) levels to refute the existence of a perfect matching in an odd clique of size \(2k+1\).     

Upper bounds for adjacent vertex-distinguishing edge coloring

An adjacent vertex-distinguishing edge coloring of a graph is a proper edge coloring such that no pair of adjacent vertices meets the same set of colors. The adjacent vertex-distinguishing edge chromatic number is the minimum number of colors required for an adjacent vertex-distinguishing edge coloring, denoted as \(\chi '_{as}(G)\). In this paper, we prove that for a connected graph G with maximum degree \(\Delta \ge 3\), \(\chi '_{as}(G)\le 3\Delta -1\), which proves the previous upper bound. We also prove that for a graph G with maximum degree \(\Delta \ge 458\) and minimum degree \(\delta \ge 8\sqrt{\Delta ln \Delta }\), \(\chi '_{as}(G)\le \Delta +1+5\sqrt{\Delta ln \Delta }\).     

Tight approximation bounds for combinatorial frugal coverage algorithms

We consider the frugal coverage problem, an interesting variation of set cover defined as follows. Instances of the problem consist of a universe of elements and a collection of sets over these elements; the objective is to compute a subcollection of sets so that the number of elements it covers plus the number of sets not chosen is maximized. The problem was introduced and studied by Huang and Svitkina (Proceedings of the 29th IARCS annual conference on foundations of software technology and theoretical computer science (FSTTCS), pp. 227–238, 2009) due to its connections to the donation center location problem. We prove that the greedy algorithm has approximation ratio at least 0.782, improving a previous bound of 0.731 in Huang and Svitkina (Proceedings of the 29th IARCS annual conference on foundations of software technology and theoretical computer science (FSTTCS), pp. 227–238, 2009). We also present a further improvement that is obtained by adding a simple corrective phase at the end of the execution of the greedy algorithm. The approximation ratio achieved in this way is at least 0.806. Finally, we consider a packing based algorithm that uses semi-local optimization, and show that its approximation ratio is not less than 0.872. Our analysis is based on the use of linear programs which capture the behavior of the algorithms in worst-case examples. The obtained bounds are proved to be tight.     

Tight bounds for NF-based bounded-space online bin packing algorithms

In Zheng et al. (J Comb Optim 30(2):360–369, 2015) modelled a surgery problem by the one-dimensional bin packing, and developed a semi-online algorithm to give an efficient feasible solution. In their algorithm they used a buffer to temporarily store items, having a possibility to lookahead in the list. Because of the considered practical problem they investigated the 2-parametric case, when the size of the items is at most 1 / 2. Using an NF-based online algorithm the authors proved an ACR of \(13/9 = 1.44\ldots \) for any given buffer size not less than 1. They also gave a lower bound of \(4/3=1.33\ldots \) for the bounded-space algorithms that use NF-based rules. Later, in Zhang et al. (J Comb Optim 33(2):530–542, 2017) an algorithm was given with an ACR of 1.4243,  and the authors improved the lower bound to 1.4230. In this paper we present a tight lower bound of \(h_\infty (r)\) for the r-parametric problem when the buffer capacity is 3. Since \(h_\infty (2) = 1.42312\ldots \), our result—as a special case—gives a tight bound for the algorithm-class given in 2017. To prove that the lower bound is tight, we present an NF-based online algorithm that considers the r-parametric problem, and uses a buffer with capacity of 3. We prove that this algorithm has an ACR that is equal to the lower bounds for arbitrary r.     

Modified linear programming and class 0 bounds for graph pebbling

Given a configuration of pebbles on the vertices of a connected graph G, a pebbling move removes two pebbles from some vertex and places one pebble on an adjacent vertex. The pebbling number of a graph G is the smallest integer k such that for each vertex v and each configuration of k pebbles on G there is a sequence of pebbling moves that places at least one pebble on v. First, we improve on results of Hurlbert, who introduced a linear optimization technique for graph pebbling. In particular, we use a different set of weight functions, based on graphs more general than trees. We apply this new idea to some graphs from Hurlbert’s paper to give improved bounds on their pebbling numbers. Second, we investigate the structure of Class 0 graphs with few edges. We show that every n-vertex Class 0 graph has at least \(\frac{5}{3}n - \frac{11}{3}\) edges. This disproves a conjecture of Blasiak et al. For diameter 2 graphs, we strengthen this lower bound to \(2n - 5\), which is best possible. Further, we characterize the graphs where the bound holds with equality and extend the argument to obtain an identical bound for diameter 2 graphs with no cut-vertex.     

Lower bounds for positive semidefinite zero forcing and their applications

The positive semidefinite zero forcing number of a graph is a parameter that is important in the study of minimum rank problems. In this paper, we focus on the algorithmic aspects of computing this parameter. We prove that it is NP-complete to find the positive semidefinite zero forcing number of a given graph, and this problem remains NP-complete even for graphs with maximum vertex degree 7. We present a linear time algorithm for computing the positive semidefinite zero forcing number of generalized series–parallel graphs. We introduce the constrained tree cover number and apply it to improve lower bounds for positive semidefinite zero forcing. We also give formulas for the constrained tree cover number and the tree cover number on graphs with special structures.     

Lower bounds for independence numbers of some locally sparse graphs

An \(m\) -distinct-coloring is a proper vertex-coloring \(c\) of a graph \(G\) if for each vertex \(v\in V\) , any color appears in at most one of \(N_0(v)\) , \(N_1(v)\) , \(\ldots \) , and \(N_m(v)\) , where \(N_i(v)\) is the set of vertices at distance \(i\) from \(v\) . In this note, we show that if \(G\) is \(C_{2m+1}\) -free which is assigned an \((m+1)\) -distinct-coloring \(c\) , then \(\alpha (G)c(G)^{1/m}\ge \Omega \Big (\sum _{v} c(v)^{1/m}\Big )\) , where \(c(G)\) is the number of colors used in \(c\) and \(c(v)\) is the number of different colors appearing in \(N_1(v)\) . Moreover, we obtain that if \(G\) has \(N\) vertices and it contains neither \(C_{2m+1}\) nor \(C_{2m}\) , then \(\alpha (G)\ge \Omega \big ((N\log N)^{m/(m+1)}\big )\) . The algorithm in the proof for the first result is random, and that for the second is constructive.