An exact semidefinite programming approach for the max-mean dispersion problem

This paper proposes an exact algorithm for the Max-Mean dispersion problem (\(Max-Mean DP\)), an NP-Hard combinatorial optimization problem whose aim is to select the subset of a set such that the average distance between elements is maximized. The problem admits a natural non-convex quadratic fractional formulation from which a semidefinite programming (SDP) relaxation can be derived. This relaxation can be tightened by means of a cutting plane algorithm which iteratively adds the most violated triangular inequalities. The proposed approach embeds the SDP relaxation and the cutting plane algorithm into a branch and bound framework to solve \(Max-Mean DP\) instances to optimality. Computational experiments show that the proposed method is able to solve to optimality in reasonable time instances with up to 100 elements, outperforming other alternative approaches.     

An approximation algorithm for the maximum spectral subgraph problem

Journal of Combinatorial Optimization - Modifying the topology of a network to mitigate the spread of an epidemic with epidemiological constant $$\lambda $$ amounts to the NP-hard problem of...     

An approximation algorithm for the uniform capacitated k-means problem

Journal of Combinatorial Optimization - In this paper, we consider the uniform capacitated k-means problem (UC-k-means), an extension of the classical k-means problem (k-means) in machine learning....     

An approximation algorithm for k-center problem on a convex polygon

This paper studies the constrained version of the k-center location problem. Given a convex polygonal region, every point in the region originates a service demand. Our objective is to place k facilities lying on the region’s boundary, such that every point in that region receives service from its closest facility and the maximum service distance is minimized. This problem is equivalent to covering the polygon by k circles with centers on its boundary which have the smallest possible radius. We present an 1.8841-approximation polynomial time algorithm for this problem.     

An improved approximation algorithm for uncapacitated facility location problem with penalties

In this paper, we consider an interesting variant of the classical facility location problem called uncapacitated facility location problem with penalties (UFLWP for short) in which each client is either assigned to an opened facility or rejected by paying a penalty. The UFLWP problem has been effectively used to model the facility location problem with outliers. Three constant approximation algorithms have been obtained (Charikar et al. in Proceedings of the Symposium on Discrete Algorithms, pp. 642–651, 2001; Jain et al. in J. ACM 50(6):795–824, 2003; Xu and Xu in Inf. Process. Lett. 94(3):119–123, 2005), and the best known performance ratio is 2. The only known hardness result is a 1.463-inapproximability result inherited from the uncapacitated facility location problem (Guha and Khuller in J. Algorithms 31(1):228–248, 1999). In this paper, We present a 1.8526-approximation algorithm for the UFLWP problem. Our algorithm significantly reduces the gap between known performance ratio and the inapproximability result. Our algorithm first enhances the primal-dual method for the UFLWP problem (Charikar et al. in Proceedings of the Symposium on Discrete Algorithms, pp. 642–651, 2001) so that outliers can be recognized more efficiently, and then applies a local search heuristic (Charikar and Guha in Proceedings of the 39th IEEE Symposium on Foundations of Computer Science, pp. 378–388, 1999) to further reduce the cost for serving those non-rejected clients. Our algorithm is simple and can be easily implemented. The research of this work was supported in part by NSF through CAREER award CCF-0546509 and grant IIS-0713489. A preliminary version of this paper appeared in the Proceedings of the 11th Annual International Computing and Combinatorics Conference (COCOON’05).     

An approximation algorithm for stochastic multi-level facility location problem with soft capacities

Journal of Combinatorial Optimization - Facility location problem is one of the most important problems in the combinatorial optimization. The multi-level facility location problem and the facility...     

A successive approximation algorithm for the multiple knapsack problem

It is well-known that the multiple knapsack problem is NP-hard, and does not admit an FPTAS even for the case of two identical knapsacks. Whereas the 0-1 knapsack problem with only one knapsack has been intensively studied, and some effective exact or approximation algorithms exist. A natural approach for the multiple knapsack problem is to pack the knapsacks successively by using an effective algorithm for the 0-1 knapsack problem. This paper considers such an approximation algorithm that packs the knapsacks in the nondecreasing order of their capacities. We analyze this algorithm for 2 and 3 knapsack problems by the worst-case analysis method and give all their error bounds.     

A simple greedy approximation algorithm for the minimum connected $$$$-Center problem

In this paper, we consider the connected \(k\)-Center (\(CkC\)) problem, which can be seen as the classic \(k\)-Center problem with the constraint of internal connectedness, i.e., two nodes in a cluster are required to be connected by an internal path in the same cluster. \(CkC\) was first introduced by Ge et al. (ACM Trans Knowl Discov Data 2:7, 2008), in which they showed the \(NP\)-completeness for this problem and claimed a polynomial time approximation algorithm for it. However, the running time of their algorithm might not be polynomial, as one key step of their algorithm involves the computation of an \(NP\)-hard problem. We first present a simple polynomial time greedy-based \(2\)-approximation algorithm for the relaxation of \(CkC\)—the \(CkC^*\). Further, we give a \(6\)-approximation algorithm for \(CkC\).     

An approximation algorithm for maximum internal spanning tree

Given a graph G, the maximum internal spanning tree problem (MIST for short) asks for computing a spanning tree T of G such that the number of internal vertices in T is maximized. MIST has possible applications in the design of cost-efficient communication networks and water supply networks and hence has been extensively studied in the literature. MIST is NP-hard and hence a number of polynomial-time approximation algorithms have been designed for MIST in the literature. The previously best polynomial-time approximation algorithm for MIST achieves a ratio of \(\frac{3}{4}\). In this paper, we first design a simpler algorithm that achieves the same ratio and the same time complexity as the previous best. We then refine the algorithm into a new approximation algorithm that achieves a better ratio (namely, \(\frac{13}{17}\)) with the same time complexity. Our new algorithm explores much deeper structure of the problem than the previous best. The discovered structure may be used to design even better approximation or parameterized algorithms for the problem in the future.     

A new approximation algorithm for the Selective Single-Sink Buy-at-Bulk problem in network design

The Selective Single-Sink Buy-at-Bulk problem was proposed by Awerbuch and Azar (FOCS 1997). For a long time, the only known non-trivial approach to approximate this problem is the tree-embedding method initiated by Bartal (FOCS 1996). In this paper, we give a thoroughly different approximation approach for the problem with approximation ratio $O(\sqrt{q})$ , where q is the number of source terminals in the problem instance. Our approach is based on a mixed strategy of LP-rounding and the greedy method. When the number q (which is always at most n) is relatively small (say, q=o(log² n)), our approximation ratio $O(\sqrt{q})$ is better than the currently known best ratio O(logn), where n is the number of vertices in the input graph.     

A mixed integer programming and heuristic algorithm for a warehouses location problem

A warehouses location problem is treated using a mixed integer programming and a heuristic algorithm. A simplification of freight rates schedules, based upon shipments consolidation and a linear regression of rates vs distances was made. Warehousing costs were divided according to fixed and variable and related to the throughput of the warehouses. Consideration was given in the analysis to the choice between owning and leasing each warehouse. In the case studied, the analysis demonstrated that a possible saving of approximately 22 per cent in annual distribution costs could be realized under the optimized warehouse location network.     

An exact algorithm for the maximum probabilistic clique problem

The maximum clique problem is a classical problem in combinatorial optimization that has a broad range of applications in graph-based data mining, social and biological network analysis and a variety of other fields. This article investigates the problem when the edges fail independently with known probabilities. This leads to the maximum probabilistic clique problem, which is to find a subset of vertices of maximum cardinality that forms a clique with probability at least \(\theta \in [0,1]\) , which is a user-specified probability threshold. We show that the probabilistic clique property is hereditary and extend a well-known exact combinatorial algorithm for the maximum clique problem to a sampling-free exact algorithm for the maximum probabilistic clique problem. The performance of the algorithm is benchmarked on a test-bed of DIMACS clique instances and on a randomly generated test-bed.     

Solving the flight gate assignment problem using dynamic programming

This paper considers the problem of assigning flights to airport gates—a problem which is NP-hard in general. We focus on a special case in which the maximization of flight/gate preference scores is the only objective. We show that for a variable number of flights and gates, this problem is still NP-hard. For a fixed number of gates, we present a dynamic programming approach that solves the flight assignment problem in linear time with respect to the number of flights. Computational results using real life data from a major European airport prove the practical relevance of this approach.     

An exact algorithm for the multicriteria ordered clustering problem

In the context of multicriteria decision aid, we address the problem of regrouping alternatives into completely ordered categories based on valued preference degrees. We assume that the number of groups is fixed a priori. This will be referred to as the multicriteria ordered clustering problem. The model is based on the definition of an inconsistency matrix and only uses the ordinal properties of the pairwise preference relations. An exact algorithm is proposed to find the ordered partition and is applied as illustration to the Human Development Index.     

Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation

Sequential resource allocation decision-making for the military medical evacuation of wartime casualties consists of identifying which available aeromedical evacuation (MEDEVAC) assets to dispatch in response to each casualty event. These sequential decisions are complicated due to uncertainty in casualty demand (i.e., severity, number, and location) and service times. In this research, we present a Markov decision process model solved using a hierarchical aggregation value function approximation scheme within an approximate policy iteration algorithmic framework. The model seeks to optimize this sequential resource allocation decision under uncertainty of how to best dispatch MEDEVAC assets to calls for service. The policies determined via our approximate dynamic programming (ADP) approach are compared to optimal military MEDEVAC dispatching policies for two small-scale problem instances and are compared to a closest-available MEDEVAC dispatching policy that is typically implemented in practice for a large-scale problem instance. Results indicate that our proposed approximation scheme provides high-quality, scalable dispatching policies that are more easily employed by military medical planners in the field. The identified ADP policies attain 99.8% and 99.5% optimal for the 6- and 12-zone problem instances investigated, as well as 9.6%, 9.2%, and 12.4% improvement over the closest-MEDEVAC policy for the 6-, 12-, and 34-zone problem instances investigated.     

An approximation algorithm for maximum weight budgeted connected set cover

This paper studies approximation algorithm for the maximum weight budgeted connected set cover (MWBCSC) problem. Given an element set \(X\), a collection of sets \({\mathcal {S}}\subseteq 2^X\), a weight function \(w\) on \(X\), a cost function \(c\) on \({\mathcal {S}}\), a connected graph \(G_{\mathcal {S}}\) (called communication graph) on vertex set \({\mathcal {S}}\), and a budget \(L\), the MWBCSC problem is to select a subcollection \({\mathcal {S'}}\subseteq {\mathcal {S}}\) such that the cost \(c({\mathcal {S'}})=\sum _{S\in {\mathcal {S'}}}c(S)\le L\), the subgraph of \(G_{\mathcal {S}}\) induced by \({\mathcal {S'}}\) is connected, and the total weight of elements covered by \({\mathcal {S'}}\) (that is \(\sum _{x\in \bigcup _{S\in {\mathcal {S'}}}S}w(x)\)) is maximized. We present a polynomial time algorithm for this problem with a natural communication graph that has performance ratio \(O((\delta +1)\log n)\), where \(\delta \) is the maximum degree of graph \(G_{\mathcal {S}}\) and \(n\) is the number of sets in \({\mathcal {S}}\). In particular, if every set has cost at most \(L/2\), the performance ratio can be improved to \(O(\log n)\).