A model and heuristic algorithms for multi-unit nondiscriminatory combinatorial auction

Single unit combinatorial auction problem (CAP) and its multi-unit extension have received a lot of attention recently. This paper introduces yet another variant of CAP which generalizes the multi-unit CAP further to allow bids on collections of items that can come from bidder defined classes of items. A bidder may be indifferent to some items with different brands, specifications or qualities and consider them as substitutable. In this case, these items can be put in a class and hence the bids can be made by referring to the items in such classes. This model enables the bidder to express his requests by using less number of bids in case he does not discriminate between different items. Because of this, we call this problem multi-unit nondiscriminatory combinatorial auction (MUNCA) problem. An integer programming formulation is given for this problem. Since this problem is NP-hard, two fast heuristic algorithms have also been designed. The heuristics give quite good solutions when compared to the optimal solution.     

Rigidity of a simple extended lower triangular matrix

For the all-ones lower triangular matrices, the upper and lower bounds on rigidity are known to match [P. Pudlak, Z. Vavrin, Computation of rigidity of order n²/r for one simple matrix, Comment Math. Univ. Carolin. 32 (2) (1991) 213-218]. In this short note, we apply these techniques to the all-ones extended lower triangular matrices, to obtain upper and lower bounds with a small gap between the two; we show that the rigidity is .     

On complexity of single-minded auction

We consider complexity issues for a special type of combinatorial auctions, the single-minded auction, where every agent is interested in only one subset of the commodities.First, we present a matching bound on the communication complexity for the single-minded auction under a general communication model. Next, we prove that it is NP-hard to decide whether Walrasian equilibrium exists in a single-minded auction. Finally, we establish a polynomial size duality theorem for the existence of Walrasian equilibrium for the single-minded auction.     

An optimal multiprocessor combinatorial auction solver

A combinatorial auction (CA) is an auction that permits bidders to bid on bundles of goods rather than just a single item. Unfortunately, winner determination for CAs is known to be NP-hard. In this paper, we propose a distributed algorithm to compute optimal solutions to this problem. The algorithm uses nagging, a technique for parallelizing search in heterogeneous distributed computing environments. Here, we show how nagging can be used to parallelize a branch-and-bound algorithm for this problem, and provide empirical results supporting both the performance advantage of nagging over more traditional partitioning methods as well as the superior scalability of nagging to larger numbers of processors.     

Fixed-parameter algorithms for Kemeny rankings

The computation of Kemeny rankings is central to many applications in the context of rank aggregation. Given a set of permutations (votes) over a set of candidates, one searches for a “consensus permutation” that is “closest” to the given set of permutations. Unfortunately, the problem is NP-hard. We provide a broad study of the parameterized complexity for computing optimal Kemeny rankings. Besides the three obvious parameters “number of votes”, “number of candidates”, and solution size (called Kemeny score), we consider further structural parameterizations. More specifically, we show that the Kemeny score (and a corresponding Kemeny ranking) of an election can be computed efficiently whenever the average pairwise distance between two input votes is not too large. In other words, Kemeny Score is fixed-parameter tractable with respect to the parameter “average pairwise Kendall–Tau distance _da$d_a$”. We describe a fixed-parameter algorithm with running time 16^⌈_da⌉⋅poly$16^{\lceil d_a\rceil }\cdot poly$. Moreover, we extend our studies to the parameters “maximum range” and “average range” of positions a candidate takes in the input votes. Whereas Kemeny Score remains fixed-parameter tractable with respect to the parameter “maximum range”, it becomes NP-complete in the case of an average range of two. This excludes fixed-parameter tractability with respect to the parameter “average range” unless P=NP. Finally, we extend some of our results to votes with ties and incomplete votes, where in both cases one no longer has permutations as input.     

Optimal 2-constraint satisfaction via sum-product algorithms

We show that for a given set of m pairwise constraints over n variables, a variable assignment that satisfies maximally many m constraints (MAX-2-CSP) can be found in time, where d is the maximum number of states per variable, and ω<2.376 is the matrix product exponent over a ring; the notation O^∗ suppresses factors polylogarithmic in m and n. As a corollary, MAX-2-SAT can be solved in O^∗(nmn^1.732) time. This improves on a recent result by Williams [R. Williams, A new algorithm for optimal 2-constraint satisfaction and its implications, Theoret. Comput. Sci. 348 (2-3) (2005) 357-365] by reducing the polynomial factor from nm³ to about nm.     

Approximation algorithms for terrain guarding

We present approximation algorithms and heuristics for several variations of terrain guarding problems, where we need to guard a terrain in its entirety by a minimum number of guards. Terrain guarding has applications in telecommunications, namely in the setting up of antenna networks for wireless communication. Our approximation algorithms transform the terrain guarding instance into a Minimum Set Cover instance, which is then solved by the standard greedy approximation algorithm [J. Comput. System Sci. 9 (1974) 256-278]. The approximation algorithms achieve approximation ratios of O(logn), where n is the number of vertices in the input terrain. We also briefly discuss some heuristic approaches for solving other variations of terrain guarding problems, for which no approximation algorithms are known. These heuristic approaches do not guarantee non-trivial approximation ratios but may still yield good solutions.     

Polynomial-time algorithms for probabilistic solutions of parameter-dependent linear matrix inequalities

A randomized approach is considered for a feasibility problem on a parameter-dependent linear matrix inequality (LMI). In particular, a gradient-based and an ellipsoid-based randomized algorithms are improved by introduction of a stopping rule. The improved algorithms stop after a bounded number of iterations and this bound is of polynomial order in the problem size. When the algorithms stop, either of the following two events occurs: (i) they find with high confidence a probabilistic solution, which satisfies the given LMI for most of the parameter values; (ii) they detect in an approximate sense the non-existence of a deterministic solution, which satisfies the given LMI for all the parameter values. These results are important because the original randomized algorithms have issues to be settled on detection of convergence, on the speed of convergence, and on the assumption of feasibility. The improved algorithms can be adapted for an optimization problem constrained by a parameter-dependent LMI. A numerical example shows the efficacy of the proposed algorithms.     

The complexity of the certification of properties of Stable Marriage

We give some lower bounds on the certificate complexity of some problems concerning stable marriage, answering a question of Gusfield and Irving.     

Computational complexity of randomized algorithms for solving parameter-dependent linear matrix inequalities

Randomized algorithms are proposed for solving parameter-dependent linear matrix inequalities and their computational complexity is analyzed. The first proposed algorithm is an adaptation of the algorithms of Polyak and Tempo [(Syst. Control Lett. 43(5) (2001) 343)] and Calafiore and Polyak [(IEEE Trans. Autom. Control 46 (11) (2001) 1755)] for the present problem. It is possible however to show that the expected number of iterations necessary to have a deterministic solution is infinite. In order to make this number finite, the improved algorithm is proposed. The number of iterations necessary to have a probabilistic solution is also considered and is shown to be independent of the parameter dimension. A numerical example is provided.     

A fixed-parameter tractable algorithm for matrix domination

Matrix domination is the NP-complete problem of determining whether a given {0,1} matrix contains a set of k non-zero entries that are in the same row or same column as all other non-zero entries. Using a kernelization and search tree approach, we show the problem to be fixed-parameter tractable with running time .     

Bases for Boolean co-clones

The complexity of various problems in connection with Boolean constraints, like, for example, quantified Boolean constraint satisfaction, have been studied recently. Depending on what types of constraints may be used, the complexity of such problems varies. A very interesting observation of the recent past has been that the thus derived classification of constraints can be explained with the help of universal algebra. More precisely, the difficulty of such a constraint problem often depends on the co-clone the constraints are from. A co-clone is a set of Boolean relations that is closed under very natural closure operations. Nearly all these co-clones can be generated by said operators out of a finite set of relations, a so-called base. Knowing a, preferably simple, base for each co-clone can therefore be of great value when studying the complexity of Boolean constraint problems, since this knowledge reduces the infinitely many cases of equivalent problems to a single one—the constraint satisfaction problem for this base. In this paper we give a finite and simple base for every Boolean co-clone, where this is possible. We give evidence that the presented bases are as easy as possible.     

Quantum computation based bundling optimization for combinatorial auction in freight service procurements

Combinatorial auction is a useful trade manner for transportation service procurements in e-marketplaces. To enhance the competition of combinatorial auction, a novel auction mechanism of two-round bidding with bundling optimization is proposed. As the recommended the auction mechanism, the shipper/auctioneer integrates the objects into several bundles based on the bidding results of first round auction. Then, carriers/bidders bid for the object bundles in second round. The bundling optimization is described as a multi-objective model with two criteria on price complementation and combination consistency. A Quantum Evolutionary Algorithm (QEA) with β-based rotation gate and the encoding scheme based on non-zero elements in complementary coefficient matrix is developed for the model solution. Comparing with a Contrast Genetic Algorithm, QEA can achieve better computational performances for small and middle size problems.     

Accelerated low rank matrix approximate algorithms for matrix completion

In this paper, we propose two accelerated algorithms for the low-rank approximate method in Wang et al. (0000) for matrix completion. The main idea is to use the successive over-relaxation technique. Based on the successive over-relaxation method for the feasible matrices or projection matrices, the low-rank matrix approximate method is modified and accelerated. Meanwhile, we discuss the convergence of the over-relaxation algorithm for the feasible matrix. Finally, the numerical experiments show them to be effective.     

Polynomial algorithms for protein similarity search for restricted mRNA structures

In this paper we consider the problem of computing an mRNA sequence of maximal similarity for a given mRNA of secondary structure constraints, introduced by Backofen et al. in [R. Backofen, N.S. Narayanaswamy, F. Swidan, On the complexity of protein similarity search under mRNA structure constraints, in: Proceedings of the Annual Symposium of Theoretical Aspects of Computer Science, in: LNCS, vol. 2285, Springer, 2002, pp. 274-286] denoted as the MRSO problem. The problem is known to be NP-complete for planar associated implied structure graphs of vertex degree at most 3. In [G. Blin, G. Fertin, D. Hermelin, S. Vialette, Fixed-parameter algorithms for protein similarity search under mRNA structure constraints, in: Proceedings of Graph-Theoretical Concepts in Computer Science, in: LNCS, vol. 3787, Springer, 2005, pp. 271-282] a first polynomial dynamic programming algorithms for MRSO on implied structure graphs with maximum vertex degree 3 of bounded cut-width is shown.We give a simple but much more general polynomial dynamic programming solution for the MRSO problem for associated implied structure graphs of bounded clique-width. Our result implies that MRSO is polynomial for graphs of bounded tree-width, co-graphs, P₄-sparse graphs, and distance hereditary graphs. Further we conclude that the problem of comparing two solutions for MRSO is hard for the class , which is defined as the set of problems which can be solved in polynomial time with a number of parallel queries to an oracle in NP.