The maximum clique problem is a classical problem in combinatorial optimization which finds important applications in different domains. In this paper we try to give a survey of results concerning algorithms, complexity, and applications of this problem, and also provide an updated bibliography. Of course, we build upon precursory works with similar goals [39, 232, 266].

The maximum clique problem

The channel assignment problem involves not only assigning channels or frequencies to each radio cell. but also satisfying frequency constraints given by a compatibility matrix. The proposed parallel algorithm is based on an artificial neural network composed of nm processing elements for an n-cell-m-frequency problem. The algorithm runs not only on a sequential machine but also on a parallel machine with up to a maximum of nm processors. The algorithm was tested by solving eight benchmark problems where the total number of frequencies varied from 100 to 533. The algorithm found the solutions in nearly constant time with nm processors. The simulation results showed that the algorithm found better solutions than the existing algorithm in one out of eight problems. >

A neural network parallel algorithm for channel assignment problems in cellular radio networks

A computer vision method is presented to determine the 3-D spatial locations of joints or feature points of human body from a film recording the human motion during walking. The proposed method first applies the geometric projection theory to obtain a set of feasible postures from a single image, then it makes use of the given dimensions of the human stick figure, physiological and motion-specific knowledge to constrain the feasible postures in both the single-frame analysis and the multi-frame analysis. Finally a unique gait interpretation is selected by an optimization algorithm. Computer simulations are used to illustrate the ideas presented. >

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Knowledge-guided visual perception of 3-D human gait from a single image sequence

Given an undirected graph with weights on the vertices, the maximum weight clique problem (MWCP) is to find a subset of mutually adjacent vertices (a clique) having the largest total weight. This is a generalization of the problem of finding the maximum cardinality clique of an unweighted graph, which is the special case of the MWCP when all vertex weights are equal. The problem is NP-hard for arbitrary graphs, and so is the problem of approximating it within a constant factor. We present a parallel, distributed heuristic for approximating the MWCP based on dynamics principles. It centers around a continuous characterization of the MWCP (a purely combinatorial problem), and lets it be formulated in terms of continuous quadratic programming. One drawback is the presence of spurious solutions, and we present their characterizations. To avoid them we introduce a regularized continuous formulation of the MWCP and show how it completely solves the problem. The formulation naturally maps onto a parallel, distributed computational network whose dynamical behavior is governed by the replicator equations. These are dynamical systems introduced in evolutionary game theory and population genetics to model evolutionary processes on a macroscopic scale. We present theoretical results which guarantee that the solutions provided by our clique finding replicator network are actually those sought. Experimental results confirm the effectiveness of the proposed approach.

Approximating the maximum weight clique using replicator dynamics

In this chapter G = (V,E) denotes an arbitrary undirected graph without loops, where V = {v 1, v 2,…, v n } is its vertex set and E = {e 1,e 2,…, e m } ⊂ (E ×E) is its edge set. Two edges are adjacent if they connect to a common vertex. Two vertices v i and v j are adjacent if there is an edge e = (v i ,v j ) ∈ E. Finally, if e = (v i ,v j ) ∈ E,we say e is incident to vertices v i , v j .

The Graph Coloring Problem: A Bibliographic Survey

A parallel algorithm for finding a near-maximum independent set in a circle graph is presented. An independent set in a graph is a set of vertices, no two of which are adjacent. A maximum independent set is an independent set whose cardinality is the largest among all independent sets of a graph. The algorithm is modified for predicting the secondary structure in ribonucleic acids (RNA). The proposed system, composed of an n neural network array (where n is the number of edges in the circle graph of the number of possible base pairs), not only generates a near-maximum independent set but also predicts the secondary structure of ribonucleic acids within several hundred iteration steps. The simulator discovered several solutions which are more stable structures, in a sequence of 359 bases from the potato spindle tuber viroid, than previously proposed structures. >

K.-C. Lee

Papers

Parallel algorithms for finding a near-maximum independent set of a circle graph