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By Giorgio Ausiello, Stefano Leonardi, Alberto Marchetti-Spaccamela (auth.), Giancarlo Bongiovanni, Rossella Petreschi, Giorgio Gambosi (eds.)

The papers during this quantity have been offered on the Fourth Italian convention on Algorithms and Complexity (CIAC 2000). The convention happened on March 1-3, 2000, in Rome (Italy), on the convention heart of the collage of Rome \La Sapienza". This convention used to be born in 1990 as a countrywide assembly to be held each 3 years for Italian researchers in algorithms, facts constructions, complexity, and parallel and dispensed computing. because of a signi cant participation of international reaserchers, ranging from the second one convention, CIAC advanced into a world convention. in accordance with the decision for papers for CIAC 2000, there have been forty-one subm- sions, from which this system committee chosen 21 papers for presentation on the convention. every one paper was once evaluated through a minimum of 3 application committee participants. as well as the chosen papers, the organizing committee invited Giorgio Ausiello, Narsingh Deo, Walter Ruzzo, and Shmuel Zaks to offer plenary lectures on the convention. we want to convey our appreciation to the entire authors of the submitted papers, to this system committee participants and the referees, to the organizing committee, and to the plenary teachers who accredited our invitation.

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Extra info for Algorithms and Complexity: 4th Italian Conference, CIAC 2000 Rome, Italy, March 1–3, 2000 Proceedings

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In Figure 2, probes A and B have no measurements to probes C and D, and placement anywhere relative to probes C and D is consistent with the data. A B C D A C B D or Fig. 2. Another example of an under-constrained ordering In the examples of Figures 1 and 2, not only are the positions not uniquely determined, but different orderings are possible. When developing search algorithms, we have to be careful to recognize and treat such cases correctly. It appears that in real data such as from [Trask, personal communication, 1996], there are no degrees of freedom in the relative positioning of probes due to the careful choice of pairs of probes to measure.

We need not recompute the tree diameter. This computation includes adding a new node to the tree. On the other hand, if (u, near(u)) is still a feasible edge, then near(u) is the best choice for u among all nodes in the tree except possibly v, the newly added node. In this case, we need only determine whether edge (u, v) would increase the tree diameter beyond the constraint, and if not, whether the weight of (u, v) is less than wnear(u). 2 The complexity of Code Segment 4 is O(n ) when the diameter constraint k is small, since it requires looking at each node in the tree once for every node not in the tree.

1 Introduction In this paper we study path layouts in ATM networks, in which pairs of nodes exchange messages along pre-defined paths in the network, termed virtual paths. Given a physical network, the problem is to design these paths optimally. Each such design forms a layout of paths in the network, and each connection between two nodes must consist of a concatenation of such virtual paths. The smallest number of these paths between two nodes is termed the hop count for these nodes, and the load (or congestion) of a layout is the maximum number of virtual paths that go through any (physical) communication line.

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