By Sándor P. Fekete, Henk Meijer, André Rohe, Walter Tietze (auth.), Adam L. Buchsbaum, Jack Snoeyink (eds.)
This e-book constitutes the completely refereed post-proceedings of the 3rd foreign Workshop on set of rules Engineering and Experimentation, ALENEX 2001, held in Washington, DC, united states in January 2001.
The 15 revised complete papers offered including the abstracts of 3 invited shows have passed through rounds of reviewing and revision and have been chosen from 31 submissions. one of the themes addressed are heuristics for approximation, community optimization, TSP, randomization, sorting, details retrieval, graph computations, tree clustering, scheduling, community algorithms, element set computations, looking out, and information mining.
Read or Download Algorithm Engineering and Experimentation: Third International Workshop, ALENEX 2001 Washington, DC, USA, January 5–6, 2001 Revised Papers PDF
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Extra info for Algorithm Engineering and Experimentation: Third International Workshop, ALENEX 2001 Washington, DC, USA, January 5–6, 2001 Revised Papers
We want to travel from source to target destination with minimal cost satisfying the resource constraint(s). Here, costs could be for example time and resource could be fuel consumption (see left part of Figure 3). Many other cost and resource models exist. For example we may want to minimize the total height diﬀerence while not travelling more than a given distance. (see right part of Figure 3). After setting up the graph and the edge costs and resources we can simply use the csp function of CNOP.
Then one repeats the following process some predetermined number of times: Apply a random 4-opt move to the current champion, and use the resulting tour as the starting tour during another run of the algorithm. If the resulting tour is better than the current champion, declare it to be the new champion. Typically don’t-look bits persist from one iteration to the next, which means that only 8 vertices are initially available as starting points for searches, which oﬀers signiﬁcant speedups. In our implementations we choose uniformly from all 4-opt moves.
Alternatively, we may also compute the minimum number of breakpoints for a given approximation error. Fig. 4. Coastline of Corsica (800 points) and minimum error approximation using 200 points Since we now have identical resources and k ≤ n, the problem is not NP-hard anymore, since the dynamic programming formulation now runs in O(kn2 ) = O(n3 ). Dahl and Realfsen [DR97,DR00] observed that solving the relaxation outperforms the exact dynamic programming but pointed out the problem that it cannot guarantee optimality although the optimum was often reached in their experiments.