### Table 1. Classification of global optimization methods based on the degree of history dependence.

"... In PAGE 2: ... Finally, the small energy difference between the correct and incorrect minima and the exponential growth of the density of the non-native states with energy impose strict requirements on the accuracy of energy evaluation (less than about 1 kcal/mol)5. Numerous approaches have been used to attack the global optimization problem in protein structure prediction, with some success1-8 ( Table1 ). These methods are initially classified according to whether they are deterministic or not; stochastic methods are further subdivided according to the degree of similarity between conformations generated in consecutive iterations of the search algorithm.... In PAGE 3: ... Most of the MC-like stochastic global optimization strategies employ a three-step iteration: (i) modify the current conformation by means of a random move; (ii) evaluate its energy; (iii) accept or reject the new conformation according to an acceptance criterion. The random moves can be ranked by magnitude of change with respect to the current conformation ( Table1 ). The first group contains algorithms in which the generated conformations do not depend on the previous ones.... ..."

### Table 2. Global and local search components used in existing global optimization methods. Method Global Component Local Component

1997

"... In PAGE 9: ... Whenever it is possible, we analyze the balance that each algorithm strikes between global search and local re nement, and relate this balance to its performance. Table2 summarizes this balance for a number of popular global... In PAGE 13: ... This compromise between global and local searches severely limits the performance of global search, and results in very high computational complexity. In view of the drawbacks in trajectory methods and the imbalance between global and local searches ( Table2 ), we propose in Section 4 a new algorithm that uses decoupled global and local search strategies. Our global search strategy is based on a traveling trace that collects geometrical information and uncovers new regions of local minima.... ..."

Cited by 18

### Table 3. Optimal views based on visual hull re- construction.

2004

"... In PAGE 7: ... Using the same search strategy, we now evaluate the visual hull construc- tions obtained from the given subset of silhouette im- ages and compare them to the ground truth. Table3 shows the optimal views for K = {2,3,4,5} and the correspond- ing error values (same format as in Table 2 except that the visual hull from a single silhouette (K = 1) has no fi- nite volume and is omitted). Note that a visual hull recon- struction (especially one from few images) is not a very... In PAGE 8: ... Interestingly, the first plateau corresponding to the top group is all the sub- sets which include the profile view #10 (one of the most salient). We can see marked similarities in the opti- mal views in Table 2 and Table3 . For example, both methods indicate views #3 and #10 to be the most infor- mative.... ..."

Cited by 7

### Table 7: Solving the Gripper problem with q pattern data bases optimally: tp is the total time to construct the tables, t is the time to perform the search of e expansions to nd a solution of length l; pd indicates search with pattern data bases and ff abbreviates forward search with FF.

2001

"... In PAGE 13: ... It spans a very large ( gt; 4#balls) but well structured search space such that greedy search engines nd optimal solutions. In Table7 we compared the FF-heuristic (with cuts disabled) and the pattern data bases heuristic when applying hill climbing. Note that searching Gripper with weighted A* results in too many expansions in the search, for which the lack of move ordering is responsible.... ..."

Cited by 49

### Table 3. Optimal clusters of the SOM of his- tograms given by dendrogram search. Ra- dius for local minima search defines the base clusters for each method. The best methods are marked with *.

2003

"... In PAGE 4: ... From the results of Table 2 it can be seen that Ward clustering gives the best base clusters of mobile cells in microcell scenario. For both scenarios clustering with lower DB index can be found from the den- drograms as can be seen from Table3 . The dendrograms built on optimal base clusters and on first guess base clus- ters (radius equals one) are searched.... ..."

Cited by 7

### Table 3. Optimal clusters of the SOM of his- tograms given by dendrogram search. Ra- dius for local minima search defines the base clusters for each method. The best methods are marked with *.

2003

"... In PAGE 4: ... From the results of Table 2 it can be seen that Ward clustering gives the best base clusters of mobile cells in microcell scenario. For both scenarios clustering with lower DB index can be found from the den- drograms as can be seen from Table3 . The dendrograms built on optimal base clusters and on first guess base clus- ters (radius equals one) are searched.... ..."

Cited by 7

### Table 3. Optimal clusters of the SOM of his- tograms given by dendrogram search. Ra- dius for local minima search defines the base clusters for each method. The best methods are marked with *.

2003

"... In PAGE 4: ... From the results of Table 2 it can be seen that Ward clustering gives the best base clusters of mobile cells in microcell scenario. For both scenarios clustering with lower DB index can be found from the den- drograms as can be seen from Table3 . The dendrograms built on optimal base clusters and on first guess base clus- ters (radius equals one) are searched.... ..."

Cited by 7

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."