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ContentsPrefaceiiiPlenary TalksYaroslav D. SergeyevInfinity computer and calculus 3Panos M. PardalosRecent Advances and Trends in Deterministic Global Optimization 5Extended AbstractsBernardetta Addis and Sven LeyfferA Trust-Region Algorithm for Global Optimization 9Bernardetta Addis, Marco Locatelli, and Fabio SchoenGlobal Optimization for Problems with a Huge Number of Local Optima 15April K. Andreas and J. Cole SmithExact Algorithms for Robust k-Path Routing Problems 17Charles Audet, Pierre Hansen, and Frédéric MessineThe Small Octagon with Longest Perimeter 23János Balogh, József Békési, Gábor Galambos, and Mihály Csaba MarkótAnalysis of a nonlinear optimization problem related to semi-on-line bin packing problems 29Balázs Bánhelyi, Tibor Csendes, and Barnabás GarayA global optimization model for locating chaos: numerical results 35V.M. Becerra, D.R. Myatt, S.J. Nasuto, J.M. Bishop, and D. IzzoAn Efficient Pruning Technique for the Global Optimisation of Multiple Gravity Assist Trajectories39Edson Tadeu Bez, Mirian Buss Gonçalves, and José Eduardo Souza de CursiCalibration of a Gravity–Opportunity Model of Trip Distribution by a Hybrid Procedure ofGlobal Optimization 47R. Blanquero, E. Carrizosa, E. Conde, and F. MessineOn weights estimation in Multiple Criteria Decision Analysis 53Sándor BozókiLeast Squares approximation of pairwise comparison matrices 57
Page 1: PROCEEDINGS OF THEINTERNATIONAL WOR
Page 6 and 7: viContentsEmilio Carrizosa, José G
Page 8 and 9: viiiContentsGraham R. Wood, Duangda
Page 11 and 12: Proceedings of GO 2005, pp. 3 - 4.I
Page 13: Proceedings of GO 2005, pp. 5 - 5.R
Page 17 and 18: Proceedings of GO 2005, pp. 9 - 14.
Page 19 and 20: A Trust-Region Algorithm for Global
Page 21 and 22: A Trust-Region Algorithm for Global
Page 23 and 24: Proceedings of GO 2005, pp. 15 - 16
Page 27 and 28: Algorithms for Robust k-path Routin
Page 29 and 30: Algorithms for Robust k-path Routin
Page 33 and 34: The Small Octagon with Longest Peri
Page 35 and 36: The Small Octagon with Longest Peri
Page 39 and 40: Analysis of a nonlinear optimizatio
Page 41 and 42: Analysis of a nonlinear optimizatio
Page 45: Numerical results for locating chao
Page 48 and 49: 40 V.M. Becerra, D.R. Myatt, S.J. N
Page 55 and 56:
Proceedings of GO 2005, pp. 47 - 52
Page 57 and 58:
Calibration of a Gravity-Opportunit
Page 59 and 60:
Calibration of a Gravity-Opportunit
Page 61 and 62:
Page 63 and 64:
On weights estimation in Multiple C
Page 65 and 66:
Page 67 and 68:
Least Squares approximation of pair
Page 69 and 70:
Page 71 and 72:
Locating competitive facilities 63W
Page 73:
Locating competitive facilities 65[
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68 E. Carrizosa, B. Martín-Barrag
Page 79 and 80:
Page 81 and 82:
Branch-and-Bound for the semi-conti
Page 83 and 84:
Branch-and-Bound for the semi-conti
Page 85 and 86:
Page 87 and 88:
Reliable Optimization in Civil Engi
Page 89 and 90:
Page 91 and 92:
A global optimization model for loc
Page 93 and 94:
Page 95 and 96:
Global Optimization of Low-Thrust S
Page 97 and 98:
Global Optimization of Low-Thrust S
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Page 101 and 102:
Neutral Data Fitting 93260Regressio
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Neutral Data Fitting 95For each dat
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Page 107 and 108:
Methods for obtaining an outer appr
Page 109 and 110:
Methods for obtaining an outer appr
Page 111 and 112:
Page 113 and 114:
Feasibility study by interval arith
Page 115 and 116:
Feasibility study by interval arith
Page 117 and 118:
Page 119 and 120:
Rigorous Affine Lower Bound Functio
Page 121:
Rigorous Affine Lower Bound Functio
Page 124 and 125:
116 C. Gil, R. Baños, M. G. Montoy
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Page 128 and 129:
Page 130 and 131:
122 C. Gutiérrez, B. Jiménez, and
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124 C. Gutiérrez, B. Jiménez, and
Page 135 and 136:
Page 137 and 138:
On the goodness of Global Optimisat
Page 139 and 140:
On the goodness of Global Optimisat
Page 141 and 142:
Page 143 and 144:
An Adaptive Radial Basis Algorithm
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Distributed Global Optimisation in
Page 153 and 154:
Distributed Global Optimisation in
Page 155 and 156:
Page 157 and 158:
VNS for Global Optimization 1491. s
Page 159:
VNS for Global Optimization 151[2]
Page 162 and 163:
154 Fuh-Hwa Franklin Liu, Chi-Wei S
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
160 Pierluigi Di Lizia, Gianmarco R
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
166 Dmitrii LozovanuTheorem 1. The
Page 176 and 177:
168 Dmitrii Lozovanuwhich determine
Page 178 and 179:
170 Dmitrii LozovanuIn [3] it is sh
Page 180 and 181:
172 Frédéric Messine and Ahmed To
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
178 R. P. Mondaini and N. V. Olivei
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180 R. P. Mondaini and N. V. Olivei
Page 191 and 192:
Page 193 and 194:
Fitting separable nonlinear spectro
Page 195 and 196:
Fitting separable nonlinear spectro
Page 197 and 198:
Page 199 and 200:
Global Optimisation Challenges in R
Page 201 and 202:
Global Optimisation Challenges in R
Page 203 and 204:
Page 205 and 206:
On solving of bilinear programming
Page 207 and 208:
Page 209 and 210:
GASUB: A genetic-like algorithm for
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Dynamic Stochastic Optimal Path 209
Page 219 and 220:
Dynamic Stochastic Optimal Path 211
Page 221 and 222:
Page 223 and 224:
Multi-Objective Optimization of Non
Page 225 and 226:
Multi-Objective Optimization of Non
Page 227 and 228:
Page 229 and 230:
Diagonal global search based on a s
Page 231 and 232:
Diagonal global search based on a s
Page 233 and 234:
Page 235 and 236:
Survivable Network Design 227given
Page 237 and 238:
Survivable Network Design 229prefer
Page 239 and 240:
Page 241 and 242:
Precision and Accuracy in Generatin
Page 243 and 244:
Precision and Accuracy in Generatin
Page 245 and 246:
Page 247:
New approach to nonconvex constrain
Page 250 and 251:
242 Boglárka Tóth and L.G. Casado
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
248 Massimiliano Vasilemultiagent e
Page 258 and 259:
250 Massimiliano Vasileproblems wer
Page 260 and 261:
252 Massimiliano VasileTable 3.Solu
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254 Tamás Vinkó and Arnold Neumai
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Page 267 and 268:
Global optimisation applied to pig
Page 269 and 270:
Global optimisation applied to pig
Page 271 and 272:
Page 273 and 274:
Optimal Triangulation: Old and New
Page 275:
Optimal Triangulation: Old and New
Page 278 and 279:
270 Author IndexGalambos, GáborUni
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