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Multi-fragment algorithm

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The algorithm builds a tour for the traveling salesman one edge at a time and thus maintains multiple tour fragments, each of which is a simple path in the complete graph of cities. At each stage, the algorithm selects the edge of minimal cost that either creates a new fragment, extends one of the
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Johnson, David; A. McGeoch, Lyle (1997). "The Traveling Salesman Problem: A Case Study in Local Optimization".
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existing paths or creates a cycle of length equal to the number of cities.
282: 210: 24: 274: 153: 184: 240: 331: 314: 51:introducing citations to additional sources 321: 307: 243:Local Search in Combinatorial Optimization 234: 254: 41:Relevant discussion may be found on the 332: 16:Travelling salesman problem heuristic 350:Algorithms and data structures stubs 269: 185:{\displaystyle \Theta (N^{2}\log N)} 18: 13: 154: 14: 361: 273: 34:relies largely or entirely on a 23: 179: 157: 1: 228: 207:multi-fragment (MF) algorithm 293:. You can help Knowledge by 7: 340:Travelling salesman problem 219:travelling salesman problem 10: 366: 268: 62:"Multi-fragment algorithm" 195: 139: 129: 119: 345:Approximation algorithms 115:Multi-fragment algorithm 124:Approximation algorithm 289:-related article is a 186: 187: 151: 47:improve this article 116: 217:algorithm for the 182: 114: 302: 301: 203: 202: 112: 111: 97: 357: 323: 316: 309: 277: 270: 261: 260: 258: 238: 191: 189: 188: 183: 169: 168: 117: 113: 107: 104: 98: 96: 55: 27: 19: 365: 364: 360: 359: 358: 356: 355: 354: 330: 329: 328: 327: 287:data structures 266: 264: 239: 235: 231: 164: 160: 152: 149: 148: 108: 102: 99: 56: 54: 40: 28: 17: 12: 11: 5: 363: 353: 352: 347: 342: 326: 325: 318: 311: 303: 300: 299: 278: 263: 262: 256:10.1.1.92.1635 232: 230: 227: 201: 200: 197: 193: 192: 181: 178: 175: 172: 167: 163: 159: 156: 146: 137: 136: 131: 130:Data structure 127: 126: 121: 110: 109: 45:. Please help 31: 29: 22: 15: 9: 6: 4: 3: 2: 362: 351: 348: 346: 343: 341: 338: 337: 335: 324: 319: 317: 312: 310: 305: 304: 298: 296: 292: 288: 284: 279: 276: 272: 271: 267: 257: 252: 248: 244: 237: 233: 226: 222: 220: 216: 215:approximation 212: 208: 198: 194: 176: 173: 170: 165: 161: 147: 145: 142: 138: 135: 132: 128: 125: 122: 118: 106: 95: 92: 88: 85: 81: 78: 74: 71: 67: 64: –  63: 59: 58:Find sources: 52: 48: 44: 38: 37: 36:single source 32:This article 30: 26: 21: 20: 295:expanding it 280: 265: 246: 242: 236: 223: 206: 204: 100: 90: 83: 76: 69: 57: 33: 144:performance 334:Categories 283:algorithms 229:References 141:Worst-case 73:newspapers 251:CiteSeerX 211:heuristic 174:⁡ 155:Θ 43:talk page 103:May 2024 196:Optimal 87:scholar 253:  89:  82:  75:  68:  60:  281:This 209:is a 134:Graph 120:Class 94:JSTOR 80:books 291:stub 205:The 66:news 285:or 213:or 171:log 49:by 336:: 249:. 245:. 199:No 322:e 315:t 308:v 297:. 259:. 247:1 180:) 177:N 166:2 162:N 158:( 105:) 101:( 91:· 84:· 77:· 70:· 53:. 39:.

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single source
talk page
improve this article
introducing citations to additional sources
"Multi-fragment algorithm"
news
newspapers
books
scholar
JSTOR
Approximation algorithm
Graph
Worst-case
performance
heuristic
approximation
travelling salesman problem
CiteSeerX
10.1.1.92.1635
Stub icon
algorithms
data structures
stub
expanding it
v
t
e
Categories
Travelling salesman problem

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