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Advice (complexity)

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163:. Simulating a Turing machine with advice is no more complicated than simulating an ordinary machine, since the advice string can be incorporated into the circuit. 147:) deciding the problem, we can use a Turing machine that interprets the advice string as a description of the circuit. Then, given the description of 474: 296:
are the same, i.e. nondeterministic logarithmic space computation with advice can be made unambiguous. This may be proved using an
496: 159:. The other direction uses a simulation of a polynomial-time Turing machine by a polynomial-size circuit as in one proof of 444: 358: 20: 268:
function is computable with an advice of length 2 and advice of more than exponential length is not meaningful.
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is sometimes defined as the class of decision problems solvable by polynomial size Boolean circuits, or by
471: 234: 396: 171: 260:. If we are allowed an advice of length 2, we can use it to encode whether each input of length 391: 348: 454: 413: 368: 8: 323: 191: 429: 155:) as the advice, the machine will correctly decide the problem on all inputs of length 440: 354: 160: 450: 409: 401: 364: 186: 40: 36: 478: 436: 297: 247: 195: 128: 194:
problems, such as the unary version of every undecidable problem, including the
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Reinhardt, Klaus; Allender, Eric (2000). "Making nondeterminism unambiguous".
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Computational input that relies on the length but not content of the input
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Advice classes can be defined for other resource bounds instead of
435:. Texts in Theoretical Computer Science. An EATCS Series. Berlin: 135:. One direction of the equivalence is easy to see. If, for every 123:
is equal to the class of decision problems such that, for every
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polynomial time Turing machine with an advice of length
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correctly deciding the problem on all inputs of length
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The most common complexity class involving advice is
426: 428: 427:Hemaspaandra, Lane A.; Ogihara, Mitsunori (2002). 381: 488: 322:, then the polynomial time hierarchy collapses ( 264:is contained in the language. Therefore, any 139:, there is a polynomial size Boolean circuit 54:if there is a polynomial time Turing machine 35:of the input, but not on the input itself. A 353:, Cambridge University Press, p. 113, 350:Computational Complexity: A Modern Approach 190:(Adleman's theorem). It also contains some 86:correctly decides the problem on the input 343: 198:. Because of that, it is not contained in 395: 31:that is allowed to depend on the length 489: 58:with the following property: for any 281:with a polynomial amount of advice. 13: 14: 508: 127:, there exists a polynomial size 497:Computational complexity theory 431:The complexity theory companion 21:computational complexity theory 461: 420: 375: 337: 1: 330: 245:) gives the complexity class 166:Because of this equivalence, 62:, there is an advice string 7: 172:polynomial-size non-uniform 115:) can be any polynomial in 74:) such that, for any input 10: 513: 406:10.1137/S0097539798339041 233:. For example, taking a 284:Known results include: 27:is an extra input to a 347:; Barak, Boaz (2009), 279:deterministic logspace 271:Similarly, the class 107:where advice length 324:Karp-Lipton theorem 477:2019-08-05 at the 277:can be defined as 174:Boolean circuits. 303:It is known that 235:non-deterministic 504: 481: 472:A Little Theorem 465: 459: 458: 434: 424: 418: 417: 399: 390:(4): 1118–1131. 379: 373: 371: 341: 318:is contained in 307:is contained in 41:complexity class 37:decision problem 512: 511: 507: 506: 505: 503: 502: 501: 487: 486: 485: 484: 479:Wayback Machine 466: 462: 447: 437:Springer-Verlag 425: 421: 380: 376: 361: 342: 338: 333: 298:isolation lemma 196:halting problem 129:Boolean circuit 17: 12: 11: 5: 510: 500: 499: 483: 482: 460: 445: 419: 397:10.1.1.55.3203 384:SIAM J. Comput 374: 359: 345:Arora, Sanjeev 335: 334: 332: 329: 328: 327: 312: 301: 180:contains both 161:Cook's theorem 82:, the machine 29:Turing machine 15: 9: 6: 4: 3: 2: 509: 498: 495: 494: 492: 480: 476: 473: 469: 468:Lance Fortnow 464: 456: 452: 448: 446:3-540-67419-5 442: 438: 433: 432: 423: 415: 411: 407: 403: 398: 393: 389: 385: 378: 370: 366: 362: 360:9780521424264 356: 352: 351: 346: 340: 336: 325: 321: 317: 313: 310: 306: 302: 299: 295: 291: 287: 286: 285: 282: 280: 276: 275: 269: 267: 263: 259: 257: 253: 249: 244: 240: 236: 232: 227: 225: 221: 217: 213: 209: 205: 201: 197: 193: 189: 188: 183: 179: 175: 173: 169: 164: 162: 158: 154: 150: 146: 142: 138: 134: 130: 126: 122: 118: 114: 110: 106: 105: 99: 97: 93: 89: 85: 81: 77: 73: 69: 65: 61: 57: 53: 51: 47: 42: 38: 34: 30: 26: 25:advice string 22: 463: 430: 422: 387: 383: 377: 349: 339: 319: 315: 308: 304: 293: 289: 288:The classes 283: 272: 270: 265: 261: 255: 251: 246: 242: 238: 230: 228: 223: 219: 215: 207: 203: 185: 181: 177: 176: 167: 165: 156: 152: 148: 144: 140: 136: 132: 124: 120: 116: 112: 108: 102: 100: 95: 91: 87: 83: 79: 75: 71: 67: 63: 59: 55: 49: 45: 43: 32: 24: 18: 222:)) for any 192:undecidable 455:0993.68042 414:0947.68063 369:1193.68112 331:References 78:of length 66:of length 39:is in the 392:CiteSeerX 309:NEXP/poly 491:Category 475:Archived 90:, given 294:UL/poly 290:NL/poly 266:boolean 453:  443:  412:  394:  367:  357:  320:P/poly 305:coNEXP 274:L/poly 210:)) or 178:P/poly 168:P/poly 121:P/poly 104:P/poly 212:NTIME 200:DTIME 23:, an 441:ISBN 355:ISBN 292:and 184:and 94:and 451:Zbl 410:Zbl 402:doi 365:Zbl 314:If 187:BPP 19:In 493:: 470:, 449:. 439:. 408:. 400:. 388:29 386:. 363:, 326:). 316:NP 248:NP 226:. 119:. 98:. 44:P/ 457:. 416:. 404:: 372:. 311:. 300:. 262:n 258:) 256:n 254:( 252:f 250:/ 243:n 241:( 239:f 231:P 224:f 220:n 218:( 216:f 214:( 208:n 206:( 204:f 202:( 182:P 157:n 153:n 151:( 149:A 145:n 143:( 141:A 137:n 133:n 125:n 117:n 113:n 111:( 109:f 96:A 92:x 88:x 84:M 80:n 76:x 72:n 70:( 68:f 64:A 60:n 56:M 52:) 50:n 48:( 46:f 33:n

Index

computational complexity theory
Turing machine
decision problem
complexity class
P/poly
Boolean circuit
Cook's theorem
polynomial-size non-uniform
BPP
undecidable
halting problem
DTIME
NTIME
non-deterministic
NP
L/poly
deterministic logspace
isolation lemma
Karp-Lipton theorem
Arora, Sanjeev
Computational Complexity: A Modern Approach
ISBN
9780521424264
Zbl
1193.68112
CiteSeerX
10.1.1.55.3203
doi
10.1137/S0097539798339041
Zbl

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