Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma
Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that P...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shaltiel, Ronen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Basel AG 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Computational complexity - Cham (ZG) : Springer International Publishing AG, 1991, 20(2011), 1 vom: 19. Apr. |
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| Übergeordnetes Werk: |
volume:20 ; year:2011 ; number:1 ; day:19 ; month:04 |
| Links: |
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| DOI / URN: |
10.1007/s00037-011-0006-4 |
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| Katalog-ID: |
SPR000641782 |
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| 520 | |a Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. | ||
| 650 | 4 | |a Derandomization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a randomness extractors |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a pseudorandomness |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a communication complexity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a circuit complexity |7 (dpeaa)DE-He213 | |
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| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00037-011-0006-4 |z lizenzpflichtig |3 Volltext |
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article:14208954:2011----::ekeadmztoowaagrtmepiiv |
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2011 |
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2011 |
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10.1007/s00037-011-0006-4 doi (DE-627)SPR000641782 (SPR)s00037-011-0006-4-e DE-627 ger DE-627 rakwb eng Shaltiel, Ronen verfasserin aut Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Basel AG 2011 Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. Derandomization (dpeaa)DE-He213 randomness extractors (dpeaa)DE-He213 pseudorandomness (dpeaa)DE-He213 communication complexity (dpeaa)DE-He213 circuit complexity (dpeaa)DE-He213 Enthalten in Computational complexity Cham (ZG) : Springer International Publishing AG, 1991 20(2011), 1 vom: 19. Apr. (DE-627)254638856 (DE-600)1463001-1 1420-8954 nnns volume:20 year:2011 number:1 day:19 month:04 https://dx.doi.org/10.1007/s00037-011-0006-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2011 1 19 04 |
| spelling |
10.1007/s00037-011-0006-4 doi (DE-627)SPR000641782 (SPR)s00037-011-0006-4-e DE-627 ger DE-627 rakwb eng Shaltiel, Ronen verfasserin aut Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Basel AG 2011 Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. Derandomization (dpeaa)DE-He213 randomness extractors (dpeaa)DE-He213 pseudorandomness (dpeaa)DE-He213 communication complexity (dpeaa)DE-He213 circuit complexity (dpeaa)DE-He213 Enthalten in Computational complexity Cham (ZG) : Springer International Publishing AG, 1991 20(2011), 1 vom: 19. Apr. (DE-627)254638856 (DE-600)1463001-1 1420-8954 nnns volume:20 year:2011 number:1 day:19 month:04 https://dx.doi.org/10.1007/s00037-011-0006-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2011 1 19 04 |
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10.1007/s00037-011-0006-4 doi (DE-627)SPR000641782 (SPR)s00037-011-0006-4-e DE-627 ger DE-627 rakwb eng Shaltiel, Ronen verfasserin aut Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Basel AG 2011 Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. Derandomization (dpeaa)DE-He213 randomness extractors (dpeaa)DE-He213 pseudorandomness (dpeaa)DE-He213 communication complexity (dpeaa)DE-He213 circuit complexity (dpeaa)DE-He213 Enthalten in Computational complexity Cham (ZG) : Springer International Publishing AG, 1991 20(2011), 1 vom: 19. Apr. (DE-627)254638856 (DE-600)1463001-1 1420-8954 nnns volume:20 year:2011 number:1 day:19 month:04 https://dx.doi.org/10.1007/s00037-011-0006-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2011 1 19 04 |
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10.1007/s00037-011-0006-4 doi (DE-627)SPR000641782 (SPR)s00037-011-0006-4-e DE-627 ger DE-627 rakwb eng Shaltiel, Ronen verfasserin aut Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Basel AG 2011 Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. Derandomization (dpeaa)DE-He213 randomness extractors (dpeaa)DE-He213 pseudorandomness (dpeaa)DE-He213 communication complexity (dpeaa)DE-He213 circuit complexity (dpeaa)DE-He213 Enthalten in Computational complexity Cham (ZG) : Springer International Publishing AG, 1991 20(2011), 1 vom: 19. Apr. (DE-627)254638856 (DE-600)1463001-1 1420-8954 nnns volume:20 year:2011 number:1 day:19 month:04 https://dx.doi.org/10.1007/s00037-011-0006-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2011 1 19 04 |
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10.1007/s00037-011-0006-4 doi (DE-627)SPR000641782 (SPR)s00037-011-0006-4-e DE-627 ger DE-627 rakwb eng Shaltiel, Ronen verfasserin aut Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Basel AG 2011 Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. Derandomization (dpeaa)DE-He213 randomness extractors (dpeaa)DE-He213 pseudorandomness (dpeaa)DE-He213 communication complexity (dpeaa)DE-He213 circuit complexity (dpeaa)DE-He213 Enthalten in Computational complexity Cham (ZG) : Springer International Publishing AG, 1991 20(2011), 1 vom: 19. Apr. (DE-627)254638856 (DE-600)1463001-1 1420-8954 nnns volume:20 year:2011 number:1 day:19 month:04 https://dx.doi.org/10.1007/s00037-011-0006-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2011 1 19 04 |
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This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. 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Shaltiel, Ronen |
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Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma Derandomization (dpeaa)DE-He213 randomness extractors (dpeaa)DE-He213 pseudorandomness (dpeaa)DE-He213 communication complexity (dpeaa)DE-He213 circuit complexity (dpeaa)DE-He213 |
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weak derandomization of weak algorithms: explicit versions of yao’s lemma |
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Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma |
| abstract |
Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. © Springer Basel AG 2011 |
| abstractGer |
Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. © Springer Basel AG 2011 |
| abstract_unstemmed |
Abstract A simple averaging argument shows that given a randomized algorithm A and a function f such that for every input x, Pr[A(x) = f(x)] ≥ 1 − ρ (where the probability is over the coin tosses of A), there exists a non-uniform deterministic algorithm B “of roughly the same complexity” such that Pr[B(x) = f(x)] ≥ 1 − ρ (where the probability is over a uniformly chosen input x). This implication is often referred to as “the easy direction of Yao’s lemma” and can be thought of as “weak derandomization” in the sense that B is deterministic but only succeeds on most inputs. The implication follows as there exists a fixed value r′ for the random coins of A such that “hardwiring r′ into A” produces a deterministic algorithm B. However, this argument does not give a way to explicitly construct B. In this paper, we consider the task of proving uniform versions of the implication above. That is, how to explicitly construct a deterministic algorithm B when given a randomized algorithm A. We prove such derandomization results for several classes of randomized algorithms. These include randomized communication protocols, randomized decision trees (here we improve a previous result by Zimand), randomized streaming algorithms, and randomized algorithms computed by polynomial-size constant-depth circuits. Our proof uses an approach suggested by Goldreich and Wigderson and “extracts randomness from the input”. We introduce a new type of (seedless) extractors that extract randomness from distributions that are “recognizable” by the given randomized algorithm. We show that such extractors produce randomness that is in some sense not correlated with the input. © Springer Basel AG 2011 |
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Weak Derandomization of Weak Algorithms: Explicit Versions of Yao’s Lemma |
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|
| score |
7.40211 |