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13
NPcomplete problems and physical reality
 ACM SIGACT News Complexity Theory Column, March. ECCC
, 2005
"... Can NPcomplete problems be solved efficiently in the physical universe? I survey proposals including soap bubbles, protein folding, quantum computing, quantum advice, quantum adiabatic algorithms, quantummechanical nonlinearities, hidden variables, relativistic time dilation, analog computing, Mal ..."
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Can NPcomplete problems be solved efficiently in the physical universe? I survey proposals including soap bubbles, protein folding, quantum computing, quantum advice, quantum adiabatic algorithms, quantummechanical nonlinearities, hidden variables, relativistic time dilation, analog computing, MalamentHogarth spacetimes, quantum gravity, closed timelike curves, and “anthropic computing. ” The section on soap bubbles even includes some “experimental ” results. While I do not believe that any of the proposals will let us solve NPcomplete problems efficiently, I argue that by studying them, we can learn something not only about computation but also about physics. 1
Algorithmic randomness, quantum physics, and incompleteness
 Proceedings of the Conference “Machines, Computations and Universality” (MCU’2004), number 3354 in Lecture Notes in Computer Science
, 2006
"... When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is almost certainly wrong. Arthur C. Clarke ..."
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Cited by 12 (2 self)
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When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is almost certainly wrong. Arthur C. Clarke
Limits on the Computational Power of Random Strings
"... Let C(x) andK(x) denote plain and prefix Kolmogorov complexity, respectively, and let RC and RK denote the sets of strings that are “random ” according to these measures; both RK and RC are undecidable. Earlier work has shown that every set in NEXP is in NP relative to both RK and RC, and that every ..."
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Cited by 5 (3 self)
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Let C(x) andK(x) denote plain and prefix Kolmogorov complexity, respectively, and let RC and RK denote the sets of strings that are “random ” according to these measures; both RK and RC are undecidable. Earlier work has shown that every set in NEXP is in NP relative to both RK and RC, and that every set in BPP is polynomialtime truthtable reducible to both RK and RC [ABK06a, BFKL10]. (All of these inclusions hold, no matter which “universal ” Turing machine one uses in the definitions of C(x) andK(x).) Since each machine U gives rise to a slightly different measure CU or KU, these inclusions can be stated as: • BPP ⊆ DEC ∩ ⋂ U
Kolmogorov Complexity, Circuits, and the Strength of Formal Theories of Arithmetic
"... Can complexity classes be characterized in terms of efficient reducibility to the (undecidable) set of Kolmogorovrandom strings? Although this might seem improbable, a series of papers has recently provided evidence that this may be the case. In particular, it is known that there is a class of prob ..."
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Can complexity classes be characterized in terms of efficient reducibility to the (undecidable) set of Kolmogorovrandom strings? Although this might seem improbable, a series of papers has recently provided evidence that this may be the case. In particular, it is known that there is a class of problems C defined in terms of polynomialtime truthtable reducibility to RK (the set of Kolmogorovrandom strings) that lies between BPP and PSPACE [4, 3]. In this paper, we investigate improving this upper bound from PSPACE to PSPACE ∩ P/poly. More precisely, we present a collection of true statements in the language of arithmetic, (each provable in ZF) and show that if these statements can be proved in certain extensions of Peano arithmetic, then BPP ⊆C⊆PSPACE ∩ P/poly. We conjecture that C is equal to P, and discuss the possibility this might be an avenue for trying to prove the equality of BPP and P.
Algorithmic Randomness and Computability
"... Abstract. We examine some recent work which has made significant progress in out understanding of algorithmic randomness, relative algorithmic randomness and their relationship with algorithmic computability and relative algorithmic computability. ..."
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Abstract. We examine some recent work which has made significant progress in out understanding of algorithmic randomness, relative algorithmic randomness and their relationship with algorithmic computability and relative algorithmic computability.
Curiouser and curiouser: The link between incompressibility and complexity
 In Proc. Computability in Europe (CiE), LNCS
, 2012
"... Abstract. This talk centers around some audacious conjectures that attempt to forge firm links between computational complexity classes and the study of Kolmogorov complexity. More specifically, let R denote the set of Kolmogorovrandom strings. Let BPP denote the class of problems that can be solve ..."
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Abstract. This talk centers around some audacious conjectures that attempt to forge firm links between computational complexity classes and the study of Kolmogorov complexity. More specifically, let R denote the set of Kolmogorovrandom strings. Let BPP denote the class of problems that can be solved with negligible error by probabilistic polynomialtime computations, and let NEXP denote the class of problems solvable in nondeterministic exponential time. Conjecture 1: NEXP = NP R. Conjecture 2: BPP is the class of problems nonadaptively polynomialtime reducible to R. These conjectures are not only audacious; they are obviously false! R is not a decidable set, and thus it is absurd to suggest that the class of problems reducible to it constitutes a complexity class. The absurdity fades if, for example, we interpret “NP R ” to be “the class of problems that are NPTuring reducible to R, no matter which universal machine we use in defining Kolmogorov complexity”. The lecture will survey the body of work (some of it quite recent) that suggests that, when interpreted properly, the conjectures may actually be true. 1
Randomness, Computation and Mathematics
"... Abstract. This article examines some of the recent advances in our understanding of algorithmic randomness. It also discusses connections with various areas of mathematics, computer science and other areas of science. Some questions and speculations will be discussed. 1 ..."
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Abstract. This article examines some of the recent advances in our understanding of algorithmic randomness. It also discusses connections with various areas of mathematics, computer science and other areas of science. Some questions and speculations will be discussed. 1
New Surprises from SelfReducibility
"... Abstract. Selfreducibility continues to give us new angles on attacking some of the fundamental questions about computation and complexity. 1 ..."
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Abstract. Selfreducibility continues to give us new angles on attacking some of the fundamental questions about computation and complexity. 1
• BPP ⊆ {A: A ≤ p
"... How powerful is the set of random strings? What can one say about a set A that is efficiently reducible to R, the set of Kolmogorovrandom strings? We present the first upper bound on the class of computable sets in P R and NP R. The two most widelystudied notions of Kolmogorov complexity are the “ ..."
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How powerful is the set of random strings? What can one say about a set A that is efficiently reducible to R, the set of Kolmogorovrandom strings? We present the first upper bound on the class of computable sets in P R and NP R. The two most widelystudied notions of Kolmogorov complexity are the “plain” complexity C(x) and “prefix ” complexity K(x); this gives rise to two common ways to define the set of random strings “R”: RC and RK. (Of course, each different choice of universal Turing machine U in the definition of C and K yields another variant RCU or RKU.) Previous work on the power of “R ” (for any of these variants) has shown