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The complexity of analog computation

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TLDR
It is suggested that any analog computer can be simulated efficiently (in polynomial time) by a digital computer from the assumption that P ≠ NP and from this assumption the operation of physical devices used for computation is drawn.
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This article is published in Mathematics and Computers in Simulation.The article was published on 1986-04-01 and is currently open access. It has received 188 citations till now. The article focuses on the topics: Optimization problem & Time complexity.

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Citations
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Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

TL;DR: In this paper, the authors considered factoring integers and finding discrete logarithms on a quantum computer and gave an efficient randomized algorithm for these two problems, which takes a number of steps polynomial in the input size of the integer to be factored.
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Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

Peter W. Shor
- 01 Jun 1999 - 
TL;DR: In this paper, the authors considered factoring integers and finding discrete logarithms, two problems that are generally thought to be hard on classical computers and that have been used as the basis of several proposed cryptosystems.
Journal ArticleDOI

On the Power of Quantum Computation

TL;DR: This work presents a problem of distinguishing between two fairly natural classes of functions, which can provably be solved exponentially faster in the quantum model than in the classical probabilistic one, when the function is given as an oracle drawn equiprobably from the uniform distribution on either class.
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Quantum information processing with superconducting circuits: a review

TL;DR: The time is ripe for describing some of the recent development of superconducting devices, systems and applications as well as practical applications of QIP, such as computation and simulation in Physics and Chemistry.
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Analyzing vision at the complexity level

TL;DR: This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together.
References
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Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Journal ArticleDOI

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI

On Computable Numbers, with an Application to the Entscheidungsproblem

TL;DR: This chapter discusses the application of the diagonal process of the universal computing machine, which automates the calculation of circle and circle-free numbers.
Frequently Asked Questions (13)
Q1. What are the contributions in "The complexity of analog computation †" ?

The authors next prove Strong Church ’ s Thesis for a class of analog computers described by well-behaved ordinary differential equations, which the authors can take as representing part of classical mechanics. 

The time required for the operation of this analog computer will depend on the parasitic capacitance of the circuit, which will determine the effective RC time constant of the circuit. 

He says:‘‘The rule of simulation that The authorwould like to have is that the number of computer elements required to simulate a large physical system is only to be proportional to the space-time volume of the physical system. 

Since the angular positions x 1 and x 2 always satisfy the constraints imposed by the stops, the maximum angular position of the z shaft will be the optimum solution of (5.1). 

If the norm of f(x) is bounded by the polynomial L k , the volume of the corresponding sphere in p-dimensional space is O(L pk ) . 

a proof of convergence for this kind of stochastic relaxation algorithm has only been obtained under the assumption of an annealing schedule involving logarithmically decreasing temperature [10]; the resulting algorithm requires exponential time which is in agreement with their prediction based on assuming Strong Church’s Thesis and P ≠ NP. 

The maximum magnitude of the second derivative of the solution vector over the interval [t 0 ,t f ] , is used as a measure of the resources required by the computation. 

The solution at time t f when the initial condition at time t 0 is y 0 and the precision constant ε determine an equivalence class of ‘‘output’’ vectors. 

In [18] the problem of finding a minimum-length interconnection network between given points in the plane is solved with movable and fixed pegs interconnected by strings; a locally optimal solution is obtained by pulling the strings. 

Turing has laid out a model for what a well-defined digital computation must be: it uses a finite set of symbols (without loss of generality {0,1}) to store information, it can be in only one of a finite set of states, and it operates by a finite set of rules for moving from state to state. 

A ‘‘physical digital computer’’ would allow encoding the value n for the variable x with k = O(logn) distinct electric fields, shaft angles, etc. 

The authors would argue that for the purposes of investigating the limitations on analog computation arising from computational complexity theory, the use of ‘‘idealized’’ analog computers whose physical operation corresponds precisely to its mathematical description is appropriate. 

If the wire lengths grow linearly with the number of edges | E|, the total capacitance seen by the voltage source will be no worse than proportional to | E| 2 .