Noise stability is computable and approximately low-dimensional

Questions of noise stability play an important role in hardness of approximation in computer science as well as in the theory of voting. In many applications, the goal is to find an optimizer of noise stability among all possible partitions of R[superscript n] for n ≥ 1 to k parts with given Gaussian measures μ[superscript 1], . . . , μ[superscript k]. We call a partition ϵ-optimal, if its noise stability is optimal up to an additive ϵ. In this paper, we give an explicit, computable function n(ϵ) such that an ϵ-optimal partition exists in R[superscript n(ϵ)]. This result has implications for the computability of certain problems in non-interactive simulation, which are addressed in a subsequent work. Keywords: Gaussian noise stability; Plurality is stablest; Ornstein Uhlenbeck operatorNational Science Foundation (U.S.) (Award CCF 1320105)United States. Office of Naval Research (Grant N00014-16-1-2227

Non interactive simulation of correlated distributions is decidable

A basic problem in information theory is the following: Let $\mathbf{P} = (\mathbf{X}, \mathbf{Y})$ be an arbitrary distribution where the marginals $\mathbf{X}$ and $\mathbf{Y}$ are (potentially) correlated. Let Alice and Bob be two players where Alice gets samples $\{x_i\}_{i \ge 1}$ and Bob gets samples $\{y_i\}_{i \ge 1}$ and for all $i$, $(x_i, y_i) \sim \mathbf{P}$. What joint distributions $\mathbf{Q}$ can be simulated by Alice and Bob without any interaction? Classical works in information theory by G{\'a}cs-K{\"o}rner and Wyner answer this question when at least one of $\mathbf{P}$ or $\mathbf{Q}$ is the distribution on $\{0,1\} \times \{0,1\}$ where each marginal is unbiased and identical. However, other than this special case, the answer to this question is understood in very few cases. Recently, Ghazi, Kamath and Sudan showed that this problem is decidable for $\mathbf{Q}$ supported on $\{0,1\} \times \{0,1\}$. We extend their result to $\mathbf{Q}$ supported on any finite alphabet. We rely on recent results in Gaussian geometry (by the authors) as well as a new \emph{smoothing argument} inspired by the method of \emph{boosting} from learning theory and potential function arguments from complexity theory and additive combinatorics.Comment: The reduction for non-interactive simulation for general source distribution to the Gaussian case was incorrect in the previous version. It has been rectified no

Making dynamic modelling effective in economics

Mathematics has been extremely effective in physics, but not in economics beyond finance. To establish economics as science we should follow the Galilean method and try to deduce mathematical models of markets from empirical data, as has been done for financial markets. Financial markets are nonstationary. This means that 'value' is subjective. Nonstationarity also means that the form of the noise in a market cannot be postulated a priroi, but must be deduced from the empirical data. I discuss the essence of complexity in a market as unexpected events, and end with a biological speculation about market growth.Economics; fniancial markets; stochastic process; Markov process; complex systems

How Quantum Computers Fail: Quantum Codes, Correlations in Physical Systems, and Noise Accumulation

The feasibility of computationally superior quantum computers is one of the most exciting and clear-cut scientific questions of our time. The question touches on fundamental issues regarding probability, physics, and computability, as well as on exciting problems in experimental physics, engineering, computer science, and mathematics. We propose three related directions towards a negative answer. The first is a conjecture about physical realizations of quantum codes, the second has to do with correlations in stochastic physical systems, and the third proposes a model for quantum evolutions when noise accumulates. The paper is dedicated to the memory of Itamar Pitowsky.Comment: 16 page

Evaluating Data Assimilation Algorithms

Data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given the observations, plays a central conceptual role. The aim of this paper is to use this Bayesian posterior probability distribution as a gold standard against which to evaluate various commonly used data assimilation algorithms. A key aspect of geophysical data assimilation is the high dimensionality and low predictability of the computational model. With this in mind, yet with the goal of allowing an explicit and accurate computation of the posterior distribution, we study the 2D Navier-Stokes equations in a periodic geometry. We compute the posterior probability distribution by state-of-the-art statistical sampling techniques. The commonly used algorithms that we evaluate against this accurate gold standard, as quantified by comparing the relative error in reproducing its moments, are 4DVAR and a variety of sequential filtering approximations based on 3DVAR and on extended and ensemble Kalman filters. The primary conclusions are that: (i) with appropriate parameter choices, approximate filters can perform well in reproducing the mean of the desired probability distribution; (ii) however they typically perform poorly when attempting to reproduce the covariance; (iii) this poor performance is compounded by the need to modify the covariance, in order to induce stability. Thus, whilst filters can be a useful tool in predicting mean behavior, they should be viewed with caution as predictors of uncertainty. These conclusions are intrinsic to the algorithms and will not change if the model complexity is increased, for example by employing a smaller viscosity, or by using a detailed NWP model

Importance sampling for thermally induced switching and non-switching probabilities in spin-torque magnetic nanodevices

Spin-transfer torque magnetoresistive random access memory is a potentially transformative technology in the non-volatile memory market. Its viability depends, in part, on one's ability to predictably induce or prevent switching; however, thermal fluctuations cause small but important errors in both the writing and reading processes. Computing these very small probabilities for magnetic nanodevices using naive Monte Carlo simulations is essentially impossible due to their slow statistical convergence, but variance reduction techniques can offer an effective way to improve their efficiency. Here, we provide an illustration of how importance sampling can be efficiently used to estimate low read and write soft error rates of macrospin and coupled-spin systems.Comment: 11 pages, 14 figure