Showing posts with label mathematica. Show all posts
Showing posts with label mathematica. Show all posts

A New Kind of Science Review

A New Kind of Science
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This review took almost one year. Unlike many previous referees (rank them by Amazon.com's "most helpful" feature) I read all 1197 pages including notes. Just to make sure I won't miss the odd novel insight hidden among a million trivial platitudes.
On page 27 Wolfram explains "probably the single most surprising discovery I have ever made:" a simple program can produce output that seems irregular and complex.
This has been known for six decades. Every computer science (CS) student knows the dovetailer, a very simple 2 line program that systematically lists and executes all possible programs for a universal computersuch as a Turing machine (TM). It computes all computable patterns, including all those in Wolfram's book, embodies the well-known limits of computability, and is basis of uncountable CS exercises.
Wolfram does know (page 1119) Minsky's very simple universal TMs from the 1960s. Using extensive simulations, he finds a slightly simpler one. New science? Small addition to old science. On page 675 we find a particularly simple cellular automaton (CA) and Matthew Cook's universality proof(?). This might be the most interesting chapter. It reflects that today's PCs are more powerful systematic searchers for simple rules than those of 40 years ago. No new paradigm though.
Was Wolfram at least first to view programs as potential explanations of everything? Nope. That was Zuse. Wolfram mentions him in exactly one line (page 1026): "Konrad Zuse suggested that [the universe] could be a continuous CA." This is totally misleading. Zuse's 1967 paper suggested the universe is DISCRETELY computable, possibly on a DISCRETE CA just like Wolfram's. Wolfram's causal networks (CA's with variable toplogy, chapter 9) will run on any universal CA a la Ulam & von Neumann & Conway & Zuse. Page 715 explains Wolfram's "key unifying idea" of the "principle of computational equivalence:" all processes can be viewed as computations. Well, that's exactly what Zuse wrote 3 decades ago.
Chapter 9 (2nd law of thermodynamics) elaborates (without reference)on Zuse's old insight that entropy cannot really increase in deterministically computed systems, although it often SEEMS to increase. Wolfram extends Zuse's work by a tiny margin, using today's more powerful computers to perform experiments as suggested in Zuse's 1969 book. I find it embarassing how Wolfram tries to suggest it was him who shifted a paradigm, not the legendary Zuse.
Some reviews cite Wolfram's previous reputation as a physicist and software entrepreneur, giving him the benefit of the doubt instead of immediately dismissing him as just another plagiator. Zuse's reputation is in a different league though: He built world's very first general purpose computers (1935-1941), while Wolfram is just one of many creators of useful software (Mathematica). Remarkably, in his history of computing (page 1107) Wolfram appears to try to diminuish Zuse's contributions by only mentioning Aiken's later 1944 machine.
On page 465 ff (and 505 ff on multiway systems) Wolfram asks whether there is a simple program that computes the universe. Here he sounds like Schmidhuber in his 1997 paper "A Computer Scientist's View of Life, the Universe, and Everything." Schmidhuber applied the above-mentioned simple dovetailer to all computable universes. His widely known writings come out on top when you google for "computable universes" etc, so Wolfram must have known them too, for he read an "immense number of articles books and web sites" (page xii) and executed "more than a hundred thousand mouse miles" (page xiv). He endorses Schmidhuber's "no-CA-but-TM approach" (page 486, no reference) but not his suggestion of using Levin's asymptotically optimal program searcher (1973) to find our universe's code.
On page 469 we are told that the simplest program for the data is the most probable one. No mention of the very science based on this ancient principle: Solomonoff's inductive inference theory (1960-1978); recent optimality results by Merhav & Feder & Hutter. Following Schmidhuber's "algorithmic theories of everything" (2000), short world-explaining programs are necessarily more likely, provided the world is sampled from a limit-computable prior distribution. Compare Li & Vitanyi's excellent 1997 textbook on Kolmogorov complexity.
On page 628 ff we find a lot of words on human thinking and short programs. As if this was novel! Wolfram seems totally unaware of Hutter's optimal universal rational agents (2001) based on simple programs a la Solomonoff & Kolmogorov & Levin & Chaitin. Wolfram suggests his simple programs will contribute to fine arts (page 11), neither mentioning existing, widely used, very short, fractal-based programs for computing realistic images of mountains and plants, nor the only existing art form explicitly based on simple programs: Schmidhuber's low-complexity art.
Wolfram talks a lot about reversible CAs but little about Edward Fredkin & Tom Toffoli who pioneered this field. He ignores Wheeler's "it from bit," Tegmark & Greenspan & Petrov & Marchal's papers, Moravec & Kurzweil's somewhat related books, and Greg Egan's fun SF on CA-based universes (Permutation City, 1995).
When the book came out some non-expert journalists hyped it without knowing its contents. Then cognoscenti had a look at it and recognized it as a rehash of old ideas, plus pretty pictures. And the reviews got worse and worse. As far as I can judge, positive reviews were written only by people without basic CS education and little knowledge of CS history. Some biologists and even a few physicists initially were impressed because to them it really seemed new. Maybe Wolfram's switch from physics to CS explains why he believes his thoughts are radical, not just reinventions of the wheel.
But he does know Goedel and Zuse and Turing. He must see that his own work is minor in comparison. Why does he desparately try to convince us otherwise? When I read Wolfram's first praise of the originality of his own ideas I just had to laugh. The tenth time was annoying. The hundredth time was boring. And that was my final feeling when I laid down this extremely repetitive book:exhaustion and boredom. In hindsight I know I could have saved my time. But at least I can warn others.

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Foundations of Fluid Mechanics with Applications: Problem Solving Using Mathematica (Modeling and Simulation in Science, Engineering and Technology) Review

Foundations of Fluid Mechanics with Applications: Problem Solving Using Mathematica (Modeling and Simulation in Science, Engineering and Technology)
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This book presents the basic concepts of continuum mechanics. The material is presented in a tensor invariant form with a large number of problems with solutions. The book integrates the use of the computer algebra system Mathematica, and contains a large number of programs on the disk that will help clarify the concepts of continuum mechanics.(taken from the author)
Cheers

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This reference presents theory, methods and computations for fluid mechanics with Mathematica program examples.

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Computational Economics and Finance: Modeling and Analysis with Mathematica (Economic & Financial Modeling with Mathematica) (Vol 2) Review

Computational Economics and Finance: Modeling and Analysis with Mathematica (Economic and Financial Modeling with Mathematica) (Vol 2)
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For the reader well-versed in Mathematica and in economic theory, this book gives a fairly good overview of how Mathematica can be used to study mathematical economics and finance. It is also assumed in the articles in the book that the reader has a strong background in mathematics. Since the book was published in 1993, Mathematica has considerably expanded, with many new features that make some of the accompanying code in the book somewhat dated, but the notebooks can still be used beneficially.In addition, economic theory is currently making more use of symbolic programming, and financial analysis has exploded as an area which is now making heavy use of high-performance computing. Although Mathematica cannot compete from a performance standpoint with the needs of financial engineering, it still has an advantage from a didactic standpoint. I did not read all of the articles in the book, so my comments will be limited to the ones that I did.
The article on "Mathematica and Diffusions" is an overview of how to use Mathematica to do stochastic calculus. The Ito calculus is reviewed briefly, and the authors begin with constructing a Weiner process. The Mathematica package they employ and on the disk accompanying the book is not discussed in detail, but is merely used to simulate realizations of the process. Readers who want a more in-depth view will have to go over the code themselves. The authors use the package to generate realizations of Weiner processes that are correlated with each other, and show this correlation via Mathematica graphics. The Black-Scholes formula is derived using the standard self-financing trading strategy and ignoring transaction costs and dividends. The algebraic manipulations are done with Mathematica, and this obscures (a little) the underlying concepts behind the derivation of this important formula. Since data structures in Mathematica are essentially lists, the authors outline the construction of the data structure that could be used to represent a diffusion, namely a list consisting of five terms: the diffusion, Weiner process name, expression for the drift and dispersion, and the initial value. For the reader familiar with OO-programming, accessor functions are used to extract the components of this data structure. This is a nice move by the authors, for it is an example of how Mathematica can be used to emulate OO-programming.
The article "Itovsn3: Doing Stochastic Calculus with Mathematica" is an overview of how to use the Itovsn3 package that is on the disk to implement Ito calculus. It is assumed that the reader has a background in stochastic calculus, since the author does not give a review. However, semimartingales, so important to those working in financial engineering, are discussed and their statistical behavior described using Mathematica. The Ito formula is presented as a semimartingale-type decomposition for smooth function of Brownian motion and the author shows using Mathematica plots how the higher order terms in the second-order Taylor expansion vanish asymptotically. This article is not merely Mathematica code for Ito calculus, for the author gives an example of how to use the package in a hedging problem.
The article "Option Valuation" is a more detailed overview of how to use Mathematica in the context of the Black-Scholes model to perform options valuation and risk management. Heavy use is made of the graphics capability of Mathematica to illustrate how option values change as a function of stock price and time of expiration. The author also shows how Mathematica can be used as a OO-language to treat options as self-contained objects with accessor functions. He does however state that Mathematica does not live up to the OO toolkits available elsewhere, contrary to my experience. He closes the article with a consideration of how to use Mathematica to value options that can be exercised before expiry, the binomial model playing the central role in the discussion. It is here in particular that the performance of Mathematica is readily felt. The numerical number-crunching needed to do the calculations in these types of models cannot be done in Mathematica efficiently and profitably.
The article "Time Series Models and Mathematica" gives a general treatment on how Mathematica can be used to study ARIMA models for time series. Mathematica is used more interactively than the other articles and the visualization obtained is quite nice in giving the reader insight into such concepts as the moving average and the spectral density function. The author shows how to estimate the spectral density function and why periodogram techniques fall short in this estimation. I would have liked to see other techniques for studying time series discussed, such as neural networks and hidden Markov models, but the author does do a fairly good job with the ARIMA models.

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As with the first volume, Volume Two of Economic and Financial Modeling with Mathematica is edited by Hal Varian, and its contributors are carefully selected by him to assure a high quality, practical work reflecting the efforts and expertise of an international cadre of Mathematicausers from the economic, financial, investments, quantitative business and operations research communities.

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Modern Introduction to Surface Plasmons: Theory, Mathematica Modeling, and Applications Review

Modern Introduction to Surface Plasmons: Theory, Mathematica Modeling, and Applications
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This book is ok. But not that great because I felt they have gone through the derivations bit fast. More of a cookbook.

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Introducing graduate students in physics, optics, materials science and electrical engineering to surface plasmons, this book also covers guided modes at planar interfaces of metamaterials with negative refractive index. The physics of localized and propagating surface plasmons, on planar films, gratings, nanowires and nanoparticles, is developed using both analytical and numerical techniques. Guided modes at the interfaces between materials with any combination of positive or negative permittivity and permeability are analyzed in a systematic manner. Applications of surface plasmon physics are described, including near-field transducers in heat-assisted magnetic recording and biosensors. Resources at www.cambridge.org/9780521767170 include Mathematica code to generate figures from the book, color versions of many figures, and extended discussion of topics such as vector diffraction theory.

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