Www.dpmms.cam.ac.uk/~wtg10/2cultures.pdf. PS_cache/math/pdf/9404/9404236v1.pdf. There’s more to mathematics than rigour and proofs. The history of every major galactic civilization tends to pass through three distinct and recognizable phases, those of Survival, Inquiry and Sophistication, otherwise known as the How, Why, and Where phases. For instance, the first phase is characterized by the question ‘How can we eat?’ , the second by the question ‘Why do we eat?’ And the third by the question, ‘Where shall we have lunch?’
(Douglas Adams, “The Hitchhiker’s Guide to the Galaxy“) One can roughly divide mathematical education into three stages: The “pre-rigorous” stage, in which mathematics is taught in an informal, intuitive manner, based on examples, fuzzy notions, and hand-waving. The transition from the first stage to the second is well known to be rather traumatic, with the dreaded “proof-type questions” being the bane of many a maths undergraduate. It is of course vitally important that you know how to think rigorously, as this gives you the discipline to avoid many common errors and purge many misconceptions. (1) What is it like to have an understanding of very advanced mathematics. Www.math.ualberta.ca/~mss/misc/A Mathematician's Apology.pdf. A map of the Tricki | Tricki.
This is an attempt to give a quick guide to the top few levels of the Tricki. It may cease to be feasible when the Tricki gets bigger, but we might perhaps be able to automate additions to it. Clicking on arrows just to the right of the name of an article reveals its subarticles. If you want to hide the subarticles again, then you should click to the right of them rather than clicking on the name of one of the subarticles themselves, since otherwise you will follow a link to that subarticle. What kind of problem am I trying to solve? General problem-solving tips Front pages for different areas of mathematics How to use mathematical concepts and statements.
Mathematics.
Mathematics. Acme Klein Bottle. Specifications for ACME Klein Bottles. Specifications for ACME Klein Bottles No two Acme Klein Bottles are alike! Since Acme glassblowers individually hand craft each one, dimensions will vary and you may find occasional bubbles or streaks in the glass. These prove that your Acme Klein Bottle was made by real, 3-dimensional humans, adding to its unique status in a world of identical, machine made commodities. WARNING! Acme constructs each Klein Bottle from genuine Baryonic matter. Do not allow your Acme Klein Bottle to come in contact with antimatter or unpredictable results may occur. Acme cannot guarantee the dimensionality of the result. Although originating in the 4th dimension, Acme Klein Bottles are immersed (not embedded) in 3 dimensions, using special techniques known to students of advanced topology.
We construct our glass Acme Klein Bottles from materials which resist both oxidation and reduction. A Klein Bottle has zero volume, so we suggest that you do not use it as a personal flotation device.
Penrose Tiles. Google Ngram Viewer. Time complexity. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor. Since an algorithm's performance time may vary with different inputs of the same size, one commonly uses the worst-case time complexity of an algorithm, denoted as T(n), which is defined as the maximum amount of time taken on any input of size n. Time complexities are classified by the nature of the function T(n). For instance, an algorithm with T(n) = O(n) is called a linear time algorithm, and an algorithm with T(n) = O(2n) is said to be an exponential time algorithm.
Table of common time complexities[edit] The following table summarizes some classes of commonly encountered time complexities. Constant time[edit] Logarithmic time[edit] Polylogarithmic time[edit] and . P versus NP problem. Diagram of complexity classes provided that P≠NP. The existence of problems within NP but outside both P and NP-complete, under that assumption, was established by Ladner's theorem.[1] The P versus NP problem is a major unsolved problem in computer science.
Informally, it asks whether every problem whose solution can be quickly verified by a computer can also be quickly solved by a computer. It was essentially first mentioned in a 1956 letter written by Kurt Gödel to John von Neumann. Gödel asked whether a certain NP complete problem could be solved in quadratic or linear time.[2] The precise statement of the P=NP problem was introduced in 1971 by Stephen Cook in his seminal paper "The complexity of theorem proving procedures"[3] and is considered by many to be the most important open problem in the field.[4] It is one of the seven Millennium Prize Problems selected by the Clay Mathematics Institute to carry a US$1,000,000 prize for the first correct solution.
Context[edit] Computational complexity theory. Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage.
Closely related fields in theoretical computer science are analysis of algorithms and computability theory. Computational problems[edit] Problem instances[edit] Turing machine[edit] Previous monthly puzzles: May 2004. Previous monthly puzzles: June-July 2004. Numbers: Facts, Figures & Fiction. Click on cover for larger image Numbers: Facts, Figures & Fiction by Richard Phillips. Published by Badsey Publications. See sample pages: 24, 82, 103. Order the book direct from Badsey Publications price £12. In Australia it is sold by AAMT and in the US by Parkwest.
For those who need a hardback copy, a limited number of the old 1994 hardback edition are still available. Have you ever wondered how Room 101 got its name, or what you measure in oktas? This new edition has been updated with dozens of new articles, illustrations and photographs. Some press comments – "This entertaining and accessible book is even more attractive in its second edition...
" – Jennie Golding in The Mathematical Gazette "...tangential flights into maths, myth and mystery... " – Vivienne Greig in New Scientist ... and on the first edition – Contents – Primality Proving 2.1: Finding very small primes. For finding all the small primes, say all those less than 10,000,000,000; one of the most efficient ways is by using the Sieve of Eratosthenes (ca 240 BC): Make a list of all the integers less than or equal to n (greater than one) and strike out the multiples of all primes less than or equal to the square root of n, then the numbers that are left are the primes.
(See also our glossary page.) For example, to find all the odd primes less than or equal to 100 we first list the odd numbers from 3 to 100 (why even list the evens?) The first number is 3 so it is the first odd prime--cross out all of its multiples. Now the first number left is 5, the second odd prime--cross out all of its multiples. Repeat with 7 and then since the first number left, 11, is larger than the square root of 100, all of the numbers left are primes. Bressoud has a pseudocode implementation of this algorithm [Bressoud89, p19] and Riesel a PASCAL implementation [Riesel94, p6].
Prime number checker. THE LAST DAYS OF THE POLYMATH. People who know a lot about a lot have long been an exclusive club, but now they are an endangered species. Edward Carr tracks some down ... From INTELLIGENT LIFE Magazine, Autumn 2009 CARL DJERASSI can remember the moment when he became a writer. It was 1993, he was a professor of chemistry at Stanford University in California and he had already written books about science and about his life as one of the inventors of the Pill. Now he wanted to write a literary novel about writers’ insecurities, with a central character loosely modelled on Norman Mailer, Philip Roth and Gore Vidal.
His wife, Diane Middlebrook, thought it was a ridiculous idea. She was also a professor—of literature. Even at 85, slight and snowy-haired, Djerassi is a determined man. Eventually Djerassi got the bound galleys of his book. Diane Middlebrook died of cancer in 2007 and, as Djerassi speaks, her presence grows stronger. Carl Djerassi is a polymath. “To me, promiscuity is a way of flitting around.
An Introduction to Wavelets: What Do Some Wavelets Look Like? W hat do S ome W avelets L ook L ike? Wavelet transforms comprise an infinite set. The different wavelet families make different trade-offs between how compactly the basis functions are localized in space and how smooth they are. Some of the wavelet bases have fractal structure. The Daubechies wavelet family is one example (see Figure 3). This figure was generated using the WaveLab command: wave=MakeWavelet(2, -4, 'Daubechies', 4, 'Mother', 2048).
Within each family of wavelets (such as the Daubechies family) are wavelet subclasses distinguished by the number of coefficients and by the level of iteration. The number next to the wavelet name represents the number of vanishing moments (A stringent mathematical definition related to the number of wavelet coefficients) for the subclass of wavelet. Wave = MakeWavelet(2,-4,'Daubechies',6,'Mother', 2048); wave = MakeWavelet(2,-4,'Coiflet',3,'Mother', 2048); wave = MakeWavelet(0,0,'Haar',4,'Mother', 512); Table of mathematical symbols. When reading the list, it is important to recognize that a mathematical concept is independent of the symbol chosen to represent it. For many of the symbols below, the symbol is usually synonymous with the corresponding concept (ultimately an arbitrary choice made as a result of the cumulative history of mathematics), but in some situations a different convention may be used.
For example, depending on context, the triple bar "≡" may represent congruence or a definition. Further, in mathematical logic, numerical equality is sometimes represented by "≡" instead of "=", with the latter representing equality of well-formed formulas. In short, convention dictates the meaning. Each symbol is shown both in HTML, whose display depends on the browser's access to an appropriate font installed on the particular device, and in TeX, as an image. Guide[edit] This list is organized by symbol type and is intended to facilitate finding an unfamiliar symbol by its visual appearance.
Basic symbols[edit] Power law. An example power-law graph, being used to demonstrate ranking of popularity. To the right is the long tail, and to the left are the few that dominate (also known as the 80–20 rule). In statistics, a power law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a power of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four.[1] Empirical examples of power laws[edit] Properties of power laws[edit] Scale invariance[edit] One attribute of power laws is their scale invariance. . , scaling the argument by a constant factor causes only a proportionate scaling of the function itself.
That is, scaling by a constant simply multiplies the original power-law relation by the constant . And A power-law only if Universality[edit]