10 MAR 2019 by ideonexus

 Null Move

Called the "null move" technique, it tells the engine to "pass" for one side. That is, to evaluate a position as if one player could make two moves in a row. If the position has not improved even after moving twice, then it can be assumed that the first move is a dud and can be quickly discarded from the search tree, reducing its size and making the search more efficient. Null moves were used in some of the earliest chess programs, including the Soviet Kaissa. It's elegant and a little ironic...
Folksonomies: algorithms
Folksonomies: algorithms
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04 NOV 2018 by ideonexus

 A Computer Algorithm for Randomization

Back in the early days of computers, one of the more popular methods of generating a sequence of random numbers was to employ the following scheme: 1. Choose a starting number between 0 and 1. 2. Multiply the starting number by 4 ("stretch" it). Subtract 4 times the square of the starting number from the quantity obtained in step 2 ("fold" the interval back on itself in order to keep the final result in the same range). 3.Given a starting number between 0 and 1, we can use the proce-dure‚Ä...
Folksonomies: algorithms randomization
Folksonomies: algorithms randomization
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From John Casti.

05 MAR 2015 by ideonexus

 The Monte Carlo Method and Evolutionary Algorithms

Back then, I thought of one thing: Have you heard of the Monte Carlo method? Ah, it’s a computer algorithm often used for calculating the area of irregular shapes. Specifically, the software puts the figure of interest in a figure of known area, such as a circle, and randomly strikes it with many tiny balls, never targeting the same spot twice. After a large number of balls, the proportion of balls that fall within the irregular shape compared to the total number of balls used to hit the ci...
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03 DEC 2013 by ideonexus

 Algorithm that Presents Opposing Viewpoints

Social networks allow people to connect with each other and have conversations on a wide variety of topics. However, users tend to connect with like-minded people and read agreeable information, a behavior that leads to group polarization. Motivated by this scenario, we study how to take advantage of partial homophily to suggest agreeable content to users authored by people with opposite views on sensitive issues. We introduce a paradigm to present a data portrait of users, in which their cha...
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Intended to break people out of their protective circles that generate extreme viewpoints, it presents opposing viewpoints that won't offend. Tricky.

29 SEP 2013 by ideonexus

 The World is a Function

Kakeru Seki: A fact is somehow related to another fact. Unless you understand these relationships, you won't be a real reporter. Noriko Hikima: True journalism! Kakeru Seki: Well, you majored in the humanities. Noriko Hikima: Yes! That's true--I've studied literature since I was in high school. Kakeru Seki: You havea lot of catching up to do, then. Let's begin with functions. Noriko Hikima: Fu...functions? Math? What? Kakeru Seki: When one thing changes, it influences another thing. A ...
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As a programmer I know that algorithms can relate to the real world, and since these algorithms are constructed in computers that ultimately run on bits and boolean logic, then the real world may be imagined to deconstruct to pure mathematics.

29 MAY 2013 by ideonexus

 Simple Explanation of Big O Notation

The simplest definition I can give for Big-O notation is this: Big-O notation is a relative representation of the complexity of an algorithm. There are some important and deliberately chosen words in that sentence: relative: you can only compare apples to apples. You can't compare an algorithm to do arithmetic multiplication to an algorithm that sorts a list of integers. But two algorithms that do arithmetic operations (one multiplication, one addition) will tell you something meaningful; re...
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One of the best, down-to-Earth explanations of a concept that can get incredibly complex.

29 MAY 2013 by ideonexus

 Simple Explanation of Big O Part II

So if you want to find a name in a phone book of a million names you can actually find any name by doing this at most 20 times. In comparing search algorithms we decide that this comparison is our 'n'. For a phone book of 3 names it takes 2 comparisons (at most).For 7 it takes at most 3.For 15 it takes 4....For 1,000,000 it takes 20. That is staggeringly good isn't it? In Big-O terms this is O(log n) or logarithmic complexity. Now the logarithm in question could be ln (base e), log10, l...
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Second part of the explanation.