How to write algorithms in word05.12.2020
Nov 07, · Top 20 Greedy Algorithms Interview Questions ‘Practice Problems’ on Greedy Algorithms; Practice Questions on Huffman Encoding ‘Quiz’ on Greedy Algorithms. If you like GeeksforGeeks and would like to contribute, you can also write an article and mail your article to [email protected] Dec 31, · We will cover use cases involving paragraphs, fonts, headers, footers, tables, images, write, read, parse and updating the document. Before exploring use cases, let’s cover basic concepts involving the Apache POI library. HWPF (Horrible Word Processor Format) These APIs are for older Word 6 and Word 95 file formats,.doc documents.
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Pythonic Data Structures and Algorithms Minimal and clean example implementations of data structures and algorithms in Python 3. Contributing Thanks for your interest in contributing! About Minimal examples of data structures and algorithms in Python Topics python search tree algorithm algoeithms algorithms graph competitive-programming sort.
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May 08, · Approach: 1) Open the file which contains vitoriayvitorianos.com example, file named “vitoriayvitorianos.com” contains a string “geeks for geeks”. 2) Create a filestream variable to store file content. 3) Extract and print words from the file stream into a string variable via while loop. Apr 22, · GitHub is where people build software. More than 56 million people use GitHub to discover, fork, and contribute to over million projects. Pythonic Data Structures and Algorithms. Minimal and clean example implementations of data structures and algorithms in Python 3. Contributing. Thanks for your interest in contributing! There are many ways to contribute to this project. Get started here. Tests Use unittest. For running all tests write down: $ python3 -m unittest discover tests.
As an effective method , an algorithm can be expressed within a finite amount of space and time,  and in a well-defined formal language  for calculating a function. The transition from one state to the next is not necessarily deterministic ; some algorithms, known as randomized algorithms , incorporate random input.
The concept of algorithm has existed since antiquity. Arithmetic algorithms, such as a division algorithm , was used by ancient Babylonian mathematicians c.
Later formalizations were framed as attempts to define " effective calculability "  or "effective method". The word 'algorithm' has its roots in Latinizing the nisba, indicating his geographic origin, of the name of Persian mathematician Muhammad ibn Musa al-Khwarizmi to algorismus. About , al-Khwarizmi wrote an Arabic language treatise on the Hindu—Arabic numeral system , which was translated into Latin during the 12th century.
In English, it was first used in about and then by Chaucer in English adopted the French term, but it wasn't until the late 19th century that "algorithm" took on the meaning that it has in modern English. Another early use of the word is from , in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu.
It begins with:. Algorism is the art by which at present we use those Indian figures, which number two times five. The poem is a few hundred lines long and summarizes the art of calculating with the new styled Indian dice Tali Indorum , or Hindu numerals.
An informal definition could be "a set of rules that precisely defines a sequence of operations",  [ need quotation to verify ] which would include all computer programs including programs that do not perform numeric calculations , and for example any prescribed bureaucratic procedure  or cook-book recipe. In general, a program is only an algorithm if it stops eventually  - even though infinite loops may sometimes prove desirable.
A prototypical example of an algorithm is the Euclidean algorithm , which is used to determine the maximum common divisor of two integers; an example there are others is described by the flowchart above and as an example in a later section.
But humans can do something equally useful, in the case of certain enumerably infinite sets: They can give explicit instructions for determining the n th member of the set , for arbitrary finite n. Such instructions are to be given quite explicitly, in a form in which they could be followed by a computing machine , or by a human who is capable of carrying out only very elementary operations on symbols.
An "enumerably infinite set" is one whose elements can be put into one-to-one correspondence with the integers. Thus Boolos and Jeffrey are saying that an algorithm implies instructions for a process that "creates" output integers from an arbitrary "input" integer or integers that, in theory, can be arbitrarily large.
The concept of algorithm is also used to define the notion of decidability —a notion that is central for explaining how formal systems come into being starting from a small set of axioms and rules. In logic , the time that an algorithm requires to complete cannot be measured, as it is not apparently related to the customary physical dimension. From such uncertainties, that characterize ongoing work, stems the unavailability of a definition of algorithm that suits both concrete in some sense and abstract usage of the term.
Algorithms are essential to the way computers process data. Many computer programs contain algorithms that detail the specific instructions a computer should perform—in a specific order—to carry out a specified task, such as calculating employees' paychecks or printing students' report cards.
Thus, an algorithm can be considered to be any sequence of operations that can be simulated by a Turing-complete system. Authors who assert this thesis include Minsky , Savage and Gurevich :.
Minsky: "But we will also maintain, with Turing … that any procedure which could "naturally" be called effective, can, in fact, be realized by a simple machine. Although this may seem extreme, the arguments … in its favor are hard to refute".
Turing machines can define computational processes that do not terminate. The informal definitions of algorithms generally require that the algorithm always terminates. This requirement renders the task of deciding whether a formal procedure is an algorithm impossible in the general case—due to a major theorem of computability theory known as the halting problem.
Typically, when an algorithm is associated with processing information, data can be read from an input source, written to an output device and stored for further processing. Stored data are regarded as part of the internal state of the entity performing the algorithm. In practice, the state is stored in one or more data structures. For some of these computational processes, the algorithm must be rigorously defined: specified in the way it applies in all possible circumstances that could arise.
This means that any conditional steps must be systematically dealt with, case-by-case; the criteria for each case must be clear and computable. Because an algorithm is a precise list of precise steps, the order of computation is always crucial to the functioning of the algorithm.
Instructions are usually assumed to be listed explicitly, and are described as starting "from the top" and going "down to the bottom"—an idea that is described more formally by flow of control. So far, the discussion on the formalization of an algorithm has assumed the premises of imperative programming. This is the most common conception—one which attempts to describe a task in discrete, "mechanical" means.
Unique to this conception of formalized algorithms is the assignment operation , which sets the value of a variable. It derives from the intuition of " memory " as a scratchpad. An example of such an assignment can be found below. For some alternate conceptions of what constitutes an algorithm, see functional programming and logic programming. Algorithms can be expressed in many kinds of notation, including natural languages , pseudocode , flowcharts , drakon-charts , programming languages or control tables processed by interpreters.
Natural language expressions of algorithms tend to be verbose and ambiguous, and are rarely used for complex or technical algorithms. Pseudocode, flowcharts, drakon-charts and control tables are structured ways to express algorithms that avoid many of the ambiguities common in the statements based on natural language.
Programming languages are primarily intended for expressing algorithms in a form that can be executed by a computer, but are also often used as a way to define or document algorithms. There is a wide variety of representations possible and one can express a given Turing machine program as a sequence of machine tables see finite-state machine , state transition table and control table for more , as flowcharts and drakon-charts see state diagram for more , or as a form of rudimentary machine code or assembly code called "sets of quadruples" see Turing machine for more.
Representations of algorithms can be classed into three accepted levels of Turing machine description, as follows: . Algorithm design refers to a method or a mathematical process for problem-solving and engineering algorithms. The design of algorithms is part of many solution theories of operation research , such as dynamic programming and divide-and-conquer.
Techniques for designing and implementing algorithm designs are also called algorithm design patterns,  with examples including the template method pattern and the decorator pattern. One of the most important aspects of algorithm design is resource run-time, memory usage efficiency; the big O notation is used to describe e. Most algorithms are intended to be implemented as computer programs. However, algorithms are also implemented by other means, such as in a biological neural network for example, the human brain implementing arithmetic or an insect looking for food , in an electrical circuit , or in a mechanical device.
In computer systems , an algorithm is basically an instance of logic written in software by software developers, to be effective for the intended "target" computer s to produce output from given perhaps null input.
An optimal algorithm, even running in old hardware, would produce faster results than a non-optimal higher time complexity algorithm for the same purpose, running in more efficient hardware; that is why algorithms, like computer hardware, are considered technology. Chaitin prefaces his definition with: "I'll show you can't prove that a program is 'elegant ' "—such a proof would solve the Halting problem ibid. Algorithm versus function computable by an algorithm : For a given function multiple algorithms may exist.
This is true, even without expanding the available instruction set available to the programmer. Rogers observes that "It is The same function may have several different algorithms".
Unfortunately, there may be a tradeoff between goodness speed and elegance compactness —an elegant program may take more steps to complete a computation than one less elegant. An example that uses Euclid's algorithm appears below.
Computers and computors , models of computation : A computer or human "computor"  is a restricted type of machine, a "discrete deterministic mechanical device"  that blindly follows its instructions. Minsky describes a more congenial variation of Lambek's "abacus" model in his "Very Simple Bases for Computability ".
However, a few different assignment instructions e. The unconditional GOTO is a convenience; it can be constructed by initializing a dedicated location to zero e. Simulation of an algorithm: computer computor language : Knuth advises the reader that "the best way to learn an algorithm is to try it. Stone gives an example of this: when computing the roots of a quadratic equation the computor must know how to take a square root. If they don't, then the algorithm, to be effective, must provide a set of rules for extracting a square root.
But what model should be used for the simulation? Van Emde Boas observes "even if we base complexity theory on abstract instead of concrete machines, arbitrariness of the choice of a model remains. It is at this point that the notion of simulation enters". For example, the subprogram in Euclid's algorithm to compute the remainder would execute much faster if the programmer had a " modulus " instruction available rather than just subtraction or worse: just Minsky's "decrement".
Kemeny and Kurtz observe that, while "undisciplined" use of unconditional GOTOs and conditional IF-THEN GOTOs can result in " spaghetti code ", a programmer can write structured programs using only these instructions; on the other hand "it is also possible, and not too hard, to write badly structured programs in a structured language".
Canonical flowchart symbols  : The graphical aide called a flowchart , offers a way to describe and document an algorithm and a computer program of one. Like the program flow of a Minsky machine, a flowchart always starts at the top of a page and proceeds down. Sub-structures can "nest" in rectangles, but only if a single exit occurs from the superstructure.
The symbols, and their use to build the canonical structures are shown in the diagram. One of the simplest algorithms is to find the largest number in a list of numbers of random order. Finding the solution requires looking at every number in the list. From this follows a simple algorithm, which can be stated in a high-level description in English prose, as:. Quasi- formal description: Written in prose but much closer to the high-level language of a computer program, the following is the more formal coding of the algorithm in pseudocode or pidgin code :.
He defines "A number [to be] a multitude composed of units": a counting number, a positive integer not including zero.
To "measure" is to place a shorter measuring length s successively q times along longer length l until the remaining portion r is less than the shorter length s. Euclid's original proof adds a third requirement: the two lengths must not be prime to one another. Euclid stipulated this so that he could construct a reductio ad absurdum proof that the two numbers' common measure is in fact the greatest. So, to be precise, the following is really Nicomachus' algorithm. Only a few instruction types are required to execute Euclid's algorithm—some logical tests conditional GOTO , unconditional GOTO, assignment replacement , and subtraction.
The following algorithm is framed as Knuth's four-step version of Euclid's and Nicomachus', but, rather than using division to find the remainder, it uses successive subtractions of the shorter length s from the remaining length r until r is less than s.
The high-level description, shown in boldface, is adapted from Knuth — E1: [Find remainder] : Until the remaining length r in R is less than the shorter length s in S, repeatedly subtract the measuring number s in S from the remaining length r in R. E2: [Is the remainder zero? E3: [Interchange s and r ] : The nut of Euclid's algorithm. Use remainder r to measure what was previously smaller number s ; L serves as a temporary location.
The following version of Euclid's algorithm requires only six core instructions to do what thirteen are required to do by "Inelegant"; worse, "Inelegant" requires more types of instructions.