entranceOptions for tasks on discipline workshop on computer. Workshop to solve the tasks for computers: educational and methodological manual
(Document)
n1.DOC.
Ministry of Education of the Russian Federation
Novosibirsk State Technical University
Workshop on EUM.
Algorithms
Approved by the Editorial Publishing Council of the University
as a teaching aid
for students of the I course of the Fppmi
(Direction 510200 - Applied Mathematics
and informatics, specialty 351500 -
Mathematical Provision and Administration
information systems) day form training
Novosibirsk
2004
T. A. Shaposhnikova, st. teacher
Reviewers: S. . Pianocand. tehn sciences, ass.,
L.V. Tunincand. tehn Sciences, Doc.
Work prepared at the Department of Applied Mathematics
Workshop on computer. Algorithms
P 691 Tutorial / V.P. Hitsenko, TA Shaposhnikova. - Novosibirsk: Publishing house NSTU, 2004. - 112 p.
The main algorithms studied in the course "Workshop on the computer" are considered: algorithms on graphs, combinatorial algorithms, full of extinguishing algorithms. Many examples illustrating the theoretical material are disassembled.
UDC 004.421 + 519.1] (075.8)
Novosibirsk State
technical University, 2004
table of contents
The course "Workshop on a computer" is the first basic discipline among programmer disciplines. It is impossible to master programming without the knowledge of the most important and most famous algorithms. In this tutorial, algorithms are dismantled in detail, widely used in solving different classes of tasks: basic algorithms on graphs, the algorithm of full extinguishing and methods for its improvement (dynamic programming algorithms, "greedy" algorithm, a method of branches and boundaries), algorithms for the formation of basic combinatorial objects.
The textbook is intended not only for students studying the initial sections of programming, but also for those who wish to enrich their skills to design algorithms (instead of the "invention of the next bicycle"). Often the difference between bad and good algorithms is more significant than between quick and slow computers. For example, we want to sort an array of a million numbers. What faster is to sort it inserts on a super computer (100 million operations per second) or merge on the home computer (1 million operations)? At the same time, if the sorting inserts is written on the assembler extremely economically, for sorting n. numbers need approximately 2 n. 2 operations. At the same time, the merger algorithm is written without special care for efficiency and requires 50 · n.· Log. n. operations. In the first case, for sorting 1 million numbers we get:
for a super computer:
for a home computer:
This shows that the development of effective algorithms is no less important than the development of fast electronics.
The training manual complements the lecture and practical material of the discipline "Workshop on a computer" and is oriented primarily to support independent work students when performing RGR and term papers. Therefore, each algorithm given in the tutorial is disassembled on practical exampleFor some, the programming language is implemented (SI). Also for algorithms are given to assess their complexity.
Algorithms are recorded in the form of "pseudocode" commented in the text, clearly presented in the pictures and in the tables.
1. Basic algorithms on graphs
Mathematical models of a large number of tasks can be described in terms of the theory of graphs, therefore the algorithms for the study of the structure (processing) of the graphs, as well as the ways of their presentation are very important.
1.1. Some basic definitions
Count (ne-oriented graph) G.(V., E.) called a combination of two sets, where V. - finite non-empty set of elements called vertices, and E. - Many unordered pairs of various elements of the set V. (These pairs are called edges). Count consisting of one vertex is called trivial.
Say that the edge e. = (u., v.) Connect tops u. and v.. Edge e. and top u. (as well as e. and v.) Called incident, and vertices u. and v. – adjacent. Ribs, incidents of the same top, are also called adjacent.
The degree of vertices - This is the number of edges incident to it. The top of the graph, which has a degree of 0, is called isolated, and having a degree 1, - hanging.
If a E. is not a set, but a set containing several identical elements, then these elements are called multiple ribsand graph - multigrafe.
If the element of the set E. maybe a pair of identical (not different) elements V.then such an element E. called loop. Pseudograph- This is a graph, which, along with multiple ribs, allowed and loops, and even a few loops at one vertex.
Count is called simpleIf any pair of vertices is connected by no more than one edge and the graph does not have a loop.
Route (way) - this is an alternating sequence
a \u003d V. 0 , e. 1 , V. 1 , E. 2 , ..., v n - 1 , e N,v n \u003db.
Vertices and edges graph such that e. i. = (v. i- 1 , v. i.), 1 ? i. ? n.. It is said that the route connects the vertices a. and b. - The ends of the route. In a simple column, the route can be added by the listing only its vertices a. = v. 0 , v. 1 , …, v. n. = b. or his ribs e. 1 , e. 2 , …, e. n. .
The route is called chainIf all his ribs are different. The route is called closed, if a v. 0 = v. n. .
Closed chain called cycle. Chain is called plainif it does not contain the same vertices. Simple closed chain called simple cycle.
Hamiltonian chain It is called a simple chain containing all the vertices of the graph. Hamiltonian cycle It is called a simple cycle containing all the vertices of the graph.
Vertex u. achievablefrom the vertex v.if there is a way from v. at u..
Path Length v. 0 , v. 1 , …, v. n. equal to the number of his ribs, i.e. n..
Distance Between the two vertices is the length of the shortest path connecting these vertices.
Part of the graph G.(V., E.) - this is such a graph G."(V.", E."), what V." V. and E." E..
Subgraph Count G. called the same part G." which together with any pair of vertices u,v. Contains and edge (u., v.) if it is in G..
Supplement Count G. called graph G." with the same set of vertices as G., and two different vertices are adjacent to G." Then and only when they are unstable in G.. Rib graphs G. and G." together form full graph. Count is called fullIf any two of its vertices are adjacent.
Two graphs G.1 I. G.2 isomorphicif there is a mutually unambiguous mapping of the set of vertices of the graph G.1 on the set of vertices of the graph G.2, preserving adjacency.
Count is called connected If for any pair of vertices there is a jointing path. The maximum connected subgraph of an unbound graph is called component connected This graph.
If the function is specified F.: V.?M., then the set M. called multiple marks, and the graph - labeled. If the function is specified F.: E.?M.. the edges of the graph attributed weight, then the graph is called weighted.
The edge of the graph is called orientedIf the order of its ends is essential. Count, all ribs of which are oriented, called oriented Count (or orgraff). In this case, the elements of the set V. called nodes, and sets of set E. – arcs. Arc (u., v.) leads from the vertex u. To the top v., Top v. refer to the successor of the vertices u., but u -previous vertices v.. Concepts parts of the orgraf, paths, distances, simple and closed path, cycle Defined for the orgraf as well as for the graph, but taking into account the orientation of the arc.
Source of Orgraf. - This is a vertex from which all other vertices are achievable. Stoke Orgraf. - This is a vertex, achieving from all other vertices.
Tree A connected graph without cycles is called.
Root tree - This is a connected orgraf without cycles satisfying conditions:
There is a single vertex called the root in which no arc is not included;
One arc leads to each non-smeed vertex.
1.2. Representation of graphs in computer
The presentation in the program of objects of the mathematical model is an important component of programming. The choice of best presentation is determined by the requirements. specific task. Known various methods Representations of graphs in the memory of the computer. They differ in the amount of memory and speed of operations over graphs. It should be noted that in many challenges on the graphs, the choice of presentation is decisive for the effectiveness of algorithms. In fig. 1.1 for non-oriented ( a B C D) and oriented ( d, e, w, s) Stocks show various views: b, E. - adjacency matrix; b, J. - matrix of incident; g. - a list of adjacent non-oriented graph with the adjacent placement of the list elements; z. - List of adjacency oriented graph with the associated location of the list item.
1.2.1. Count adjacent matrix
The adjacency matrix of the marked graph with n. The vertices are called the matrix a \u003d [ a. iJ. ], i., j. = 1, 2, ..., n., wherein
The adjacency matrix uniquely determines the graph (Fig. 1.1, a-B., d-E.). For an unoriented graph, the matrix A is symmetric relative to the main diagonal. The number of units in the line is equal to the degree of relevant vertex. The loop in the array matrix can be represented by a corresponding unit diagonal element. The edges can be represented by allowing the matrix element to be greater than 1.
The advantage of such a presentation is "direct access" to the edges of the graph, i.e. There is an opportunity in one step to get an answer to the question "Does the edge exist in the column (x., y.) ? " For small graphs, when there is enough space in memory, with the arrangement matrix is \u200b\u200boften easier to work. The disadvantage is that, regardless of the number of ribs, the amount of memory occupied is n. n. or n. n./2 – n.If you use symmetry and store only the triangular view of the adjacency matrix. In addition, each element of the matrix is \u200b\u200bsufficient to present with one binary discharge.
1.2.2. Country incident matrix
The incidence matrix is \u200b\u200bcalled the matrix B \u003d [ b. iJ. ], i. = 1, 2, ..., n., j. = 1, 2, ..., m. (Where n. - number of vertices, and m. - The number of edges of the graph), the rows of which correspond to the vertices, and the columns - ribs. The element of the matrix in a non-oriented column is:
In case of an oriented graph with n. vertices I. m. Arcs The element of the incidence matrix is \u200b\u200bequal to:
The matrix lines also correspond to the vertices, and the columns are arcs.
The incidence matrix uniquely determines the structure of the graph (Fig. 1.1, but–at, Dr.). In each column of the matrix B exactly two units. There are no equal columns.
The disadvantage of this presentation is that n. m. Memory units, most of which will be occupied by zeros. Not always convenient access to information. For example, to answer questions "Is there an arc in the column (x., y.) ? " Or "To which tops are the ribs from the top x.? " You may need to bust all columns of the matrix.
1.2.3. Matrix scales graph
A simple weighted graph can be represented by its scales w \u003d [ w. iJ. ], where w. iJ. - Weight of the rib connecting the vertices i., j. = 1,2, ..., m.. The weight of non-existent ribs rely equal? or 0 depending on the task. Matrix of scales is a simple generalization of the adjacency matrix.
1.2.4. List of edges graph
When describing the graph by the list of his ribs (or for the arg-arms list), each edge is represented by a pair of peaks incident. This view can be implemented by two arrays (or one two-dimensional):
x \u003d.(x. 0 , X. 1 , ..., x m)and y \u003d (y 0 , Y. 1 , ..., y m)
Where m. - Number of ribs in the graph. Each element in the array is a vertex mark, and i.-E. the edge of the edge comes out of the top x. i. and enters the top y. i. . The amount of memory is in this case 2 m. memory units. The inconvenience is a large number of steps needed to obtain a plurality of vertices to which ribs are conducted from this vertex.
1.2.5. Lists of adjacent vertices of the graph
The graph may definitely be represented by the structure of the adjacency of its vertices. The adjacency structure consists of ADJ lists [ x.] Top of the graph adjacent to the top x.. ADJ lists [ x.] Compiled for each vertex graph. The adjacency structure is conveniently implemented by an array of n. (number of vertices in the column)
linearly related lists (1.1, a-G.). Each list contains 
but d.
| 1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 |
|||
| 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
|
| 2 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
|
| 3 | 1 | 1 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
|
| 4 | 0 | 0 | 1 | 0 | 1 | 4 | 1 | 1 | 0 | 0 | 0 |
|
| 5 | 1 | 0 | 1 | 1 | 0 | 5 | 0 | 0 | 0 | 1 | 0 |
b. e.
| Ѕ | 1/3 | 1/5 | 2/3 | 3/4 | 3/5 | 4/5 | Ѕ | 1/3 | 4/1 | 4/2 | 5/4 |
|||
| 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | -1 | 0 | 0 |
|
| 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | -1 | 0 | 0 | -1 | 0 |
|
| 3 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 3 | 0 | -1 | 0 | 0 | 0 |
|
| 4 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 1 | 1 | -1 |
|
| 5 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 5 | 0 | 0 | 0 | 0 | 1 |
at j.
g. z.
Fig. 1.1
The vertices are adjacent to the vertex for which the list is compiled. The list of adjacent vertices of the graph gives a compact representation for rarefied graphs - those in which many ribs have a lot less than the set of vertices. The disadvantage of this presentation is: if we want to know if there is a rib in the column ( x., y.), have to browse the whole list of adj [ x.] In Search y.. The amount of memory required is for oriented n.+ m. and n.+2 m. for non-oriented graphs of memory units, where n. - the number of vertices of the graph, and m. - The number of edges (arcs) of the graph. If the problem solving algorithm is based on adding and removing vertices from lists, the storage of adjacency lists is conveniently implemented using the associated listed representation (1.1, dn).
1.3. Country bypass
Bypassing a graph is some systematic passage of its vertices (and / or ribs). Coming by the graph, we move along the ribs (arcs) and we pass all the vertices. In this case, you can get a lot of information that is necessary for further processing of the graph, therefore, bypassing the graph - the basis of many algorithms for studying the structure of the graph. If, when visiting the vertices, the structure of the graph does not change, the two main ways to bypass are most useful: bypassing and around the width.
1.3.1. Bypass (or search) in depth
Let the graph set and fixed the initial vertex s. (The source graph may be non-oriented or oriented). The search strategy in the depth is that, starting from the initial vertex, go deep into the depth, while it is possible (there are no outgoing ribs), and return and look for another way when there are no such edges. This is done until all the vertices are detected achievable from the source. If the undetected vertices remain after that, one of them is selected (as the initial) and the process is repeated. So do until we find all the vertices of the graph.
When when searching we first detect the top v.adjacent to the top u., It is necessary to mark this event. The depth search algorithm uses for this color (tags) vertices. Each of the peaks are first white (not traveled). Being detected, it becomes gray. When the vertex is fully processed (i.e., when the list of adjacent peaks will be viewed), it will become black. Thus, in the process of searching from the graph, a part is allocated to the "depth search tree" or several trees (depth search forest), if the search is repeated from several vertices. Each peak falls at exactly one search tree in depth, so these trees do not intersect. In addition, you can put additional labels on the tops of the tree: the label when the top was detected (became gray), and the label when the processing of the adjacent list was completed u. verkhin (I. u. became black).
The algorithm below uses the graph view of the adjacent vertices of ADJ [ u.]. For each vertex u.
count additionally stored her color MARK [ u.] and its predecessor PR [ u.]. If there is no precursor (for example, if u. =
s. or u.
not yet discovered), then PR [ u.] = nil.. In addition, in D [ u.] I.
f [ u.] Additional for u. Tags: Time Tags. In D [ u.] Time is recorded when the vertex u. was discovered (and became gray), and in F [ u.] The time is written when the processing of the adjacent list has been completed u. Verkhin (I. u. became black). In the above time algorithm of Time Time D [ u.] I.
f [ u.] These are integers from 1 to 2 | V.|; For any vertex u. Inequality: D [ u.] U]. Vertex u. It will be white until d [ u.], gray between D [ u.] and f [ u.] and black after f [ u.]. The algorithm uses recursion to view all adjacent
u. Verkhin.
Search_V_Glubina ( G.)
2 for (each vertex u. V.[G.])
4 PR [ u.] ?nil.;
7 for (each vertex s. V.[G.])
Search ( u.)
3 D [ u.]? TIME? TIME + 1;
4 for (each v. ADJ [ u.])
5 (IF (MARK [ v.] \u003d White)
6 (PR [ v.] ?u.; Search ( v.); }
9 f [ u.]? TIME? TIME + 1;
10 }
The algorithm begins with the fact that first (lines 2-5) all the vertices are painted in white (marked as not passed); In the PR field placed nil. (While the vertices have no predecessor). Then (string 6) is set to the initial (zero) time (Time variable - global variable). For all vertices (strings 7-8), which are still not passed (white), the Search procedure is called. These vertices become roots of the depth of search trees.
At the moment of call Search ( u.) Top u. - White. In the SEARCH procedure, it immediately becomes gray (line 2). Its detection time (line 3) is entered in D [ u.] (The time counter before this increased by one). Then viewed (rows 4-7) adjacent to u. vertices; The SEARCH procedure is called for those that turn out to be white by the time of the call. After viewing all related u. Top peaks u. We make black and write in f [ u.] Time of this event.
Ministry of Education of the Russian Federation
Bashkir State University
Workshop on EUM.
Tasks for C ++
Part 1
Compiler:
Rykov V.I. Workshop on computer. Tasks for C ++ .. Part1. / Edition of the Bashkir University. - Ufa 2006. - Nos. C.
The work is devoted to the programming methodology in C ++.
Contains initial encoding information, launch and debugging programs. Contains texts of tasks and, in the necessary cases, instructions on the technology of solving them.
Methods for programming and encoding programs for each task type is presented in the form of complete examples.
Work is used when performing laboratory and practical work Under the discipline "Workshop on a computer".
1 Introduction 5.
1.1 First Program 5
2 certificate of C ++ 5
2.1 Basic data types 5
3 Simple data types 6
3.1 Model task Input operators, cycle. Attachment of structures 6.
3.2 Structure of pseudocode 7
3.3 Implementation of control structures 7
3.4 Model task integers. Operators for, While, IF 8
4 Arrays 10.
4.1 Model task Set of arrays. Machine zero 10.
4.2 Model task Including managing structures 18
5 procedures and functions 20
5.1 Model task example function 20
5.2 Overload function 21
5.3 Transfer of parameters to function 21
5.4 Transfer of an array address to function 22
6 vectors and matrix 24
6.1 Model task Multidimensional arrays, input from file 24
7 Processing symbolic information 29
7.1 Decision Find the longest symmetrical word of the specified sentence 31
8 Recursion 33.
8.1 Solution Calculation of the factorial of a positive number 33
8.2 Solution Recursive functions. Work with rows. 36.
8.3 Solution to build a syntactic analyzer for the concept of bracket. 38.
9 form of a report on laboratory work 41
10 Options for laboratory work 42
1. Introduction
Programming initial information is set out in the Microsoft Visual C ++ environment and debugging programs.
1.1 Presenting program
The program "2 + 3". In the program after invitation, two numbers are introduced. To enter each number you need to dial it on the keyboard and press the ENTER key.
#Include "iostream.h"
char * Rus (Const Char * Text);
iNT MAIN (Int Argc, Char * Argv)
// COUTRETURN 0;
char * Rus (Const Char * Text)
Workshop on computer, decision methods linear systems And finding your own values, part 1, Bogachev K.Yu., 1998
The present allowance contains descriptions of algorithms offered to the implementation of the Mechanics and Mathematical Faculty of Moscow State University on the computer, but a workshop on the computer. " For all algorithms, the necessary theoretical substantiation is given, the corresponding estimated relations and recommendations, but their practical implementation on the computer (the organization of the calculation process. Storage of data and results in the memory of the computer, etc.).
Methods for solving linear systems based on unitary transformations of matrices.
Each of the above methods for solving linear systems can be represented as a sequence. elementary transformations Matrixes (see, for example, such a representation in §4 for the Gauss method). Each of the transformations is given by some matrix P, so that the use of this preparation is equivalent to multiplying (left) of the original matrix A on the matrix R. Thus, each step of the above algorithms is the transition from the matrix A to the matrix A \u003d RA. On the number of conditionality of this new matrix A \u003d RA, it is possible to argue that K (RA)< к(Р)к(А). Поэтому может случиться так. что в процессе проведения преобразований число обусловленности матрицы возрастает и на каждом шаге метод будет вносить все большую вычислительную погрешность. В результате может оказаться, что исходная матрица имела приемлемое число обусловленности, однако после нескольких шагов алгоритма она уже имеет слишком большое число обусловленности, так что последующие шаги алгоритма приведут к появлению очень большой вычислительной погрешности.
An idea arises to select the matrices of the transform number. So that the number of conditionality of the matrix in the process of transformations has not increased. Lemma 1.5 indicates us an example of such matrices: if the Matrix of the transformation of the R unitary (orthogonal in the real case), then relative to the spectral norm to (Ra) \u003d K (A).
The method of rotations and the reflections method are the algorithms for the selection of unitary matrices of transformations P, such as, as a result of all these transformations, the initial matrix A is driven by a triangular form. The system with a triangular matrix is \u200b\u200bthen solved, for example, by the reference of the Gauss method. Despite. What the complexity of these methods is greater than the Gauss method (respectively, 3 and 2 times), these methods were widespread in computational practice due to their sustainability of the accumulation of computational error.
Free download electronic book In a convenient format, watch and read:
Download the book Workshop on computer, methods for solving linear systems and finding our own values, part 1, Bogachev K.Yu., 1998 - FilesKachat.com, fast and free download.
- Workshop on computer, methods for solving linear systems and finding our own values, part 2, Bogachev K.Yu., 1998
- Mathematics and design, class 1, training manual for general education organizations, Volkova S.I., 2016
- Mathematics, oral exercises, grade 1, Tutorial for general education organizations, Volkova S.I., 2016
The following textbooks and books.
Federal Agency for Education
State educational institution
Tomsk Polytechnic University
__________________________________________________________________
"Approve"
Director IDO
"____" ____________ 2007
Workshop on EUM.
Working programm, Methodical instructions and control tasks For students of specialties 521600 (080100) "Economy", 060500 (080109) "Accounting, Analysis and Audit", 060700 (080103) "National Economics", 060800 (080502) "Economics and Management at the enterprise", 061100 (080507) " Management Management »Institute for Remote Education
Semester | |
Independent work, weeks | |
Tasks, weeks | |
Writing report, clock | |
Forms of control | |
UDC 681.3: 658.8
Workshop on computer: Work program, Methodical instructions for students of specialties 521600 (080100) "Economy", 060500 (080109) "Accounting, Analysis and Audit", 060700 (080103) "National Economics", 060800 (080502) "Economics and Management At the enterprise, "061100 (080507)" Management Management ". ID / Sost. . - Tomsk: ed. TPU, 2007. - 23 s.
The working program, guidelines and control tasks are considered and recommended for the publication of the methodological seminar of the Department of Economics April 12, 2007, Protocol
Head Department, Professor, d. E. N .____________
annotation
Working program, Methodical instructions and control tasks for production practice "Workshop on computer" are intended for students of specialties 521600 (080100) "Economy", 060500 (080109) "Accounting, Analysis and Audit", 060700 (080103) "National Economics", 060800 (080502) "Economics and Management at the enterprise", 061100 (080507) "Management of the Organization". Educational practice is held in the fourth semester on computer in the computer class of providing the department or branch IDO, the duration of practice is 4 weeks.
The list of the main issues to be studied in practice is given. The options are given control tasks. Methodical instructions on their implementation are given.
1. Objectives and objectives of production practices
Production Practice Targets
Educational practice "Workshop on EUM" is intended to consolidate skills on the use of information technology. During its passage, students get acquainted with the structure of the economic information system, with information resources, overall characteristic and the classification of information technologies using Microsoft Office in its work. Practice is important in preparation economistcontributes to the successful implementation of the continuous use program computer at training process. Special attention is paid to independent work and the instill of practical skills with a wide use of computer. To secure and verify the work experience gained, the workshop contains additional tasks that students must fulfill and submit the results in the report.
Tasks performed during training practice
During the practice, students perform tasks for processing economic information and financial calculations in Excel, creating databases and work with them in the ACCESS DBMS environment.
Passage of training practice "Workshop on a computer" includes:
a) independent work on teaching benefits, guidelines;
b) performing independent tasks and reference tasks;
d) Protection of practice.
Topic 1. Information Technologies
1. Information, technology.
2. Economic information system.
3. Conceptual model of information technology.
4. Informational resources and the properties of information technology.
5. Classification of information technology.
Topic 2. Processing of economic information in Excel
1. Preparation and editing of economic information.
2. Simplest calculations in Excel tables.
3. Preparation of reports for business analysis.
Topic 3. Financial calculations in Excel
1. Accrual interest rates.
2. Analysis of investments.
3. Forecasting the values \u200b\u200bof the time series.
Topic 4. Access database management system
1. Basic concepts of DBMS Access.
2. ACCESS Database Working Environment.
3. Creating tables Access.
4. Creating the simplest forms and their use.
5. Search for information and creating requests.
6. Creating reports.
During the passage of practice, tasks are performed on the following topics. Each student must execute one task from the given tasks for independent work. The task number indicates the manual. Training Practice held on personal computer And lies in practical use Computer students software products (Microsoft Office.).
Topic2 . Processing of economic information in Excel
Preparation and editing of economic information
1. Create a table in which you need to include the following data on vehicle owners: last name, first name, patronymic, date of birth, address, car brand, number state registration, date of release, mileage (km). The table must contain data for at least ten owners.
2. Create a table that fixes the results of the session and includes the following data: last name, first name, patronymic, date of passing the exam, the name of the subject, the result of the delivery (number). The session was 4 exam.
3. Create a table containing the following information about the supply of goods of the food group: the name of the goods, the cost per unit (p.), The number (pcs., Kg), the name of the company - the buyer, the name, first name, dealer, the date of delivery. The table must contain at least ten types of goods.
4. Create a table containing information on the availability of goods of the industrial group (audio and video equipment) in the warehouse of the company: product name, the cost of unit (p.), Quantity (pcs.), The name of the manufacturer, the date of receipt. The table must contain at least ten types of goods.
Tasks for independent work
Topic 3.Financial calculations in Excel
In the conditions of the corresponding independent tasks for the "Preparation and Editing Economic Information" section, find:
1. Age of vehicle owners (TC), the total cost of all vehicles, the average mileage of the vehicle, the date of the issue of the newest and the oldest TS.
2. The middle score obtained on the exams, the date of the first exam, is the last exam.
3. The cost of goods implemented by each dealer, the date of the last delivery, the price of the most expensive goods, the total value of the goods supplied by the company.
4. The cost of all goods in stock, the date of receipt of the goods, longer than all stored in the warehouse, the total number of goods, the price of the most expensive goods.
Topic 4.Database Management SystemAccess
Tasks for independent work
With the ACCESS DBMS Create:
1. Database of product implementation by a commercial organization for the specified period.
Field names: dealer, delivery amount, quantity of supplies, delivery date, invoice number, client.
Tables: Dealer, client.
2. The database of warehouse accounting in a commercial organization to the specified date.
Field names: Product name, quantity, price per unit., Supplier, delivery date.
Tables: Goods, suppliers.
As a prototype for tasks 1 and 2, take any known commercial organization of the region, district, city. Data can be conditional.
In the shape of dealer(Task 1) and name of product (task 2) Create buttons: Forward on the records, Back by recordings, Search, Output.
4. Examination
4.1. General guidelines
To complete the study of economic tasks in the environment tableware processor Excel At the end of the production practice, you need to perform control tasks here for the issued option.
Control tasks and results of the solution must be brought in a production practice report.
The report design is made in accordance with the general requirements of reporting (see paragraph 6.)
4.2. Methodical instructions and options for test tasks
Task number 1.
The trading company in the current month delivered products N. Customers for a total amount S. R. with the provision of a trade loan for a period of one month under the percentage PI. Determine:
· Profit company from this loan;
· Pure profit, provided that the income tax is 20%;
· Profit with an increase in inflation 1% per month;
· Change lending conditions for the inflation level so that the company makes a profit of 10%.
Values S.1 , S.2 ,…, SN. Set arbitrarily so that.
Values PI Take from the interval: 
The source data for the task options are shown in Table 1. Table 1
Option number | Amount delivery | Number of clients N. |
Example of execution
Let the data on the perfect sales are specified in Table 2
table 2
Client | Sales amount, r. | Percent |
To perform the task, it is necessary to carry out the following calculations:
Profit \u003d 13350 p.
Profit tax \u003d 2670 p.
Net profit \u003d 10680 p.
Net profit with inflation 1% https://pandia.ru/text/78/464/images/image009_63.gif "width \u003d" 351 "height \u003d" 41 "\u003e \u003d 7.92%
Fig. 4.1. Performing task number 1 in Excel
Task number 2.
Commodity reserves are purchased by enterprise 4 times during the operational cycle ( N.1, N.2, N.3, N.four). Stocks at the beginning (beginning of the residue) make up N.0 units. Movement of stocks (quantity, price, cost) on the periods are given Table. 3.
Determine:
· Commodity stocks N. During the receipt period and their value at revenues S.;
· Balance of goods R. at the end of the period;
· The cost of the balance of goods is three methods - weighted average, LIFO, FIFO, if 500 units of goods were implemented;
· The cost of the balance of goods is three methods - weighted, lifo, FIFO, if 100 units of goods were implemented.
Table 3.
Indicators | amount | Price per unit., R. | Cost at prices arrivals, r. |
Residue (initial) | |||
Sales | |||
Residue (end) |
The source data for the task options are shown in Table 4.
Table 4.
Option number | N.0 | N.4 |
|||
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Contest 1: Python (in Anytask)
NUMPY library. Vectorization of calculations.
Important article documentation NUMPY:
Contest 2: NUMPY (in Anytask)
Code Organization in Python.
Functions, modules, classes.
Contest 3: Classes (in Anytask)
Metric classification methods.
Discussion of the first practical task.
Introduction to image processing.
Visualization in Python.
Preparation of text reports. Tex system.
Exception Handling. Menengers context. Testing.
Preparation of short speeches.
Iterators and generators.
Requirements for the report on practical tasks
The report must be a self-sufficient document in pDF formatprepared in the LATEX system. Students who have completed reports on past tasks are able to pass reports in HTML or PDF format, prepared using JUPYTER Notebook.
The report should give verifying answers to the following questions:
- What course is the task?
- What task is done?
- Who is the task?
- What was the assignment?
- What was done? What was not done?
- Are the correct answers to all theoretical questions of the task?
- Are all the necessary experiments been carried out? Have you received meaningful conclusions?
- Is the creative part of the task?
- Did the student who else use? If so, in which volume?
- What literature did the student use?
Some elements of a good report:
- Report volume: 5--20 pages;
- The report of the report does not repeat the full task formulation;
- The report structure corresponds to the task items;
- Vector fonts are used;
- Graphs are properly decorated;
- Scale for graphs is chosen correctly;
- On different graphs, the results for the same methods are displayed in the same color;
- Between the location of the graphs and the places of their mention in the text regarding small distance (on the same or on the next page);
- The pages should not have a lot of empty space;
- In most cases, graphics / tables / pseudocodes of algorithms should not occupy most of one page of the report;
- All numbers in the text / tables are indicated with the required number of meaningful digits;
- In most cases, there should be no code in the report;
- For all experiments, the selected design of experiments is described, as well as conclusions from the results obtained;