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Series ISSN 1939-5221 Generating Functions in Engineering and the Applied Sciences Rajan Chattamvelli, VIT University, Vellore, Tamil Nadu Ramalingam Shanmugam, Texas State University This is an introductory book on generating functions (GFs) and their applications. It discusses commonly encountered ...
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apply the lessons learned in their respective disciplines. The purpose is to give a broad exposure to commonly used techniques of combinatorial mathematics, highlighting applications in a variety of disciplines. ABOUT SYNTHESIS This volume
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apply the lessons learned in their respective disciplines. The purpose is to give a broad exposure to commonly used techniques of combinatorial mathematics, highlighting applications in a variety of disciplines. ABOUT SYNTHESIS This volume is a printed version of a work that appears in the Synthesi...
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velli and Ramalingam Shanmugam 2019 Transformative Teaching: A Collection of Stories of Engineering Faculty’s Pedagogical Journeys Nadia Kellam, Brooke Coley, and Audrey Bok
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velli and Ramalingam Shanmugam 2019 Transformative Teaching: A Collection of Stories of Engineering Faculty’s Pedagogical Journeys Nadia Kellam, Brooke Coley, and Audrey Boklage 2019 Ancient Hindu Science: Its Transmission and Impact of World Cultures Alok Kumar 2019 Value Relational Engineering Shuichi Fukuda 2018 ...
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13 The Making of Green Engineers: Sustainable Development and the Hybrid Imagination Andrew Jamison 2013 Crafting Your Research Future: A Guide to Successful Master’s and Ph.D. Degrees in Science & Engineering Charles
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13 The Making of Green Engineers: Sustainable Development and the Hybrid Imagination Andrew Jamison 2013 Crafting Your Research Future: A Guide to Successful Master’s and Ph.D. Degrees in Science & Engineering Charles X. Ling and Qiang Yang 2012 iv Fundamentals of Engineering Economics and Decision Analysis David ...
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ids, Part Two: Transport Properties of Solids Richard F. Tinder 2007 Tensor Properties of Solids, Part One: Equilibrium Tensor Properties of Solids Richard F. Tinder 2007 Essentials
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ids, Part Two: Transport Properties of Solids Richard F. Tinder 2007 Tensor Properties of Solids, Part One: Equilibrium Tensor Properties of Solids Richard F. Tinder 2007 Essentials of Applied Mathematics for Scientists and Engineers Robert G. Watts 2007 Project Management for Engineering Design Charles Lessard and ...
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sciences, such as or- dinary generating functions (OGF), exponential generating functions (EGF), probability gen- erating functions (PGF), etc. Some new GFs like Pochhammer generating functions for both rising and falling factor
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sciences, such as or- dinary generating functions (OGF), exponential generating functions (EGF), probability gen- erating functions (PGF), etc. Some new GFs like Pochhammer generating functions for both rising and falling factorials are introduced in Chapter 2. Two novel GFs called “mean deviation generating function”...
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, Pochhammer generating functions, poly- mer chemistry, power series, recurrence relations, reliability engineering, special numbers, statistics, strided sequences, survival function, truncated distributions. Contents ix List of Tables . . . . .
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, Pochhammer generating functions, poly- mer chemistry, power series, recurrence relations, reliability engineering, special numbers, statistics, strided sequences, survival function, truncated distributions. Contents ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Ordinary Generating Functions . . . . . . . . . . . .
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Ordinary Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Recurrence Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4.2 Types of Sequences . . . . . . . ....
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.7.1 Auto-Covariance Generating Function . . . . . . . . . . . . . . . . . . . . . . . . 16 Information Generating Function (IGF) . . . . .
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.7.1 Auto-Covariance Generating Function . . . . . . . . . . . . . . . . . . . . . . . . 16 Information Generating Function (IGF) . . . . . . . . . . . . . . . . . . . . . . . 16 1.7.2 1.7.3 Generating Functions in Graph Theory . . . . . . . . . . . . . . . . . . . . . . . . 16 1.7.4 Generating Functions in Number Theo...
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1.2 Addition and Subtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 x 2
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1.2 Addition and Subtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 x 2.1.3 Multiplication by Non-Zero Constant . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.1.4 Linear Combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Shifting . ....
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of Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Summary . . . . . . . . . . .
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of Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4 3 Generating Functions in Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
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3.6 MD of Some Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.6 MD of Some Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.6.1 MD of Geometric Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.6.2 MD of Binomial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.6.3 MD o...
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. . . 54 3.10 Factorial Moment Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3
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. . . 54 3.10 Factorial Moment Generating Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.11 Conditional Moment Generating Functions (CMGF) . . . . . . . . . . . . . . . . . . 58 3.12 Generating Functions of Truncated Distributions . . . . . . . . . . . . . . . . . . . . . . 58 3.13 Convergenc...
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. . . . . . . . . . . . . . . . . . . . . 64 Applications in Computing . . . . . . . . . . . . . . . . . . . . . .
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. . . . . . . . . . . . . . . . . . . . . 64 Applications in Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2.1 Merge-Sort Algorithm Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.2.2 Quick-Sort Algorithm Analysis . . . . . . . . . . . . . . . ...
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. . . . . . . . . . . . . . . . . . . . . . . . . 71 4.3.4 Towers of Hanoi Puzzle . . . . . . . . . . .
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. . . . . . . . . . . . . . . . . . . . . . . . . 71 4.3.4 Towers of Hanoi Puzzle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Applications in Graph Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.4.1 Graph Enumeration . . . . . . . . . . . . . . . ...
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Applications in Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 S
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Applications in Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Sums of IID Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.8.1 4.8.2 Infinite Divisibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8...
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. . . . . . . . . . . . . . . . 86 4.12 Applications in Management . . . . . . . . . . . . . . . . . . . . . . .
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. . . . . . . . . . . . . . . . 86 4.12 Applications in Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.12.1 Annuity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6 4.7 4.8 4.9.1 4.5 xii 4.13 Applications in Ec...
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.1 Some standard generating functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Convolution as
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.1 Some standard generating functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Convolution as diagonal sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2 2.3 Summary of convolutions and powers . . . . . . . . . . . . . . . . . . . . . . . . . ....
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. . . . . . . 53 Summary table of zero-truncated generating functions . . . . . . . . . . . . . . . . . . 59 C H A P T E R
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. . . . . . . 53 Summary table of zero-truncated generating functions . . . . . . . . . . . . . . . . . . 59 C H A P T E R 1 1 Types of Generating Functions After finishing the chapter, readers will be able to (cid:1) (cid:1) (cid:1) • explain the meaning and uses of generating functions. • describe the ordin...
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GENERATING FUNCTIONS Definition 1.1 (Definition of Generating Function) A GF is a simple and concise expression in one or more dummy variables that captures the coefficients of a finite or
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GENERATING FUNCTIONS Definition 1.1 (Definition of Generating Function) A GF is a simple and concise expression in one or more dummy variables that captures the coefficients of a finite or infinite series, and generates a quantity of interest using calculus or algebraic operations, or simple substitutions. GFs are used in...
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f .0/ C D C 1.1.2 EXISTENCE OF GENERATING FUNCTIONS GFs are much easier to work with than a whole lot of numbers or symbols. This does not mean that any sequence can be
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f .0/ C D C 1.1.2 EXISTENCE OF GENERATING FUNCTIONS GFs are much easier to work with than a whole lot of numbers or symbols. This does not mean that any sequence can be converted into a GF. For instance, a random sequence may not have a nice and compact GF. Nevertheless, if the sequence follows a mathematical rule...
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range is (cid:140) t 1(cid:141). As the input can be complex numbers in some signal processing applications, the GF can also be complex. A formal power series implies that nothing is assumed on the
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range is (cid:140) t 1(cid:141). As the input can be complex numbers in some signal processing applications, the GF can also be complex. A formal power series implies that nothing is assumed on the conver- gence. As shown below, the sequence 1; x; x2; x3; : : : has GF F(x) = 1=.1 x/ which converges x2/, in the region ...
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1.2 NOTATIONS AND NOMENCLATURES A finite sequence will be denoted by placing square-brackets around them, assuming that each element is distinguishable. An English alphabet will be used to identify it when necessary
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1.2 NOTATIONS AND NOMENCLATURES A finite sequence will be denoted by placing square-brackets around them, assuming that each element is distinguishable. An English alphabet will be used to identify it when necessary. An infinite series will be denoted by simple brackets, and named using Greek alphabet. Thus, S D .a0; ...
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C 1/ D (cid:3) (cid:1) (cid:1) (cid:1) (cid:3) .x j C (cid:0) 1/ D j 1 (cid:
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C 1/ D (cid:3) (cid:1) (cid:1) (cid:1) (cid:3) .x j C (cid:0) 1/ D j 1 (cid:0) Y .x 0 k D k/ C D (cid:128).x j / C (cid:128).x/ : (1.1) 2. Falling Factorial Notation In the falling factorial, a variable is decremented successively at each iteration. This is denoted as x.j / .x x (cid:3) (cid:0...
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the dummy variable. For example, G.x; t/ denotes an OGF of a random variable X. Some authors use x as a subscript as Gx.t/. These dummy variables assume special values (like 0 or 1) to generate
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the dummy variable. For example, G.x; t/ denotes an OGF of a random variable X. Some authors use x as a subscript as Gx.t/. These dummy variables assume special values (like 0 or 1) to generate a quantity of interest. Thus, uniform and absolute convergence at these points are assumed. The GFs used in statistics can be...
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derived easily. This reasoning holds for other GFs too. C X (cid:3) 1.4 ORDINARY GENERATING FUNCTIONS Let (cid:27) D .a0; a1; a2; a3;
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derived easily. This reasoning holds for other GFs too. C X (cid:3) 1.4 ORDINARY GENERATING FUNCTIONS Let (cid:27) D .a0; a1; a2; a3; : : : an; : : :/ be a sequence of bounded numbers. Then the power series f .x/ D anxn 1 X 0 n D (1.3) is called the Ordinary Generating Function (OGF) of (cid:27). Here x ...
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1 x/(cid:0) 1: x/(cid:0) 1; 1; .1; (cid:0) (cid:0) 6 (cid:0) 1/=.x (cid:0) 1
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1 x/(cid:0) 1: x/(cid:0) 1; 1; .1; (cid:0) (cid:0) 6 (cid:0) 1/=.x (cid:0) 1, and odd coefficients a2n 1. TYPES OF GENERATING FUNCTIONS The OGF is .xn 1/=.x 1/ when there are a finite number of 1’s (say n of them). Thus, (cid:0) .1; 1; 1; 1; 1/ has OGF .x5 1 when even coefficients x2/(cid:0) 0. The OGF is called ...
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C (cid:1) (cid:1) (cid:1) .1; 2; 3; 4; 5; : : :/. This has OGF 1 2x .1;
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C (cid:1) (cid:1) (cid:1) .1; 2; 3; 4; 5; : : :/. This has OGF 1 2x .1; 2; 3; 4; : : : ; n; : : :/ (cid:148) .1 .1 2 (cid:0) x/(cid:0) 2 x/(cid:0) 1=.1 1=.1 (cid:0) x/2 x/2: C D D (cid:148) C 2; 3; .1; (cid:0) (cid:0) 4; : : : ; n; .n (cid:0) C 1/; : : :/ See Table 1.1 for more examples. ...
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=0k=0k=0k=0k=0k=0k=0 1.4. ORDINARY GENERATING FUNCTIONS 7 1.4.1 RECURRENCE RELATIONS There are in general two
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=0k=0k=0k=0k=0k=0k=0 1.4. ORDINARY GENERATING FUNCTIONS 7 1.4.1 RECURRENCE RELATIONS There are in general two ways to represent the nth term of a sequence: (i) as a function of n, and (ii) as a recurrence relation between nth term and one or more of the previous terms. For example, an .1 and Jones, 1996]. 1=n gives a...
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:0) n k n 1.4.2 TYPES OF SEQUENCES (cid:0) There are many types of sequences encountered in engineering. Examples are arithmetic se- quence, geometric sequence, exponential sequence, Harmonic sequence
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:0) n k n 1.4.2 TYPES OF SEQUENCES (cid:0) There are many types of sequences encountered in engineering. Examples are arithmetic se- quence, geometric sequence, exponential sequence, Harmonic sequence, etc. Alternating se- quence is one in which the sign of the coefficients alter between plus and minus. This is of a...
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141). It is called a geometric progres- sion in mathematics and geometric sequence in engineering. It also appears in various physical, natural, and life sciences. As examples, the equations of state of gaseous mixtures (like
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141). It is called a geometric progres- sion in mathematics and geometric sequence in engineering. It also appears in various physical, natural, and life sciences. As examples, the equations of state of gaseous mixtures (like van der Vaals equation, Virial equation), mean-energy modeling of oscillators used in crystall...
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1 Consider the infinite series expansion of .1 C , which is the OGF of the given sequence. Here “a” is any nonzero constant. D C (cid:0) 1 ax 1 ax/(
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1 Consider the infinite series expansion of .1 C , which is the OGF of the given sequence. Here “a” is any nonzero constant. D C (cid:0) 1 ax 1 ax/(cid:0) a2x2 a3x3 (cid:0) C (cid:1) (cid:1) (cid:1) Example 1.2 (Find the OGF of an) Find the OGF of (i) an 1=3n. D 2n C 3n and (ii) an 2n C D Solution ...
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0) t/2 as the required OGF. (cid:0) 0 t n. The first sum is 2t=.1 t/2 and second one is 1=.1 1/. Write G.t / t
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0) t/2 as the required OGF. (cid:0) 0 t n. The first sum is 2t=.1 t/2 and second one is 1=.1 1/. Write G.t / t/. Take .1 P1n D C D C (cid:0) (cid:0) (cid:0) C D D D Example 1.4 (OGF of perfect squares) Find the OGF for all positive perfect .1; 4; 9; 16; 25; : : :/. squares Solution 1.4 Consider the expres...
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=x 2=.1 x/ (cid:0) D .1 C C x/=(cid:140)x.1 x/(cid:141) (cid:0) (1.7)
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=x 2=.1 x/ (cid:0) D .1 C C x/=(cid:140)x.1 x/(cid:141) (cid:0) (1.7) from which @f C perfect squares of all positive integers as x.1 as follows: @x D x/=.1 .1 (cid:0) x/3. Multiply this expression by x to get the desired GF of x/3. This can be represented symbolically x/=.1 C (cid:0) .1; 1; 1; 1; ...
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, 28, 82,: : :). Solution 1.5 Suppose we subtract 1 from each number in the sequence. Then we get (1, 3, 9, 27, 81, 2
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, 28, 82,: : :). Solution 1.5 Suppose we subtract 1 from each number in the sequence. Then we get (1, 3, 9, 27, 81, 243, : : :), which are powers of 3. Hence, above sequence is the sum of .1; 1; 1; 1; : : :/ .1; 3; 32; 33; 34; : : :/ with respective OGFs 1=.1 3x/. Add them to get 1=.1 x/ 3x/ as the answer. x/ and 1=...
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a1x x2 x x/(cid:0) C C 2Note that the nth derivative of 1=.1 C (cid:1) (cid:1) (cid:1) C C
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a1x x2 x x/(cid:0) C C 2Note that the nth derivative of 1=.1 C (cid:1) (cid:1) (cid:1) C C (cid:1) (cid:1) (cid:1) (cid:0) x/n C x/ is n(cid:138)=.1 (cid:0) (cid:0) 1 and that of 1=.1 ax/b is .n b (cid:0) C (cid:0) C C D , C (cid:1) (cid:1) (cid:1) 1/(cid:138)an=(cid:140).b (cid:0) 1/(cid:138).1 ...
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1 A aj xk a0 C D .a0 C a1/x .a0 a1 C C C a2/x2 : C (cid:1) (
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1 A aj xk a0 C D .a0 C a1/x .a0 a1 C C C a2/x2 : C (cid:1) (cid:1) (cid:1) (1.10) (cid:3) This result has great implications. Computing the sum of several terms of a sequence appears in several fields of applied sciences. If the GF of such a sequence has closed form, it is a simple matter of multiplyi...
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C (cid:0) (cid:0) 1 k2. x/, D C (cid:0) Example 1.6 (OGF of harmonic series) Find the OGF of 1 1=2 1
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C (cid:0) (cid:0) 1 k2. x/, D C (cid:0) Example 1.6 (OGF of harmonic series) Find the OGF of 1 1=2 1=3 C C C (cid:1) (cid:1) (cid:1) C 1=n. Solution 1.6 We know that x2=2 x/ (cid:0) 1 xk=k. We also know that when an OGF is multiplied by 1=.1 x3=3 C C x/, we get a new (cid:1) (cid:1) (cid:1) D OGF in whi...
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is the OGF of .1=2; 1 3 .1 C 2 /; 1 4 .1 1 2 C 1 3 /; (cid:1) (cid:1) (cid
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is the OGF of .1=2; 1 3 .1 C 2 /; 1 4 .1 1 2 C 1 3 /; (cid:1) (cid:1) (cid:1) /. C C (cid:1) (cid:1) (cid:1) (cid:0) 1.5. EXPONENTIAL GENERATING FUNCTIONS (EGF) 11 m k 1/k(cid:0) A direct application of the above result is in proving the combinatorial 0. P According to the above theorem, the sum of th...
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t/ has coefficients with negative sign 0 a2kt 2k, and G.t/ denotes the greatest integer 1. If F .t/ 1t 2k C m=2
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t/ has coefficients with negative sign 0 a2kt 2k, and G.t/ denotes the greatest integer 1. If F .t/ 1t 2k C m=2 t// is the GF of Pb c k t//=2 is the GF of P1k D k(cid:1)F .t/k is also a GF. x b c F .t//n 0 a2kt 2k. 1. Here P1k D (cid:0) 1t 2k m P k D (cid:20) 0 a2k t//=2 D F . F . 0 (cid:0) P D D (ci...
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D gives an OGF. But it is the usual practice to write it either as (1.12) or as H.x/ D 1 X 0 n D anenxn where en 1
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D gives an OGF. But it is the usual practice to write it either as (1.12) or as H.x/ D 1 X 0 n D anenxn where en 1=n(cid:138) D (1.13) (1.14) where we have two independent multipliers. As the coefficients of exponential function .exp.x// are reciprocals of factorials of natural numbers, they are most suited wh...
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GF may exist for a sequence. As an example, consider the sequence 1; 2; 4; 8; 16; 32; : : : with nth term an 2x/, whereas the EGF is
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GF may exist for a sequence. As an example, consider the sequence 1; 2; 4; 8; 16; 32; : : : with nth term an 2x/, whereas the EGF is e2x. Thus, the EGF may converge in lot many problems where the OGF either is not convergent or k(cid:1)xk. We could write this as an EGF by expanding does not exist. Consider .1 n (cid:0)...
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x/ so that P1n D xn=n(cid:138) D (cid:2) 1=.1 1=.1 0 n(cid:138) 0 1 xn (cid:0)
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x/ so that P1n D xn=n(cid:138) D (cid:2) 1=.1 1=.1 0 n(cid:138) 0 1 xn (cid:0) (cid:0) (cid:0) (cid:0) (cid:2) Now consider (cid:16)ebt (cid:0) eat (cid:17) =.b a/ (cid:0) D t=1(cid:138) .b2 a2/=.b a/t 2=2(cid:138) C .bn (cid:0) an/=.b (cid:0) a/t n=n(cid:138) .b3 (cid:0) a3/=.b (cid:0) a/t...
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2) (cid:11)/ (cid:0) D F0 C F1t=1(cid:138) C F2t 2=2(cid:138) C F3t
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2) (cid:11)/ (cid:0) D F0 C F1t=1(cid:138) C F2t 2=2(cid:138) C F3t 3=3(cid:138) C (cid:1) (cid:1) (cid:1) C Fnt n=n(cid:138) C (cid:1) (cid:1) (cid:1) ; (1.19) where Fn is the nth Fibonacci number. Another reason for the popularity of EGF is that they have interesting product and com- position formula...
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.t. x of both sides. As a0 is a constant, its derivative is zero. Use xn derivative of xn (cid:0) (cid:3) C (cid:1) (cid:1) (cid:
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.t. x of both sides. As a0 is a constant, its derivative is zero. Use xn derivative of xn (cid:0) (cid:3) C (cid:1) (cid:1) (cid:1) C akxk 1=.k . Differentiate again (this time a1 being a constant vanishes) to 1/(cid:138) (cid:0) (cid:0) get H 00.x/ a3x=1(cid:138) pro- cess k times. All terms whose coefficients are below ...
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uishable balls into m distinguishable urns (or boxes) in such a way that none of the urns is empty. Solution 1.8 Let un;m denote the total number of ways. Assume that i th urn has ni balls
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uishable balls into m distinguishable urns (or boxes) in such a way that none of the urns is empty. Solution 1.8 Let un;m denote the total number of ways. Assume that i th urn has ni balls so n where each ni > that all urn contents should add up to n. That gives n1 C (cid:1) (cid:1) (cid:1) C x3=3(cid:138) 0. Let x=1(...
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) /m; (1.20) where ak each urn gets one ball), and upper limit is n. The RHS is easily seen to be .ex using binomial theorem to get uk;m and the lower limit is obviously m
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) /m; (1.20) where ak each urn gets one ball), and upper limit is n. The RHS is easily seen to be .ex using binomial theorem to get uk;m and the lower limit is obviously m (because this corresponds to the case where 1/m. Expand it D (cid:0) .ex 1/m (cid:0) D emx (cid:0) ! m 1 e.m (cid:0) 1/x C ! m 2...
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) n k(cid:1) is the classical Laguerre (cid:0) 1.6 POCHHAMMER GENERATING FUNCTIONS Analogous to the formal power series expansions given above, we could also define Poch
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) n k(cid:1) is the classical Laguerre (cid:0) 1.6 POCHHAMMER GENERATING FUNCTIONS Analogous to the formal power series expansions given above, we could also define Pochhammer GF as follows. Here the series remains the same but the dummy variable is either rising factorial or falling factorial type. 1.6.1 RISING P...
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; k/xn=n(cid:138) ond kind is P1n D constants to get falling EGF for Pochhammer numbers b.k/. xn. The 0 S.n; k/x.k/ x.n/. The
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; k/xn=n(cid:138) ond kind is P1n D constants to get falling EGF for Pochhammer numbers b.k/. xn. The 0 S.n; k/x.k/ x.n/. The EGF of Stirling number x//k and that of Stirling number of sec- 1/k=k(cid:138). The falling factorial can also be applied to the n k D .1=k(cid:138)/.log.1 k s.n; k/xn=n(cid:138) 0 s.n; k/xk...
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/.x x.x 1 neighbors to it so that 1 choices. Continue arguing like this until a single node is left. Hence, the GF is 2/ : : : .x 1/ or x.n/ as the required
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/.x x.x 1 neighbors to it so that 1 choices. Continue arguing like this until a single node is left. Hence, the GF is 2/ : : : .x 1/ or x.n/ as the required GF. (cid:0) n (cid:0) (cid:0) (cid:0) (cid:0) C 16 1. TYPES OF GENERATING FUNCTIONS 1.7 OTHER GENERATING FUNCTIONS There are many other GFs used in va...
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GF. n aj j where D a j j Solution 1.11 Split n as P(cid:0) n 1=.1 a/ nt (cid:0) a(cid:
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