# Stochastic Inventory Control

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Chapter 3 Stochastic Inventory Control 1 In this chapter, we consider in much greater details certain dynamic inventory control problems of the type already encountered in section 1.3. In addition to the fact that this is a classical topic in stochastic control, we emphasize the following important idea. The dynamic programming algorithm is not only useful for computations, it is also a basic tool for the theoretical investigation of control problems. Using it, we prove here the optimality of the class of socalled base stock and (s, S)-policies for a classical formulation of the inventory management problem. References for this chapter are [Ber07, section 4.2], [Por02]. Numerous variations are possible starting from the basic inventory control problem, which aim at capturing different real-world situations. 3.1 Problem Formulation In the dynamic inventory control problem (for a single product), the state x k represents the level of inventory at the beginning of period k. We consider a slightly different formulation compared to section (1.3), and assume backlogging rather than lost sales if the demand at a given period cannot be met. Hence we assume now that x k can take positive or negative values, which in the later case represents backlogged orders. Backlogged orders must be fulfilled first at the next period. Moreover, a unit penalty cost c B is charged for each unit that is backlogged at each period. Using this device in fact allows us to ignore sales revenues and simply seek to minimize the expected cost of running the system. Each unit of positive leftover stock at the end of a period incurs a holding cost c H 0 in that period. The time axis is divided as usual into discrete periods k = 0, 1,..., N. At the beginning of period k, we observe the current inventory level x k. We can decide how much additional product u k to order, and we assume that the order is fulfilled immediately, i.e., is received in time to serve the demand w k for that period. This demand is uncertain but we assume that it is stochastic, and more precisely that the w k s are independent identically distributed (i.i.d.) and 1 This version: September

2 bounded random variables with a known distribution 2. The state dynamics (1.1) are then x k+1 = x k + u k w k,k=0, 1,..., N 1. The (convex) holding and shortage cost for a period is of the form (see Fig. 3.1) L(x) =c B ( x) + + c H x +, where x + = max(0,x). In addition, there is a cost c 0 per unit of ordered product. We assume c B >c, so that it is not optimal to never order anything and simply accumulate backlog penalty costs. We also assume c + c H > 0 (so at least one of these coefficients is positive). Finally, we assume that if the inventory level at period N is x, aconvex and nonnegative terminal cost v N (x) is incurred 3. We wish to minimize the total cost [ N 1 ] E (cu k + L(x k + u k w k )) + v N (x N ). k=0 Note that we decided, somewhat arbitrarily, that the shortage and holding costs are both charged at the end of each period (i.e., for the inventory left after the demand is realized). Applying the DP algorithm (1.12, 1.13), we obtain JN (x N )=v N (x N ) Jk [ ]} (x k ) = min cuk + L(x k + u k )+E wk J k+1 (x k + u k w k, 0 k N 1, u k 0 where we defined L(y) =E w [L(y w)], and E w is the expectation with respect to the distribution of w 0 (recall that we assume the w i s to be i.i.d.). 3.2 Proving Properties of the Value Function via DP and Backward Induction For this problem, it is convenient to make a change of variable and define the new control to be the level of inventory after ordering y k = x k + u k, with the corresponding constraint y k x k. Define G k (y) =cy + L(y)+E w [J k+1(y w)]. (3.1) Then we can rewrite the DP recursion as } J k (x k )= min G k (y k )} cx k. (3.2) y k x k 2 Everything probably works with w k dependent on x k,u k as in our basic model, but I haven t checked the details. Also, probably something weaker than the boundedness assumption works 3 it might be possible to remove the nonnegativity assumption. I leave this for a future revision. 28

3 L(x) c B c H Figure 3.1: Immediate cost function in the dynamic inventory management problem. x Hence we see that the main issue becomes to understand the minimization problem for G k (y) over the interval [x k, ). Now assume for a moment that G k has an unconstrained minimum at S k S k arg min y R G k(y). Suppose also that for any such minimizer and y S k, G k is nondecreasing (see Fig. 3.2). Then (in addition to guaranteeing that the set of minimizers is an interval), we see that the constrained minimization problem in this case has a simple solution: set y = x k if x k S k, and y = S k if x k <S k. So we bring the inventory level back to S k as soon as it drops below this level. In terms of the original control parameter, we would have the optimal policy of the form µ S k x k if x k <S k k(x k )= (3.3) 0 if x k S k. We will prove shortly that our conjecture on the shape of G k is true, and that in fact something much stronger holds, namely that G k is convex 4 and coercive, i.e., lim y + G k(y) =+. A decision rule of the form (3.3) is called a base stock policy. For practical use such a policy is very convenient 5. We only need to compute in advance and record the set of scalars S 0,S 1,..., S N 1, called the base stock levels. Then at the beginning of period k, the inventory manager simply observes the inventory 4 convexity could be guessed directly for Jk, as a result of partial minimization in a stochastic convex program. 5 There are also computational advantages to know that there is such a simple parametrization of the optimal policy, which will become clear once we look at approximation methods, rollout and policy iteration. 29

4 G k (y) S k y Figure 3.2: Structure of the function G k guaranteeing the optimality of base stock policies with no fixed cost. level x k, does nothing if x k S k, and brings it to level S k otherwise by ordering the quantity S k x k. Our goal for the rest of the section is thus to prove that, for all 0 k N 1, the function G k is convex and coercive. This guarantees the existence of the base stock levels S k and the optimality of the base stock policy (3.3). The method employed to do this is very important and must be remembered. We use the DP algorithm and a backward induction argument to prove a certain property of the optimal value function, in this case convexity. First note the following Lemma If Jk+1 is convex and nonnegative, then G k is convex and coercive. Proof. This follows from the definition (3.1). First, by exercise 6 below, y L(y) and y E w [J k+1 (y w)] are convex, so G k is convex. Because J k+1 is nonnegative, we have G k (y) cy +L(y). Then lim y + G k (y) =+ because c + c H > 0, and lim y G k (y) =+ because c B >c(and E[w] < ). Exercise 6. Prove the result skipped above, namely, if h : R R is a convex function, then y E w [h(y w)] is also convex. Hence everything is proved if we show the following Lemma The value function Jk for all 0 k N. is a convex and nonnegative function, Proof. We proceed using backward induction. For k = N, we have JN (x) = v N (x) and so the lemma is true by hypothesis. Suppose now that Jk+1 is 30

5 nonnegative and convex, we show that Jk is also nonnegative and convex. Now using lemma 3.2.1, we deduce the existence of S k, a minimizer of G k. As shown previously, the minimizer in the DP equation (3.2) is given by (3.3). We have then J k (x k )= c(s k x k )+L(S k )+E w [Jk+1 (S k w k )] if x k <S k L(x k )+E w [Jk+1 (x k w k )] if x k S k. or equivalently: J k (x k )= G k (S k ) cx k if x k <S k G k (x k ) cx k if x k S k. Now the nonnegativity of Jk is immediate from the first expression since J k+1 and L are nonnegative. For the convexity, consider the second expression. The function which is constant equal to G k (S k ) for x k <S k and then equal to G k (x k ) for x k S k is convex, since S k is a minimizer of G k (which is convex by lemma and our induction hypothesis). Adding the linear function x cx preserves convexity, so Jk (x k) is convex. This concludes the induction step. Theorem The base stock policy (3.3) is optimal at each period of the finite-horizon inventory control problem. Exercise 7. Convince yourself that we showed that the base stock policy (3.3) is indeed optimal by collecting the elements for the proof of the theorem Remark. In a problem that you encounter for the first time, it can be hard to find which property of the value function will be useful... Typically you can try to compute the solution for problems with a few periods (starting with 1; actually here the inventory control problem with one period is also important in management science, it is called the newsvendor problem). Then try to find a pattern that can be propagated through induction. The next section introduces a complication to the previous formulation that gives an additional example of the technique, and the problems and following chapters will also give you the opportunity to practice. It is useful to know some basic results and examples of problems with a nice structure. However, properties of the value function tend to be quite fragile, i.e., small changes in the assumptions of the problem can have a dramatic impact on the properties of the value function. 3.3 Adding a Positive Fixed Ordering Cost Suppose now that ordering any quantity of product involves a fixed positive cost K in addition to the unit ordering cost. Thus, the cost for ordering a quantity u of product is 0 if no product is ordered and K + cu if u>0. The addition of this fixed cost significantly complicates the analysis of the problem, and in particular the value function is not convex any more in general. A 31

6 more general notion, K-convexity, was introduced by Scarf [Sca60] to solve this problem. Intuitively, the base stock policy (3.3) should not be optimal in the presence of a fixed cost, which penalizes the frequent ordering of small quantities. Instead, we should delay new orders so that the fixed cost does not represent an exaggerated proportion of the total ordering cost. The optimal policy turns out to be characterized by two levels denoted S k and s k at each period, with s k <S k. It consist in waiting that the inventory level drops below s k and then prescribes to bring the inventory back to level S k. Such a policy is imaginatively called an (s, S) policy. The DP recursion step (3.2) is now replaced by } Jk (x k ) = min G k (x k ), min (K + G k (y k )) cx k, (3.4) y k >x k again using as new control variable y k = x k + u k, the level of inventory after ordering. The first term in the minimization corresponds to not ordering any product. To understand this minimization problem, we plot G k (y), and look for the values y greater or equal to x k. If there is a value of G k (y) that is smaller than G k (x k ) by more than K, then it is advantageous to move to that level of inventory. Referring to Fig. 3.3, we see that for x k s k, we should set y k = S k, whereas for x k >s k, we should not change the inventory level because the function G k (y) never drops below G k (x k ) by more than K for y x k. We will see however that this function is not K-convex, hence K-convexity is only a sufficient condition guaranteeing the optimality of (s, S) policies. On the other hand, on Fig. 3.4 we have a function G k for which an (s, S) policy is not optimal. Indeed if we have s k x k t with t > S k and s k, S k defined on the figure, then it is optimal to bring the level of inventory to S k instead of leaving it unchanged. This situation will be ruled out by K-convexity. K-convexity is a generalization of convexity for functions of a single real variable. There are a number of equivalent characterizations of K-convexity, but perhaps the easiest way of seeing that the functions on Fig. 3.3 and 3.4 are not K-convex is by using the following definition Definition A function f : R R is K-convex, with K 0, if for each y y, 0 θ 1, we have f(θy + (1 θ)y ) θf(y) + (1 θ)(k + f(y )). Hence clearly a 0-convex function is synonymous with a convex function. For a function to be K-convex, it must lie below the line segment connecting (y, f(y)) and (y,f(y )+K) on the interval [y, y ]. Such a function can be discontinuous, but the jumps at the discontinuity point must necessarily be downwards and cannot be too large. A K-convex function for K > 0 need not be convex or even quasi-convex, and can have several local minima. The following property is immediate from the definition. Lemma If f is K-convex, y < y, and f(y) =K + f(y ), then f(z) K + f(y ) for all z [y, y ]. 32

7 Exercise 8. Prove Lemma A K-convex function can cross the value K +f(y) at most once on (,y). We see immediately then that the function on Fig. 3.3 is not K-convex because it does not remain under the horizontal dotted line f(y )+K on the interval [y, y ] shown on the figure. The same idea applies to the function of Fig Here are some additional properties of K-convex functions. Lemma A function f : R R is K-convex iff K + f(y + a) f(y)+ a [f(y) f(y b)], for all x R,a 0, b > 0. b 2. If f is differentiable, then f is K-convex iff K + f(y) f(x)+f (x)(y x), for all x y. 3. If f 1 and f 2 are K- and L-convex (K, L 0), and α, β > 0, then αf 1 +βf 2 is (αk + βl)-convex. 4. If f is K-convex and w is a random variable, then E w [f(y w)] is also K-convex, provided that E w [ f(y w) ] < for all y. Exercise 9. Prove Lemma Next we show that if G k is K-convex, continuous, and coercive, then an (s, S) policy is optimal at stage k. Lemma If f is a continuous and coercive K-convex function, then there exist scalars s S such that 1. S minimizes f: f(s) f(y), y R. 2. f(s)+k = f(s) and f(y) >f(s) for all y < s. 3. f(y) is a decreasing function on (,s). 4. f(y) f(y )+K for all s y y. Proof. S exists because f is continuous and coercive. Define s to be the smallest number z in R such that z S and f(s)+k = f(z). The rest follows more or less directly from lemma 3.3.1, it is best to draw a picture. Exercise 10. Use lemma to show that if G k is continuous, K-convex and coercive, with K equal to the fixed ordering cost, then an (s, S) policy is optimal at stage k. Give a characterization of the inventory levels s and S in terms of the value of G k at these points. The final step consists in showing that G k is K-convex, continuous, and coercive, for k =0,..., N 1. This implies the optimality of an (s, S) policy at all stages by lemma and exercise 10. As you have probably guessed by now, we show the crucial properties of G k by backward induction. The proof follows the argument of section 3.2 with convexity replaced by K-convexity. 33

8 Lemma If J k+1 is K-convex, nonnegative and continuous, then G k is K-convex, coercive, and continuous. Proof. K-convexity and coercivity of G k follow as in lemma 3.2.1, using the properties in lemma Continuity of G k uses the assumption that w is bounded to get that L(y) and E w [Jk+1 (y w)] are continuous.6 Hence it is now sufficient to show the following result. is a K-convex, nonnegative and contin- Lemma The value function Jk uous function, for all 0 k N. Proof. At stage N, we have JN (x N)=v N (x N ) convex and nonnegative, so the result is true by hypothesis. Now assume that Jk+1 is K-convex, nonnegative and continuous. This implies that G k is K-convex, coercive and continuous by lemma Next, Jk is defined by (3.4). We deduce that J k has the following form Jk K + G k (S k ) cx, for x s k, (x) = G k (x) cx, for x s k, where s k,s k are defined as in lemma Jk is continuous, by definition of s k and since G k is continuous. The nonnegativity of Jk follows say from the definition of Jk as cost-to-go, where all costs are nonnegative. We want to show that Jk is K-convex. Equivalently since x cx is 0-convex and by lemma 3.3.2, we want to show that G k defined by G k (x) =Jk (x) +cx is K- convex. We consider definition If s k y y then G k = G k with G k K-convex so the K-convexity inequality is satisfied. If y y s k then G k is constant (equal to G k (s k )=K + G k (S k )) so again the K-convexity inequality is satisfied. The remaining case is y < s k <y. Consider z [y, y ]. We need to show that G k (z) is below the line segment connecting (y, G k (s k )) (since G k (y) =G k (s k )) and (y,g k (y )+K) (since G k (y )=G k (y )). First note that K + G k (y ) K + G k (S k )= G k (y) so this line segment is increasing. Then if y z s k this is clear because G k is constant on that interval. If s k z y we first have by K-convexity on [s k,y] that G k (z) is below the line segment connecting (s k,g k (s k )) and (y,g k (y )+K). Then it is not hard to see (draw it!) that this later line is itself below the line of interest. This concludes the proof of the K-convexity of Jk and the induction step. 3.4 Practice Problems Problem Do all the exercises found in the chapter. 6 a better argument and assumption on w can probably be made here. 34

9 G k (y) K K y y s k S k y Figure 3.3: Structure of the function G k guaranteeing the optimality of an (s, S) policy (although this function is not K-convex). G k (y) K K s k S k t s k Sk y Figure 3.4: A function G k for which an (s, S) policy is not optimal. 35

### Reading 7 : Program Correctness

CS/Math 240: Introduction to Discrete Mathematics Fall 2015 Instructors: Beck Hasti, Gautam Prakriya Reading 7 : Program Correctness 7.1 Program Correctness Showing that a program is correct means that

### 6.231 Dynamic Programming and Stochastic Control Fall 2008

MIT OpenCourseWare http://ocw.mit.edu 6.231 Dynamic Programming and Stochastic Control Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 6.231

### Econometrics Simple Linear Regression

Econometrics Simple Linear Regression Burcu Eke UC3M Linear equations with one variable Recall what a linear equation is: y = b 0 + b 1 x is a linear equation with one variable, or equivalently, a straight

### Decomposition Approach to Serial Inventory Systems under Uncertainties

The University of Melbourne, Department of Mathematics and Statistics Decomposition Approach to Serial Inventory Systems under Uncertainties Jennifer Kusuma Honours Thesis, 2005 Supervisors: Prof. Peter

### 1.3 Induction and Other Proof Techniques

4CHAPTER 1. INTRODUCTORY MATERIAL: SETS, FUNCTIONS AND MATHEMATICAL INDU 1.3 Induction and Other Proof Techniques The purpose of this section is to study the proof technique known as mathematical induction.

Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650

### Deterministic Problems

Chapter 2 Deterministic Problems 1 In this chapter, we focus on deterministic control problems (with perfect information), i.e, there is no disturbance w k in the dynamics equation. For such problems there

### Date: April 12, 2001. Contents

2 Lagrange Multipliers Date: April 12, 2001 Contents 2.1. Introduction to Lagrange Multipliers......... p. 2 2.2. Enhanced Fritz John Optimality Conditions...... p. 12 2.3. Informative Lagrange Multipliers...........

### The Trip Scheduling Problem

The Trip Scheduling Problem Claudia Archetti Department of Quantitative Methods, University of Brescia Contrada Santa Chiara 50, 25122 Brescia, Italy Martin Savelsbergh School of Industrial and Systems

### 2.3 Convex Constrained Optimization Problems

42 CHAPTER 2. FUNDAMENTAL CONCEPTS IN CONVEX OPTIMIZATION Theorem 15 Let f : R n R and h : R R. Consider g(x) = h(f(x)) for all x R n. The function g is convex if either of the following two conditions

### 3.3. Solving Polynomial Equations. Introduction. Prerequisites. Learning Outcomes

Solving Polynomial Equations 3.3 Introduction Linear and quadratic equations, dealt within Sections 3.1 and 3.2, are members of a class of equations, called polynomial equations. These have the general

### On spare parts optimization

On spare parts optimization Krister Svanberg Optimization and Systems Theory, KTH, Stockholm, Sweden. krille@math.kth.se This manuscript deals with some mathematical optimization models for multi-level

### Probability Generating Functions

page 39 Chapter 3 Probability Generating Functions 3 Preamble: Generating Functions Generating functions are widely used in mathematics, and play an important role in probability theory Consider a sequence

### CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,

### Further Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1

Further Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1 J. Zhang Institute of Applied Mathematics, Chongqing University of Posts and Telecommunications, Chongqing

### The Epsilon-Delta Limit Definition:

The Epsilon-Delta Limit Definition: A Few Examples Nick Rauh 1. Prove that lim x a x 2 = a 2. (Since we leave a arbitrary, this is the same as showing x 2 is continuous.) Proof: Let > 0. We wish to find

### t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d).

1. Line Search Methods Let f : R n R be given and suppose that x c is our current best estimate of a solution to P min x R nf(x). A standard method for improving the estimate x c is to choose a direction

### FIRST YEAR CALCULUS. Chapter 7 CONTINUITY. It is a parabola, and we can draw this parabola without lifting our pencil from the paper.

FIRST YEAR CALCULUS WWLCHENW L c WWWL W L Chen, 1982, 2008. 2006. This chapter originates from material used by the author at Imperial College, University of London, between 1981 and 1990. It It is is

### In this section, we will consider techniques for solving problems of this type.

Constrained optimisation roblems in economics typically involve maximising some quantity, such as utility or profit, subject to a constraint for example income. We shall therefore need techniques for solving

### 1 Portfolio mean and variance

Copyright c 2005 by Karl Sigman Portfolio mean and variance Here we study the performance of a one-period investment X 0 > 0 (dollars) shared among several different assets. Our criterion for measuring

### Single item inventory control under periodic review and a minimum order quantity

Single item inventory control under periodic review and a minimum order quantity G. P. Kiesmüller, A.G. de Kok, S. Dabia Faculty of Technology Management, Technische Universiteit Eindhoven, P.O. Box 513,

### x if x 0, x if x < 0.

Chapter 3 Sequences In this chapter, we discuss sequences. We say what it means for a sequence to converge, and define the limit of a convergent sequence. We begin with some preliminary results about the

### A Production Planning Problem

A Production Planning Problem Suppose a production manager is responsible for scheduling the monthly production levels of a certain product for a planning horizon of twelve months. For planning purposes,

### Mathematics for Economics (Part I) Note 5: Convex Sets and Concave Functions

Natalia Lazzati Mathematics for Economics (Part I) Note 5: Convex Sets and Concave Functions Note 5 is based on Madden (1986, Ch. 1, 2, 4 and 7) and Simon and Blume (1994, Ch. 13 and 21). Concave functions

### Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh

Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh Peter Richtárik Week 3 Randomized Coordinate Descent With Arbitrary Sampling January 27, 2016 1 / 30 The Problem

### ALGORITHMIC TRADING WITH MARKOV CHAINS

June 16, 2010 ALGORITHMIC TRADING WITH MARKOV CHAINS HENRIK HULT AND JONAS KIESSLING Abstract. An order book consists of a list of all buy and sell offers, represented by price and quantity, available

### Notes from Week 1: Algorithms for sequential prediction

CS 683 Learning, Games, and Electronic Markets Spring 2007 Notes from Week 1: Algorithms for sequential prediction Instructor: Robert Kleinberg 22-26 Jan 2007 1 Introduction In this course we will be looking

### THE FUNDAMENTAL THEOREM OF ARBITRAGE PRICING

THE FUNDAMENTAL THEOREM OF ARBITRAGE PRICING 1. Introduction The Black-Scholes theory, which is the main subject of this course and its sequel, is based on the Efficient Market Hypothesis, that arbitrages

### We call this set an n-dimensional parallelogram (with one vertex 0). We also refer to the vectors x 1,..., x n as the edges of P.

Volumes of parallelograms 1 Chapter 8 Volumes of parallelograms In the present short chapter we are going to discuss the elementary geometrical objects which we call parallelograms. These are going to

### A Comparison of the Optimal Costs of Two Canonical Inventory Systems

A Comparison of the Optimal Costs of Two Canonical Inventory Systems Ganesh Janakiraman 1, Sridhar Seshadri 2, J. George Shanthikumar 3 First Version: December 2005 First Revision: July 2006 Subject Classification:

### Reinforcement Learning

Reinforcement Learning LU 2 - Markov Decision Problems and Dynamic Programming Dr. Martin Lauer AG Maschinelles Lernen und Natürlichsprachliche Systeme Albert-Ludwigs-Universität Freiburg martin.lauer@kit.edu

### Math 55: Discrete Mathematics

Math 55: Discrete Mathematics UC Berkeley, Fall 2011 Homework # 5, due Wednesday, February 22 5.1.4 Let P (n) be the statement that 1 3 + 2 3 + + n 3 = (n(n + 1)/2) 2 for the positive integer n. a) What

### Critical points of once continuously differentiable functions are important because they are the only points that can be local maxima or minima.

Lecture 0: Convexity and Optimization We say that if f is a once continuously differentiable function on an interval I, and x is a point in the interior of I that x is a critical point of f if f (x) =

### STRATEGIC CAPACITY PLANNING USING STOCK CONTROL MODEL

Session 6. Applications of Mathematical Methods to Logistics and Business Proceedings of the 9th International Conference Reliability and Statistics in Transportation and Communication (RelStat 09), 21

### Principle of Data Reduction

Chapter 6 Principle of Data Reduction 6.1 Introduction An experimenter uses the information in a sample X 1,..., X n to make inferences about an unknown parameter θ. If the sample size n is large, then

### 1 Limiting distribution for a Markov chain

Copyright c 2009 by Karl Sigman Limiting distribution for a Markov chain In these Lecture Notes, we shall study the limiting behavior of Markov chains as time n In particular, under suitable easy-to-check

### 5. Convergence of sequences of random variables

5. Convergence of sequences of random variables Throughout this chapter we assume that {X, X 2,...} is a sequence of r.v. and X is a r.v., and all of them are defined on the same probability space (Ω,

### WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT?

WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT? introduction Many students seem to have trouble with the notion of a mathematical proof. People that come to a course like Math 216, who certainly

### Lecture Notes on Elasticity of Substitution

Lecture Notes on Elasticity of Substitution Ted Bergstrom, UCSB Economics 210A March 3, 2011 Today s featured guest is the elasticity of substitution. Elasticity of a function of a single variable Before

### Bargaining Solutions in a Social Network

Bargaining Solutions in a Social Network Tanmoy Chakraborty and Michael Kearns Department of Computer and Information Science University of Pennsylvania Abstract. We study the concept of bargaining solutions,

### 1 Approximating Set Cover

CS 05: Algorithms (Grad) Feb 2-24, 2005 Approximating Set Cover. Definition An Instance (X, F ) of the set-covering problem consists of a finite set X and a family F of subset of X, such that every elemennt

### Reinforcement Learning

Reinforcement Learning LU 2 - Markov Decision Problems and Dynamic Programming Dr. Joschka Bödecker AG Maschinelles Lernen und Natürlichsprachliche Systeme Albert-Ludwigs-Universität Freiburg jboedeck@informatik.uni-freiburg.de

### Equations Involving Lines and Planes Standard equations for lines in space

Equations Involving Lines and Planes In this section we will collect various important formulas regarding equations of lines and planes in three dimensional space Reminder regarding notation: any quantity

### Numerical methods for American options

Lecture 9 Numerical methods for American options Lecture Notes by Andrzej Palczewski Computational Finance p. 1 American options The holder of an American option has the right to exercise it at any moment

### 1 if 1 x 0 1 if 0 x 1

Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or

### Sufficient Conditions for Monotone Value Functions in Multidimensional Markov Decision Processes: The Multiproduct Batch Dispatch Problem

Sufficient Conditions for Monotone Value Functions in Multidimensional Markov Decision Processes: The Multiproduct Batch Dispatch Problem Katerina Papadaki Warren B. Powell October 7, 2005 Abstract Structural

### EMBEDDING COUNTABLE PARTIAL ORDERINGS IN THE DEGREES

EMBEDDING COUNTABLE PARTIAL ORDERINGS IN THE ENUMERATION DEGREES AND THE ω-enumeration DEGREES MARIYA I. SOSKOVA AND IVAN N. SOSKOV 1. Introduction One of the most basic measures of the complexity of a

### FACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z

FACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z DANIEL BIRMAJER, JUAN B GIL, AND MICHAEL WEINER Abstract We consider polynomials with integer coefficients and discuss their factorization

### it is easy to see that α = a

21. Polynomial rings Let us now turn out attention to determining the prime elements of a polynomial ring, where the coefficient ring is a field. We already know that such a polynomial ring is a UF. Therefore

### Offline sorting buffers on Line

Offline sorting buffers on Line Rohit Khandekar 1 and Vinayaka Pandit 2 1 University of Waterloo, ON, Canada. email: rkhandekar@gmail.com 2 IBM India Research Lab, New Delhi. email: pvinayak@in.ibm.com

### Linear Span and Bases

MAT067 University of California, Davis Winter 2007 Linear Span and Bases Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (January 23, 2007) Intuition probably tells you that the plane R 2 is of dimension

### Subsets of Euclidean domains possessing a unique division algorithm

Subsets of Euclidean domains possessing a unique division algorithm Andrew D. Lewis 2009/03/16 Abstract Subsets of a Euclidean domain are characterised with the following objectives: (1) ensuring uniqueness

### Optimal shift scheduling with a global service level constraint

Optimal shift scheduling with a global service level constraint Ger Koole & Erik van der Sluis Vrije Universiteit Division of Mathematics and Computer Science De Boelelaan 1081a, 1081 HV Amsterdam The

### CHAPTER IV NORMED LINEAR SPACES AND BANACH SPACES

CHAPTER IV NORMED LINEAR SPACES AND BANACH SPACES DEFINITION A Banach space is a real normed linear space that is a complete metric space in the metric defined by its norm. A complex Banach space is a

### Some Polynomial Theorems. John Kennedy Mathematics Department Santa Monica College 1900 Pico Blvd. Santa Monica, CA 90405 rkennedy@ix.netcom.

Some Polynomial Theorems by John Kennedy Mathematics Department Santa Monica College 1900 Pico Blvd. Santa Monica, CA 90405 rkennedy@ix.netcom.com This paper contains a collection of 31 theorems, lemmas,

### Lecture 2: Abhyankar s Results on the Newton Polygon

Lecture 2: Abhyankar s Results on the Newton Polygon Definition. Let P(X, Y ) = p ij X i Y j C[X, Y ]. The support of P, S(P), is the set {(i, j) N N f ij 0}. The Newton polygon of P, N(P), is the convex

### Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2

CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2 Proofs Intuitively, the concept of proof should already be familiar We all like to assert things, and few of us

### Nonlinear Algebraic Equations. Lectures INF2320 p. 1/88

Nonlinear Algebraic Equations Lectures INF2320 p. 1/88 Lectures INF2320 p. 2/88 Nonlinear algebraic equations When solving the system u (t) = g(u), u(0) = u 0, (1) with an implicit Euler scheme we have

### Gambling and Data Compression

Gambling and Data Compression Gambling. Horse Race Definition The wealth relative S(X) = b(x)o(x) is the factor by which the gambler s wealth grows if horse X wins the race, where b(x) is the fraction

### Theorem 2. If x Q and y R \ Q, then. (a) x + y R \ Q, and. (b) xy Q.

Math 305 Fall 011 The Density of Q in R The following two theorems tell us what happens when we add and multiply by rational numbers. For the first one, we see that if we add or multiply two rational numbers

### The Steepest Descent Algorithm for Unconstrained Optimization and a Bisection Line-search Method

The Steepest Descent Algorithm for Unconstrained Optimization and a Bisection Line-search Method Robert M. Freund February, 004 004 Massachusetts Institute of Technology. 1 1 The Algorithm The problem

### Maximum Likelihood Estimation

Math 541: Statistical Theory II Lecturer: Songfeng Zheng Maximum Likelihood Estimation 1 Maximum Likelihood Estimation Maximum likelihood is a relatively simple method of constructing an estimator for

### Applied Algorithm Design Lecture 5

Applied Algorithm Design Lecture 5 Pietro Michiardi Eurecom Pietro Michiardi (Eurecom) Applied Algorithm Design Lecture 5 1 / 86 Approximation Algorithms Pietro Michiardi (Eurecom) Applied Algorithm Design

### Choice under Uncertainty

Choice under Uncertainty Part 1: Expected Utility Function, Attitudes towards Risk, Demand for Insurance Slide 1 Choice under Uncertainty We ll analyze the underlying assumptions of expected utility theory

### Chapter 2: Binomial Methods and the Black-Scholes Formula

Chapter 2: Binomial Methods and the Black-Scholes Formula 2.1 Binomial Trees We consider a financial market consisting of a bond B t = B(t), a stock S t = S(t), and a call-option C t = C(t), where the

### LOOKING FOR A GOOD TIME TO BET

LOOKING FOR A GOOD TIME TO BET LAURENT SERLET Abstract. Suppose that the cards of a well shuffled deck of cards are turned up one after another. At any time-but once only- you may bet that the next card

### Numerisches Rechnen. (für Informatiker) M. Grepl J. Berger & J.T. Frings. Institut für Geometrie und Praktische Mathematik RWTH Aachen

(für Informatiker) M. Grepl J. Berger & J.T. Frings Institut für Geometrie und Praktische Mathematik RWTH Aachen Wintersemester 2010/11 Problem Statement Unconstrained Optimality Conditions Constrained

CS787: Advanced Algorithms Topic: Online Learning Presenters: David He, Chris Hopman 17.3.1 Follow the Perturbed Leader 17.3.1.1 Prediction Problem Recall the prediction problem that we discussed in class.

### Cubic Functions: Global Analysis

Chapter 14 Cubic Functions: Global Analysis The Essential Question, 231 Concavity-sign, 232 Slope-sign, 234 Extremum, 235 Height-sign, 236 0-Concavity Location, 237 0-Slope Location, 239 Extremum Location,

### The Exponential Distribution

21 The Exponential Distribution From Discrete-Time to Continuous-Time: In Chapter 6 of the text we will be considering Markov processes in continuous time. In a sense, we already have a very good understanding

### Chapter Three. Functions. In this section, we study what is undoubtedly the most fundamental type of relation used in mathematics.

Chapter Three Functions 3.1 INTRODUCTION In this section, we study what is undoubtedly the most fundamental type of relation used in mathematics. Definition 3.1: Given sets X and Y, a function from X to

### ON PRICE CAPS UNDER UNCERTAINTY

ON PRICE CAPS UNDER UNCERTAINTY ROBERT EARLE CHARLES RIVER ASSOCIATES KARL SCHMEDDERS KSM-MEDS, NORTHWESTERN UNIVERSITY TYMON TATUR DEPT. OF ECONOMICS, PRINCETON UNIVERSITY APRIL 2004 Abstract. This paper

### 1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let

Copyright c 2009 by Karl Sigman 1 Stopping Times 1.1 Stopping Times: Definition Given a stochastic process X = {X n : n 0}, a random time τ is a discrete random variable on the same probability space as

### 2.3 Bounds of sets of real numbers

2.3 Bounds of sets of real numbers 2.3.1 Upper bounds of a set; the least upper bound (supremum) Consider S a set of real numbers. S is called bounded above if there is a number M so that any x S is less

### MATH 425, PRACTICE FINAL EXAM SOLUTIONS.

MATH 45, PRACTICE FINAL EXAM SOLUTIONS. Exercise. a Is the operator L defined on smooth functions of x, y by L u := u xx + cosu linear? b Does the answer change if we replace the operator L by the operator

### OPRE 6201 : 2. Simplex Method

OPRE 6201 : 2. Simplex Method 1 The Graphical Method: An Example Consider the following linear program: Max 4x 1 +3x 2 Subject to: 2x 1 +3x 2 6 (1) 3x 1 +2x 2 3 (2) 2x 2 5 (3) 2x 1 +x 2 4 (4) x 1, x 2

### Partitioning edge-coloured complete graphs into monochromatic cycles and paths

arxiv:1205.5492v1 [math.co] 24 May 2012 Partitioning edge-coloured complete graphs into monochromatic cycles and paths Alexey Pokrovskiy Departement of Mathematics, London School of Economics and Political

### ON LIMIT LAWS FOR CENTRAL ORDER STATISTICS UNDER POWER NORMALIZATION. E. I. Pancheva, A. Gacovska-Barandovska

Pliska Stud. Math. Bulgar. 22 (2015), STUDIA MATHEMATICA BULGARICA ON LIMIT LAWS FOR CENTRAL ORDER STATISTICS UNDER POWER NORMALIZATION E. I. Pancheva, A. Gacovska-Barandovska Smirnov (1949) derived four

### Example Correctness Proofs

Example Correctness Proofs Kevin C. Zatloukal March 8, 2014 In these notes, we give three examples of proofs of correctness. These are intended to demonstrate the sort of proofs we would like to see in

### Chapter 21: The Discounted Utility Model

Chapter 21: The Discounted Utility Model 21.1: Introduction This is an important chapter in that it introduces, and explores the implications of, an empirically relevant utility function representing intertemporal

### COMP 250 Fall Mathematical induction Sept. 26, (n 1) + n = n + (n 1)

COMP 50 Fall 016 9 - Mathematical induction Sept 6, 016 You will see many examples in this course and upcoming courses of algorithms for solving various problems It many cases, it will be obvious that

### Lecture Notes on Elasticity of Substitution

Lecture Notes on Elasticity of Substitution Ted Bergstrom, UCSB Economics 20A October 26, 205 Today s featured guest is the elasticity of substitution. Elasticity of a function of a single variable Before

### Supply Chain Coordination with Financial Constraints and Bankruptcy Costs

Submitted to Management Science manuscript Supply Chain Coordination with Financial Constraints and Bankruptcy Costs Panos Kouvelis, Wenhui Zhao Olin Business School, Washington University, St. Louis,

### Taylor Polynomials and Taylor Series Math 126

Taylor Polynomials and Taylor Series Math 26 In many problems in science and engineering we have a function f(x) which is too complicated to answer the questions we d like to ask. In this chapter, we will

### Using pivots to construct confidence intervals. In Example 41 we used the fact that

Using pivots to construct confidence intervals In Example 41 we used the fact that Q( X, µ) = X µ σ/ n N(0, 1) for all µ. We then said Q( X, µ) z α/2 with probability 1 α, and converted this into a statement

### 3. Mathematical Induction

3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)

### THEORY OF SIMPLEX METHOD

Chapter THEORY OF SIMPLEX METHOD Mathematical Programming Problems A mathematical programming problem is an optimization problem of finding the values of the unknown variables x, x,, x n that maximize

### On the Relation Between the Benefits of Risk Pooling and the Variability of Demand

On the Relation Between the Benefits of Risk Pooling and the Variability of Demand Yigal Gerchak Dept. of Industrial Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel Fax: 972-3-640-7669, e-mail:

### No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics

No: 10 04 Bilkent University Monotonic Extension Farhad Husseinov Discussion Papers Department of Economics The Discussion Papers of the Department of Economics are intended to make the initial results

### 4: SINGLE-PERIOD MARKET MODELS

4: SINGLE-PERIOD MARKET MODELS Ben Goldys and Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2015 B. Goldys and M. Rutkowski (USydney) Slides 4: Single-Period Market

### Gambling Systems and Multiplication-Invariant Measures

Gambling Systems and Multiplication-Invariant Measures by Jeffrey S. Rosenthal* and Peter O. Schwartz** (May 28, 997.. Introduction. This short paper describes a surprising connection between two previously

### HOMEWORK 5 SOLUTIONS. n!f n (1) lim. ln x n! + xn x. 1 = G n 1 (x). (2) k + 1 n. (n 1)!

Math 7 Fall 205 HOMEWORK 5 SOLUTIONS Problem. 2008 B2 Let F 0 x = ln x. For n 0 and x > 0, let F n+ x = 0 F ntdt. Evaluate n!f n lim n ln n. By directly computing F n x for small n s, we obtain the following

### GenOpt (R) Generic Optimization Program User Manual Version 3.0.0β1

(R) User Manual Environmental Energy Technologies Division Berkeley, CA 94720 http://simulationresearch.lbl.gov Michael Wetter MWetter@lbl.gov February 20, 2009 Notice: This work was supported by the U.S.

### MATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform

MATH 433/533, Fourier Analysis Section 11, The Discrete Fourier Transform Now, instead of considering functions defined on a continuous domain, like the interval [, 1) or the whole real line R, we wish

### Nonparametric adaptive age replacement with a one-cycle criterion

Nonparametric adaptive age replacement with a one-cycle criterion P. Coolen-Schrijner, F.P.A. Coolen Department of Mathematical Sciences University of Durham, Durham, DH1 3LE, UK e-mail: Pauline.Schrijner@durham.ac.uk

### Quotient Rings and Field Extensions

Chapter 5 Quotient Rings and Field Extensions In this chapter we describe a method for producing field extension of a given field. If F is a field, then a field extension is a field K that contains F.

### max cx s.t. Ax c where the matrix A, cost vector c and right hand side b are given and x is a vector of variables. For this example we have x

Linear Programming Linear programming refers to problems stated as maximization or minimization of a linear function subject to constraints that are linear equalities and inequalities. Although the study

### Numerical Analysis Lecture Notes

Numerical Analysis Lecture Notes Peter J. Olver 5. Inner Products and Norms The norm of a vector is a measure of its size. Besides the familiar Euclidean norm based on the dot product, there are a number