MATHEMATICAL PROGRAMMING I
|
|
- Edwina Jewel Underwood
- 5 years ago
- Views:
Transcription
1 MATHEMATICAL PROGRAMMING I Books There is no single course text, but there are many useful books, some more mathematical, others written at a more applied level. A selection is as follows: Bazaraa, Jarvis and Sherali. Linear Programming and Network Flows. Wiley, 2nd Ed. 199 A solid reference text. Christos Papadimitriou and Kenneth Steiglitz Combinatorial Optimization: Algorithms and Complexity. Dover, Recommended - good value. Gass, Saul I. Linear Programming: Methods and Applications, 5th edition. Thomson,1985. Dantzig, George B. Linear Programming and Extensions, Princeton University Press, 196. The most widely cited early textbook in the eld. Chvatal, V., Linear Programming, Freeman, 198. Luenberger, D. Introduction to Linear and Nonlinear Programming, Addison Wesley, Wolsey, Laurence A. Integer programming, Wiley, Taha, H. Operations Research: An Introduction Prentice-Hall, 7th Ed. 2. (More applied, many examples) Winston, Wayne Operations Research Applications & Algorithms, Duxbury Press, 1997 (Totally applied) Useful websites 1. FAQ page at Optimization Technology Center Northwestern University and Argonne National Laboratory 2. My notes are currently at: 1
2 1. Introduction De nition A linear programming problem (or LP) is the optimization (maximization or minimization) of a linear function of n real variables subject to a set of linear constraints. Example 1.1 The following is a LP problem in n = 2 non-negative variables x 1 ; x 2 : maximize x 1 +x 2 O.F. subject to x 1 + x 2 6 Constraint 1 x 1 +2x 2 8 Constraint 2 x 1; x 2 Non-negativity The variables x 1 ; x 2 are the decision variables which can be represented as a vector x in the positive quadrant of a real 2D space R 2. The function f (x 1 ; x 2 ) = x 1 + x 2 we wish to maximize is known as the objective function (OF) and represents the value of a particular choice of x 1 and x 2. The two inequalities that have to be satis ed by a feasible solution to our problem are known as the LP constraints. Finally, the constraints x 1 ; x 2 represent non-negativity of the problem variables. The set of x-values, i.e. all pairs (x 1 ; x 2 ), satisfying all the constraints is a subset S R 2 known as the LP s feasible region. For minimization problems, the value of the OF is required to be as small as possible and f (x 1 ; x 2 ) = f (x) is often referred to as a cost function. Sometimes we denote the objective function by z (x) or z (x). Notes Graphical solution of this example (which will be covered in lectures) is only possible for problems in two variables. Finding the maximum of z (x) is equivalent to nding the minimum of z (x) so we can, for theoretical purposes and without loss of generality (w.l.o.g.), consider either max or min problems only. Any additive constant in z (x) can also be ignored. A problem with a variable x that can take positive or negative values (known as free or unrestricted in sign (u.r.s.) variables) can easily be incorporated into a LP by de ning x = u v with u; v : LP problems are commonly formulated with a mixture of, and = constraints. 2
3 Example 1.2 A rm manufactures two products A and B. To produce each product requires a certain amount of processing on each of three machines I, II, III. The processing time (hours) per unit production of A,B are as given in the table I II III A B The total available production time of the machines I, II, III is 4 hours, 6 hours and hours respectively, each week. If the unit pro t from A and B is $5 and $ respectively, determine the weekly production of A and B which will maximize the rm s pro t. Formulation: Let x 1 be the no. of item A to produce per week Let x 2 be the no. of items of B to produce per week Producing x 1 units of Product A consumes :5x 1 hours on machine I and contributes 5x 1 towards pro t. Producing x 2 items of Product B requires in addition :25x 2 hours on machine I and contributes x 2 towards pro t. The following formulation seeks to maximize pro t: Maximize 5x 1 + x 2 (Objective Function) subject to :5x 1 + :25x 2 4 Constraints :4x 1 + :x :2x 1 + :4x 2... x 1 ; x 2 Non-negativity This is an optimization problem in 2 non-negative decision variables x 1 ; x 2 (the unknowns) and constraints (not counting the non-negativity constraints). More generally, notice that each constraint row can be regarded as a resource constraint. The solution to the LP in this case tells us how best to use scarce resources. Examples of resources that often vary linearly with amounts of production are manpower, materials, time.
4 Example 1. (The diet problem) How to optimize the choice of n foods (e.g. animal feed) when each food has some of each of m nutrients? Suppose a ij = amount of i th nutrient in a unit of j th food, i = 1; :::; m j = 1; :::; n r i = yearly requirement of the i th nutrient, i = 1; :::; m x j = yearly consumption of the j th food, j = 1; :::; n c j = cost per unit of the i th food, j = 1; :::; n: We seek the "best" yearly diet represented by a vector x that satis es the nutritional requirement Ax r and interpret "best" as least cost min c T x s.t. Ax r x 1.1 Standard Form For an LP in standard form, all the constraints are equalities. (apart from non-negativity constraints) Suppose there are m such equality constraints. The LP can be a maximization (MAX) or a minimization (MIN) problem. Let x = (x 1 ; :::; x n ) T be n non-negative real variables. c T = (c 1 ; c 2 ; :::; c n ) be a set of real (OF) coe cients A = (a ij ) be a m n matrix of real coe cients b = (b 1 ; :::; b m ) be a non-negative real r.h.s. vector (sometimes called the requirements vector) The general LP in standard form with n variables and m constraints (MINimization form) is 4
5 Minimize c 1 x 1 + c 2 x 2 + ::: + c n x n = P n j=1 c jx j subject to a 11 x 1 + a 12 x 2 + ::: + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + ::: + a 2n x n = b 2... a m1 x 1 + a m2 x 2 + ::: + a mn x n = b m and x 1 ; x 2 :::; x n For mathematical convenience, note that b i for each i (as mentioned above) rows of A will be assumed to be linearly independent The last condition (a technicality) ensures for m n that a set of m linearly independent columns of A can be found (known as a basis of R m ). Example 1.1 (contd.) To convert this problem to standard form, we introduce two nonnegative slack variables s 1 ; s 2 and rewrite the set of constraints as x 1 + x 2 6 x 1 +2x 2 8 x 1 + x 2 +s 1 = 6 x 1 +2x 2 +s 2 = 8 where are equivalent since s 1 ; s 2. Notice that the problem dimensions are changed to m = 2; n = 4: 1.2 Vector-matrix notation We can write the LP (standard min/maximization form) concisely as Min/max subject to c T x Ax = b x (SF) Note that x is to be interpreted component-wise as each x j : Equivalently, Min/max c T x j Ax = b; x where x = (x 1 ; :::; x n ) T c T = (c 1 ; :::; c n ) is a column vector is a conformable row-vector. Note: In the subsequent notes we will not always adhere strictly (pedantically) to bold face for matrices and vectors. Books also adopt di erent conventions. Where confusion is unlikely we may also write x (the vector x) as a 5
6 row vector with or without a transpose sign. e.g. x = (1; ; ; 5) rather than x T. Usually vectors are in lower case, the exception being A j to denote the j th column of the matrix A: A = a 11 a 12 : : : a 1n a 21 a 22 : : : a 2n.. 1 C A and b = b 1 b 2. 1 C A. a m1 : : : : : : a mn b m Assumptions We suppose that m n; in fact the rank of A is m (full row rank).,the rows of A are linearly independent (no redundant constraints).,it is possible to choose (usually in many ways) a subset of m linearly independent columns of A; to form a basis. B = A j(1) ; A j(2) ; :::; A j(m) The matrix formed from these columns is called the basis matrix B: 1. Canonical form In Example 1.1, the constraints are all in the same direction and the original formulation may be written brie y in canonical maximization form where x = A = x1 maximize subject to ; c x T = ; b = c T x Ax b x (CF1) The problem minimize subject to c T x Ax b x (c.f. diet problem) is said to be in canonical minimization form. (CF2) Notice the direction of the constraint inequalities is determined by whether we have a MAX or a MIN problem. (Intuitively) When maximizing remember that we have a ceiling-type constraint and, when minimizing, a oor-type constraint. 6
7 1.4 General LP problems Any LP problem may be structured into either standard form (SF) or one of the canonical forms (CF1), (CF2) Example 1.4 minimize x 1 2x 2 x subject to x 1 + 2x 2 +x 14 x 1 +2x 2 +4x 12 x 1 x 2 +x = 2 x 1; x 2 u.r.s: x a) Convert the LP to standard form Let x 1 = u 1 v 1 ; x 2 = u 2 v 2 ; x = ( + x ) with x and u j ; v j (j = 1; 2) 1. Introduce a slack variable s 1 to Constraint 1 Introduce a surplus variable s 2 to Constraint 2 This results in minimize u 1 v 1 2u 2 +2v 2 +x (+9) subject to u 1 v 1 + 2u 2 2v 2 x +s 1 = 17 u 1 v 1 +2u 2 2v 2 4x s 2 = 24 u 1 v 1 u 2 +v 2 x = 5 u 1 ; v 1 ; u 2 ; v 2 ; x ; s 1 ; s 2 b) Obtain the canonical minimization form To reverse the inequality in Constraint 1 we multiplied by -1. Replace the equality a T x = b in Constraint by a T x b and a T x b then reverse the latter constraint by a sign change minimize u 1 v 1 2u 2 +2v 2 +x subject to u 1 +v 1 2u 2 +2v 2 +x 17 u 1 v 1 +2u 2 2v 2 4x 24 u 1 v 1 u 2 +v 2 x 5 u 1 +v 1 +u 2 v 2 +x 5 u 1 ; v 1 ; u 2 ; v 2 ; x c) Convert the problem into a maximization Change the objective function (OF) to maximize u 1 +v 1 +2u 2 2v 2 x 7
8 2. Basic solution and extreme points 2.1 Basic solutions The constraints of an LP in standard form are an underdetermined linear equation system A x = b mn n1 m1 with m < n: There are fewer equations than unknowns ) an in nite number of solutions. De nition A solution x to (2.1) corresponding to some basis matrix B that is obtained by setting n m remaining components of x to zero and solving for the remaining m variables is known as a basic solution. If, in addition, x such a solution is said to be feasible for the LP. If we assume (w.l.o.g) that the entries of A; x and b are integers, we can bound from above the absolute value of the components of any basic solution. Lemma (c.f. Papadimitriou & Steiglitz, p.) Let x = (x 1 ; :::; x n ) be a basic solution. Then jx j j m! m 1 where = max fja ij jg i;j = max fjb jjg j=1;:::;m Proof Trivial if x j is non-basic, since x j = : For x j a basic variable, its value is the sum of products mx b ij b j j=1 of elements of B 1 multiplied by elements of b: Each element of B 1 is given by B 1 = Adj A det A 8
9 Now j det Aj is integer valued, therefore the denominator 1. Adj A is the matrix of cofactors. Each cofactor is the determinant of a.(m 1) (m 1) matrix, i.e. the sum of (m 1)! products of m 1 elements of A: Therefore each element of B 1 is bounded in modulus by (m 1)! m 1 Because each x j is the sum of m elements of B 1 multiplied by an element of b j ; we have jx j j m! m 1 as required. Example 2.1 Consider the LP min 2x 2 + x 4 + 5x 7 subject to x 1 + x 2 + x + x 4 = 4 x 1 + x 5 = 2 x + x 6 = x 2 + x + x 7 = 6 x 1 ; x 2 ; x ; x 4 ; x 5 ; x 6 ; x 7 One basis is B = fa 4 ; A 5 ; A 6 ; A 7 g ; which corresponds to the matrix B = I: the corresponding basic solution is x = (; ; ; 4; 2; ; 6) : Another basis corresponds to B = fa 2 ; A 5 ; A 6 ; A 7 g with basic solution x = (; 4; ; ; 2; ; 6) : Note that x is not a feasible solution, since x 7 < : Remark: The basis feasible solutions (BFS) of an LP are precisely the vertices or extreme points (EP s) of the feasible region. We will show that the optimum (if it exists) is achieved at a vertex. Let B be a m m non-singular submatrix of A (m columns of A). Let x B denote the components of x corresponding to B and x N denote the remaining n m (zero) components. For convenience of notation we may reorder the columns of A so that the rst m columns relate to B and the remaining columns to a m (n m) submatrix N. Then Ax = B N x B x N = Bx B + Nx N = b 9
10 Since x N = for this basic solution x we obtain Bx B = b x B = B 1 b (2.2) De nition: A BFS (and the corresponding vertex) is called degenerate if it contains more than n m zeros. i.e. Some component of x B is zero,() the basic solution is degenerate. Lemma If two distinct bases correspond to the same BFS x then then x is degenerate. Proof Suppose that B and B both determine the same BFS x: Then x has zeros in all the n m columns not in B: Some such column must belong to B so x is degenerate. Example 2.2 Determine all the basic solutions to the system x 1 + x 2 6 x 2 x 1 ; x 2 Solution Introduce slack variables s 1 ; s 2 to write the system in standard form x 1 +x 2 +s 1 = 6 x 2 +s 2 = or in matrix form (with m = 2; n = 4) x 1 x 2 s 1 s 2 1 C A = 6 : A x = b (24) (41) (21) Set n m = 2 variables to zero to obtain a basic solution if the resulting B-matrix is invertible (so columns of B form a basis or minimal spanning set of R m ). 1
11 Set s 1 = s 2 = then B = x B = B 1 b = 1 and B 1 = = x = x T B ; xt N = (; ; ; ) T is a BFS Set x 2 = s 1 = : B = = I 1 2 = B 1 6 x B = B 1 b = b = : so x = (6; ; ; ) T is a BFS. Continue to examine a total of We obtain (Ex.) the four BFS s 1 6 x 1 = B A x 2 = 4 2 = 4!2! 2! = 6 selections of basic variables. 1 C A x = 1 C A x 4 = 6 1 C A Ex. The corners or vertices of the feasible region in (x 1 ; x 2 ) space are (; ) ; (; ) ; (6; ) ; (; ) : 11
12 Theorem 1 (Existence of a Basic Feasible Solution) Given a LP in standard form where A is (m n) of rank m i) If there is a feasible solution there is a BFS ii) If the LP has an optimal solution, there is an optimal BFS. Proof i) Let A be partitioned by columns as (A 1 ja 2 j:::ja n ) ie. A j denotes the j th column of A (an m vector) Suppose that x = (x 1 ; x 2 ; :::; x n ) T is a feasible solution. Then Ax = x 1 A 1 + x 2 A 2 + ::: + x n A n = b where x j ; each j: Let x have p strictly positive components and renumber the columns of A so these are the rst p components x 1 ; x 2 ; :::; x p. Then Ax = x 1 A 1 + x 2 A 2 + ::: + x p A p = b (1) Case 1 A 1 ; :::A p are linearly independent. Then p m. If p = m then A 1 ; :::A m form a basis. i.e. they span R m : If p < m we can add additional columns from A to complete a basis. Assigning a value zero to the corresponding variables x p+1 ; :::; x m results in a (degenerate) BFS. Case 2 A 1 ; :::A p are linearly dependent. By de nition, 9 a non-trivial linear combination of the A j s summing to zero i.e. y 1 A 1 + y 2 A 2 + ::: + y p A p = (2) where some y j > can be assumed. Eq. (1) - "Eq. (2) gives is true for any ": (x 1 "y 1 ) A 1 + (x 2 "y 2 )A 2 + ::: + (x p "y p )A p = b () Let y T = (y 1 ; y 2 ; :::; y p ; ; :::; ). The vector x "y satis es (2.1). Consider " " ; i.e. increasing from a value of zero and let xj " = min y j > y j 12
13 be the minimum ratio over positive components y j : For this value of "; at least one coe cient in () is zero and x most p 1 strictly positive coe cients. "y has at Repeating this process as necessary, we eventually obtain a set of linearly independent columns fa j g. We are thus back to Case 1 and conclude that we can construct a BFS given a feasible solution. ii) Let x T = (x 1 ; x 2 ; :::; x n ) be an optimal ()feasible) solution to LP with the strictly positive components x 1 ; :::; x p (after reordering). Consider the same two cases as before. Case 1 (A 1 ; :::A p are linearly independent) If p < m; the procedure described before results in an optimal BFS whose OF value P c j x j is unchanged through addition of components with value x j =. Case 2 (A 1 ; :::A p are linearly dependent) The value of the solution x "y is c T (x "y) = c T x "c T y (4) For " su ciently small, x "y is a feasible solution (all components ) of value c T x "c T y. However, because x is optimal, the value of (4) is not permitted to be less than c T x (for minimization): Therefore c T y =, and (4) does not change in value, though the number of strictly positive components of x is reduced. Example 2. (illustrating fundamental theorem) Consider the following LP in standard form: Maximize 8x 1 +6x 2 s. t. x 1 + x 2 +s 1 = 1 2x 1 + x 2 +s 2 = 15 5x 1 +1x 2 +s = 8 x j j = 1; 2 s i i = 1; 2; 1. Identify x and the constants A; b; c for this problem. 2. Construct a BFS from the given feasible solution x T = (x 1 ; x 2 ; s 1 ; s 2 ; s ) = (; 65; 5; 25; ) with value 6. 1
14 Let y T = (y 1 ; y 2 ; y ; y 4 ; ) and seek y such that Ay = or y 1 + y 2 +y = 2y 1 + y 2 +y 4 = 5y 1 +1y 2 = With equations and 4 unknowns, there are an in nite number of possible choices. e.g. let y T = ( 2; 1; 1; ; ) and note that c T y = 1 < : x " y = ( + 2"; 65 "; 5 "; 25 "; ) T The minimum ratio over positive y s is 65 min 1 ; 5 1 ; 25 = 5 Let x = x 5y = (4; 6; ; 1; ) T with value 6 5 ( 1) = 68 The columns of A corresponding to x 1 ; x 2 ; s 2 form the basis matrix B A 5 1 which is invertible (verify e.g. jbj 6= ). The term basis refers to the vectors A 1 ; A 2 ; A 4 which span R (in general R m ) the space of the columns of A: Note: Some books refer to B simply as the basis. ) x = (4; 6; ; 1; ) T is a BFS Ex. Draw the feasible region S and show that x is a corner of S. 14
15 2.2 Geometry of LP (Extreme points) Regarding the vector x as a point in n-dimensional space R n provides an alternative geometric view and further insight into the solution of LP problems. Convex sets Let p q 2 R n : The line segment P Q consists of all points p+ (1 where < < 1. ) q {Such points are termed convex linear combinations of p and q: More generally, a convex linear combination of p 1 ; p 2 ; :::; p k is P k i=1 ip i with i and P k i=1 i = 1.} De nition A set K R n is convex if, for x 1 ; x 2 2 K and for every < < 1; the point x 1 + (1 ) x 2 belongs to K. Result The feasible region (FR) of a LP in standard form is convex. F = fx j Ax = b; x g Proof Let x 1 ; x 2 2 F: Consider x = x 1 + (1 ) x 2 for < < 1 so x is a solution of Ax = b: Ax = A [x 1 + (1 ) x 2 ] = Ax 1 + (1 ) Ax 2 = b+ (1 ) b = b Also < < 1 and x 1 ; x 2 )x 1 + (1 ) x 2 ) x is a feasible solution of the system Ax = b ie. x 2 S: Some further de nitions useful in understanding the geometric nature of an LP are as follows: The region to one side of an inequality of the form x 2 R n ja T x b a (closed) halfspace is The region x 2 R n ja T x = b is a hyperplane [an (n 1) dimensional region, subspace if b = ] A polyhedral set or a polyhedron is the intersection of a nite number of halfspaces 15
16 A bounded polyhedron (one that doesn t extend to in nity in any direction) is termed a polytope. Result The FR of an LP containing a mixture of equality and inequality constraints is also a polyhedron. Proof Observe that Ax = b can be written as Ax b and Ax b The extreme points (EP s) or vertices of a polyhedron play a very important part in LP because, if an LP has a nite optimal solution, it is achieved at a vertex. De nition An extreme point of a convex set K is a point which cannot be expressed as a convex linear combination of two distinct points of K. i.e. x 2 K is an extreme point if and only y; z 2 K (y 6= z) such that x = y+ (1 ) z Theorem 2 (Equivalence of EP s and BFS s) We show that for LP in standard form and i) BFS) EP and ii) EP)BFS Proof i) Let x be a BFS to the LP in standard form. Suppose (for contradiction) that (w.l.o.g.) the rst p components fx j g p j=1 are strictly positive and x j = for j > p. Then Ax = b reduces to where fa j g are linearly independent. x 1 A 1 + x 2 A 2 + ::: + x p A p = b If x is not an extreme point, 9 two distinct points y,z 2 F such that x = y+ (1 ) z for and < < 1: For i > p; x i = = y i + (1 ) z i and so y i = z i = : (since y i ; z i because y, z 2 F and ; 1 > ) Therefore y; z have at most p non zero components, Therefore y 1 A 1 + y 2 A 2 + ::: + y p A p = b z 1 A 1 + z 2 A 2 + ::: + z p A p = b (y 1 z 1 )A 1 + (y 2 z 2 )A 2 + ::: + (y p z p )A p = with not all coe cients zero (because y 6= z). This contradicts our assumption that fa j g are linearly independent. 16
17 ii) Let x be an extreme point of F with precisely p non-zero components, so x 1 A 1 + x 2 A 2 + ::: + x p A p = b (w.l.o.g.) with x 1 ; x 2 ; :::; x p > and x i = (i > p) : Suppose (for contradiction) that x is not a BFS. i.e. the columns of A are linearly dependent y 1 A 1 + y 2 A 2 + ::: + y p A p = for some coe cients fy j g p j=1 not all zero. De ne the n vector y = (y 1 ; y 2 ; :::; y p ; ; :::; ) T so that Ay = : We can nd " su ciently small so that x 1 = x + "y and x 2 = x "y. [NB. x 1 6= x 2 because y 6= ]. Now x 1 and x 2 belong to F because Ax 1 = A (x + "y) = Ax + "Ay = Ax = b and similarly for x 2. Since x = 1 2 (x 1 + x 2 ) x can be written as a linear combination of distinct points of F; contradicting our assumption that x is an EP of S: Consequence We can re-phrase the fundamental theorem of LP in terms of extreme points; 1. If the feasible region F is non-empty, it has at least one EP 2. If the LP has a nite optimal solution (always true if F is bounded), it has an optimal solution which is an EP of F: Representation of convex polytopes Any point in a convex polytope (i.e. a bounded polyhedron) can be represented as a convex linear combination of its extreme points. This enables an alternative proof of the fundamental theorem. of Note S has a finite number of extreme points, since there are a maximum n m sets of basic variables. 17
18 Theorem (Fundamental Theorem restated) A linear objective function c T x achieves its minimum over a convex polytope (bounded polyhedron) at an extreme point of S: Proof Let x 1 ; x 2 ; :::; x k be the set of EP s of S: Any x 2 S has the representation x = 1 x x 2 + ::: + k x k for some set of coe cients f i g with i each i and P k i=1 i = 1 and c T x = 1 c T x c T x 2 + ::: + k c T x k = 1 z z 2 + ::: + k z k ; say Let z o = min fz i g k i=1 be the minimum OF value at any vertex. Then z i z for each i; giving c T x 1 z + 2 z + ::: + K z = ( ::: + k ) z = z If x is optimal, c T x z so c T x = z showing that the optimal value of the LP is achieved at a vertex with minimum value z : 18
4. Duality and Sensitivity
4. Duality and Sensitivity For every instance of an LP, there is an associated LP known as the dual problem. The original problem is known as the primal problem. There are two de nitions of the dual pair
More informationDeveloping an Algorithm for LP Preamble to Section 3 (Simplex Method)
Moving from BFS to BFS Developing an Algorithm for LP Preamble to Section (Simplex Method) We consider LP given in standard form and let x 0 be a BFS. Let B ; B ; :::; B m be the columns of A corresponding
More informationChapter 5 Linear Programming (LP)
Chapter 5 Linear Programming (LP) General constrained optimization problem: minimize f(x) subject to x R n is called the constraint set or feasible set. any point x is called a feasible point We consider
More informationNumerical Optimization
Linear Programming Computer Science and Automation Indian Institute of Science Bangalore 560 012, India. NPTEL Course on min x s.t. Transportation Problem ij c ijx ij 3 j=1 x ij a i, i = 1, 2 2 i=1 x ij
More informationLinear and Integer Programming - ideas
Linear and Integer Programming - ideas Paweł Zieliński Institute of Mathematics and Computer Science, Wrocław University of Technology, Poland http://www.im.pwr.wroc.pl/ pziel/ Toulouse, France 2012 Literature
More informationPart 1. The Review of Linear Programming Introduction
In the name of God Part 1. The Review of Linear Programming 1.1. Spring 2010 Instructor: Dr. Masoud Yaghini Outline The Linear Programming Problem Geometric Solution References The Linear Programming Problem
More informationLinear Programming and the Simplex method
Linear Programming and the Simplex method Harald Enzinger, Michael Rath Signal Processing and Speech Communication Laboratory Jan 9, 2012 Harald Enzinger, Michael Rath Jan 9, 2012 page 1/37 Outline Introduction
More information3. Linear Programming and Polyhedral Combinatorics
Massachusetts Institute of Technology 18.453: Combinatorial Optimization Michel X. Goemans April 5, 2017 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory
More information3. Linear Programming and Polyhedral Combinatorics
Massachusetts Institute of Technology 18.433: Combinatorial Optimization Michel X. Goemans February 28th, 2013 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory
More information1 The linear algebra of linear programs (March 15 and 22, 2015)
1 The linear algebra of linear programs (March 15 and 22, 2015) Many optimization problems can be formulated as linear programs. The main features of a linear program are the following: Variables are real
More informationVector Spaces. Addition : R n R n R n Scalar multiplication : R R n R n.
Vector Spaces Definition: The usual addition and scalar multiplication of n-tuples x = (x 1,..., x n ) R n (also called vectors) are the addition and scalar multiplication operations defined component-wise:
More informationOPERATIONS RESEARCH. Linear Programming Problem
OPERATIONS RESEARCH Chapter 1 Linear Programming Problem Prof. Bibhas C. Giri Department of Mathematics Jadavpur University Kolkata, India Email: bcgiri.jumath@gmail.com MODULE - 2: Simplex Method for
More information3. Vector spaces 3.1 Linear dependence and independence 3.2 Basis and dimension. 5. Extreme points and basic feasible solutions
A. LINEAR ALGEBRA. CONVEX SETS 1. Matrices and vectors 1.1 Matrix operations 1.2 The rank of a matrix 2. Systems of linear equations 2.1 Basic solutions 3. Vector spaces 3.1 Linear dependence and independence
More informationLecture 6 Simplex method for linear programming
Lecture 6 Simplex method for linear programming Weinan E 1,2 and Tiejun Li 2 1 Department of Mathematics, Princeton University, weinan@princeton.edu 2 School of Mathematical Sciences, Peking University,
More informationInteger programming: an introduction. Alessandro Astolfi
Integer programming: an introduction Alessandro Astolfi Outline Introduction Examples Methods for solving ILP Optimization on graphs LP problems with integer solutions Summary Introduction Integer programming
More informationLinear Algebra. James Je Heon Kim
Linear lgebra James Je Heon Kim (jjk9columbia.edu) If you are unfamiliar with linear or matrix algebra, you will nd that it is very di erent from basic algebra or calculus. For the duration of this session,
More information3.7 Cutting plane methods
3.7 Cutting plane methods Generic ILP problem min{ c t x : x X = {x Z n + : Ax b} } with m n matrix A and n 1 vector b of rationals. According to Meyer s theorem: There exists an ideal formulation: conv(x
More informationMAT 2009: Operations Research and Optimization 2010/2011. John F. Rayman
MAT 29: Operations Research and Optimization 21/211 John F. Rayman Department of Mathematics University of Surrey Introduction The assessment for the this module is based on a class test counting for 1%
More informationChapter 1. Preliminaries
Introduction This dissertation is a reading of chapter 4 in part I of the book : Integer and Combinatorial Optimization by George L. Nemhauser & Laurence A. Wolsey. The chapter elaborates links between
More informationCSCI 1951-G Optimization Methods in Finance Part 01: Linear Programming
CSCI 1951-G Optimization Methods in Finance Part 01: Linear Programming January 26, 2018 1 / 38 Liability/asset cash-flow matching problem Recall the formulation of the problem: max w c 1 + p 1 e 1 = 150
More information1 Review Session. 1.1 Lecture 2
1 Review Session Note: The following lists give an overview of the material that was covered in the lectures and sections. Your TF will go through these lists. If anything is unclear or you have questions
More informationAn introductory example
CS1 Lecture 9 An introductory example Suppose that a company that produces three products wishes to decide the level of production of each so as to maximize profits. Let x 1 be the amount of Product 1
More informationTHE UNIVERSITY OF HONG KONG DEPARTMENT OF MATHEMATICS. Operations Research I
LN/MATH2901/CKC/MS/2008-09 THE UNIVERSITY OF HONG KONG DEPARTMENT OF MATHEMATICS Operations Research I Definition (Linear Programming) A linear programming (LP) problem is characterized by linear functions
More informationChapter 2: Linear Programming Basics. (Bertsimas & Tsitsiklis, Chapter 1)
Chapter 2: Linear Programming Basics (Bertsimas & Tsitsiklis, Chapter 1) 33 Example of a Linear Program Remarks. minimize 2x 1 x 2 + 4x 3 subject to x 1 + x 2 + x 4 2 3x 2 x 3 = 5 x 3 + x 4 3 x 1 0 x 3
More informationLectures 6, 7 and part of 8
Lectures 6, 7 and part of 8 Uriel Feige April 26, May 3, May 10, 2015 1 Linear programming duality 1.1 The diet problem revisited Recall the diet problem from Lecture 1. There are n foods, m nutrients,
More informationContents. 4.5 The(Primal)SimplexMethod NumericalExamplesoftheSimplexMethod
Contents 4 The Simplex Method for Solving LPs 149 4.1 Transformations to be Carried Out On an LP Model Before Applying the Simplex Method On It... 151 4.2 Definitions of Various Types of Basic Vectors
More informationz = f (x; y) f (x ; y ) f (x; y) f (x; y )
BEEM0 Optimization Techiniques for Economists Lecture Week 4 Dieter Balkenborg Departments of Economics University of Exeter Since the fabric of the universe is most perfect, and is the work of a most
More informationLinear Programming. Linear Programming I. Lecture 1. Linear Programming. Linear Programming
Linear Programming Linear Programming Lecture Linear programming. Optimize a linear function subject to linear inequalities. (P) max " c j x j n j= n s. t. " a ij x j = b i # i # m j= x j 0 # j # n (P)
More informationLinear programs, convex polyhedra, extreme points
MVE165/MMG631 Extreme points of convex polyhedra; reformulations; basic feasible solutions; the simplex method Ann-Brith Strömberg 2015 03 27 Linear programs, convex polyhedra, extreme points A linear
More information3 Development of the Simplex Method Constructing Basic Solution Optimality Conditions The Simplex Method...
Contents Introduction to Linear Programming Problem. 2. General Linear Programming problems.............. 2.2 Formulation of LP problems.................... 8.3 Compact form and Standard form of a general
More informationYinyu Ye, MS&E, Stanford MS&E310 Lecture Note #06. The Simplex Method
The Simplex Method Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/ yyye (LY, Chapters 2.3-2.5, 3.1-3.4) 1 Geometry of Linear
More informationChapter 1: Linear Programming
Chapter 1: Linear Programming Math 368 c Copyright 2013 R Clark Robinson May 22, 2013 Chapter 1: Linear Programming 1 Max and Min For f : D R n R, f (D) = {f (x) : x D } is set of attainable values of
More informationPart 1. The Review of Linear Programming
In the name of God Part 1. The Review of Linear Programming 1.2. Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Basic Feasible Solutions Key to the Algebra of the The Simplex Algorithm
More informationa11 a A = : a 21 a 22
Matrices The study of linear systems is facilitated by introducing matrices. Matrix theory provides a convenient language and notation to express many of the ideas concisely, and complicated formulas are
More informationECON0702: Mathematical Methods in Economics
ECON0702: Mathematical Methods in Economics Yulei Luo SEF of HKU January 12, 2009 Luo, Y. (SEF of HKU) MME January 12, 2009 1 / 35 Course Outline Economics: The study of the choices people (consumers,
More informationx 1 + x 2 2 x 1 x 2 1 x 2 2 min 3x 1 + 2x 2
Lecture 1 LPs: Algebraic View 1.1 Introduction to Linear Programming Linear programs began to get a lot of attention in 1940 s, when people were interested in minimizing costs of various systems while
More informationWeek 2. The Simplex method was developed by Dantzig in the late 40-ties.
1 The Simplex method Week 2 The Simplex method was developed by Dantzig in the late 40-ties. 1.1 The standard form The simplex method is a general description algorithm that solves any LPproblem instance.
More informationAM 121: Intro to Optimization Models and Methods
AM 121: Intro to Optimization Models and Methods Fall 2017 Lecture 2: Intro to LP, Linear algebra review. Yiling Chen SEAS Lecture 2: Lesson Plan What is an LP? Graphical and algebraic correspondence Problems
More informationDr. S. Bourazza Math-473 Jazan University Department of Mathematics
Dr. Said Bourazza Department of Mathematics Jazan University 1 P a g e Contents: Chapter 0: Modelization 3 Chapter1: Graphical Methods 7 Chapter2: Simplex method 13 Chapter3: Duality 36 Chapter4: Transportation
More informationΩ R n is called the constraint set or feasible set. x 1
1 Chapter 5 Linear Programming (LP) General constrained optimization problem: minimize subject to f(x) x Ω Ω R n is called the constraint set or feasible set. any point x Ω is called a feasible point We
More informationFundamental Theorems of Optimization
Fundamental Theorems of Optimization 1 Fundamental Theorems of Math Prog. Maximizing a concave function over a convex set. Maximizing a convex function over a closed bounded convex set. 2 Maximizing Concave
More informationLinear Programming Duality P&S Chapter 3 Last Revised Nov 1, 2004
Linear Programming Duality P&S Chapter 3 Last Revised Nov 1, 2004 1 In this section we lean about duality, which is another way to approach linear programming. In particular, we will see: How to define
More informationOptimization methods NOPT048
Optimization methods NOPT048 Jirka Fink https://ktiml.mff.cuni.cz/ fink/ Department of Theoretical Computer Science and Mathematical Logic Faculty of Mathematics and Physics Charles University in Prague
More informationChoose three of: Choose three of: Choose three of:
MATH Final Exam (Version ) Solutions July 8, 8 S. F. Ellermeyer Name Instructions. Remember to include all important details of your work. You will not get full credit (or perhaps even any partial credit)
More informationCO 250 Final Exam Guide
Spring 2017 CO 250 Final Exam Guide TABLE OF CONTENTS richardwu.ca CO 250 Final Exam Guide Introduction to Optimization Kanstantsin Pashkovich Spring 2017 University of Waterloo Last Revision: March 4,
More informationLinear Programming. (Com S 477/577 Notes) Yan-Bin Jia. Nov 28, 2017
Linear Programming (Com S 4/ Notes) Yan-Bin Jia Nov 8, Introduction Many problems can be formulated as maximizing or minimizing an objective in the form of a linear function given a set of linear constraints
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS SYSTEMS OF EQUATIONS AND MATRICES Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationNotes taken by Graham Taylor. January 22, 2005
CSC4 - Linear Programming and Combinatorial Optimization Lecture : Different forms of LP. The algebraic objects behind LP. Basic Feasible Solutions Notes taken by Graham Taylor January, 5 Summary: We first
More informationF 1 F 2 Daily Requirement Cost N N N
Chapter 5 DUALITY 5. The Dual Problems Every linear programming problem has associated with it another linear programming problem and that the two problems have such a close relationship that whenever
More information3.3 Easy ILP problems and totally unimodular matrices
3.3 Easy ILP problems and totally unimodular matrices Consider a generic ILP problem expressed in standard form where A Z m n with n m, and b Z m. min{c t x : Ax = b, x Z n +} (1) P(b) = {x R n : Ax =
More informationThe Kuhn-Tucker Problem
Natalia Lazzati Mathematics for Economics (Part I) Note 8: Nonlinear Programming - The Kuhn-Tucker Problem Note 8 is based on de la Fuente (2000, Ch. 7) and Simon and Blume (1994, Ch. 18 and 19). The Kuhn-Tucker
More informationLinear Programming Redux
Linear Programming Redux Jim Bremer May 12, 2008 The purpose of these notes is to review the basics of linear programming and the simplex method in a clear, concise, and comprehensive way. The book contains
More informationLinear Algebra. Linear Algebra. Chih-Wei Yi. Dept. of Computer Science National Chiao Tung University. November 12, 2008
Linear Algebra Chih-Wei Yi Dept. of Computer Science National Chiao Tung University November, 008 Section De nition and Examples Section De nition and Examples Section De nition and Examples De nition
More information4.3 - Linear Combinations and Independence of Vectors
- Linear Combinations and Independence of Vectors De nitions, Theorems, and Examples De nition 1 A vector v in a vector space V is called a linear combination of the vectors u 1, u,,u k in V if v can be
More informationIntroduction to Linear Algebra. Tyrone L. Vincent
Introduction to Linear Algebra Tyrone L. Vincent Engineering Division, Colorado School of Mines, Golden, CO E-mail address: tvincent@mines.edu URL: http://egweb.mines.edu/~tvincent Contents Chapter. Revew
More informationAdvanced Microeconomics Fall Lecture Note 1 Choice-Based Approach: Price e ects, Wealth e ects and the WARP
Prof. Olivier Bochet Room A.34 Phone 3 63 476 E-mail olivier.bochet@vwi.unibe.ch Webpage http//sta.vwi.unibe.ch/bochet Advanced Microeconomics Fall 2 Lecture Note Choice-Based Approach Price e ects, Wealth
More informationIntroduction to Linear and Combinatorial Optimization (ADM I)
Introduction to Linear and Combinatorial Optimization (ADM I) Rolf Möhring based on the 20011/12 course by Martin Skutella TU Berlin WS 2013/14 1 General Remarks new flavor of ADM I introduce linear and
More informationThe Simplex Algorithm
8.433 Combinatorial Optimization The Simplex Algorithm October 6, 8 Lecturer: Santosh Vempala We proved the following: Lemma (Farkas). Let A R m n, b R m. Exactly one of the following conditions is true:.
More informationLINEAR PROGRAMMING I. a refreshing example standard form fundamental questions geometry linear algebra simplex algorithm
Linear programming Linear programming. Optimize a linear function subject to linear inequalities. (P) max c j x j n j= n s. t. a ij x j = b i i m j= x j 0 j n (P) max c T x s. t. Ax = b Lecture slides
More informationDuality of LPs and Applications
Lecture 6 Duality of LPs and Applications Last lecture we introduced duality of linear programs. We saw how to form duals, and proved both the weak and strong duality theorems. In this lecture we will
More informationIE 400 Principles of Engineering Management. The Simplex Algorithm-I: Set 3
IE 4 Principles of Engineering Management The Simple Algorithm-I: Set 3 So far, we have studied how to solve two-variable LP problems graphically. However, most real life problems have more than two variables!
More informationIntroduction to Integer Programming
Lecture 3/3/2006 p. /27 Introduction to Integer Programming Leo Liberti LIX, École Polytechnique liberti@lix.polytechnique.fr Lecture 3/3/2006 p. 2/27 Contents IP formulations and examples Total unimodularity
More information1. Algebraic and geometric treatments Consider an LP problem in the standard form. x 0. Solutions to the system of linear equations
The Simplex Method Most textbooks in mathematical optimization, especially linear programming, deal with the simplex method. In this note we study the simplex method. It requires basically elementary linear
More informationUNDERGROUND LECTURE NOTES 1: Optimality Conditions for Constrained Optimization Problems
UNDERGROUND LECTURE NOTES 1: Optimality Conditions for Constrained Optimization Problems Robert M. Freund February 2016 c 2016 Massachusetts Institute of Technology. All rights reserved. 1 1 Introduction
More informationNew Artificial-Free Phase 1 Simplex Method
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:09 No:10 69 New Artificial-Free Phase 1 Simplex Method Nasiruddin Khan, Syed Inayatullah*, Muhammad Imtiaz and Fozia Hanif Khan Department
More informationLP Duality: outline. Duality theory for Linear Programming. alternatives. optimization I Idea: polyhedra
LP Duality: outline I Motivation and definition of a dual LP I Weak duality I Separating hyperplane theorem and theorems of the alternatives I Strong duality and complementary slackness I Using duality
More informationLecture slides by Kevin Wayne
LINEAR PROGRAMMING I a refreshing example standard form fundamental questions geometry linear algebra simplex algorithm Lecture slides by Kevin Wayne Last updated on 7/25/17 11:09 AM Linear programming
More information3 The Simplex Method. 3.1 Basic Solutions
3 The Simplex Method 3.1 Basic Solutions In the LP of Example 2.3, the optimal solution happened to lie at an extreme point of the feasible set. This was not a coincidence. Consider an LP in general form,
More informationOPTIMISATION 3: NOTES ON THE SIMPLEX ALGORITHM
OPTIMISATION 3: NOTES ON THE SIMPLEX ALGORITHM Abstract These notes give a summary of the essential ideas and results It is not a complete account; see Winston Chapters 4, 5 and 6 The conventions and notation
More informationDistributed Real-Time Control Systems. Lecture Distributed Control Linear Programming
Distributed Real-Time Control Systems Lecture 13-14 Distributed Control Linear Programming 1 Linear Programs Optimize a linear function subject to a set of linear (affine) constraints. Many problems can
More informationOptimization WS 13/14:, by Y. Goldstein/K. Reinert, 9. Dezember 2013, 16: Linear programming. Optimization Problems
Optimization WS 13/14:, by Y. Goldstein/K. Reinert, 9. Dezember 2013, 16:38 2001 Linear programming Optimization Problems General optimization problem max{z(x) f j (x) 0,x D} or min{z(x) f j (x) 0,x D}
More information15-780: LinearProgramming
15-780: LinearProgramming J. Zico Kolter February 1-3, 2016 1 Outline Introduction Some linear algebra review Linear programming Simplex algorithm Duality and dual simplex 2 Outline Introduction Some linear
More informationLecture 1 Introduction
L. Vandenberghe EE236A (Fall 2013-14) Lecture 1 Introduction course overview linear optimization examples history approximate syllabus basic definitions linear optimization in vector and matrix notation
More informationSubmodular Functions, Optimization, and Applications to Machine Learning
Submodular Functions, Optimization, and Applications to Machine Learning Spring Quarter, Lecture 12 http://www.ee.washington.edu/people/faculty/bilmes/classes/ee596b_spring_2016/ Prof. Jeff Bilmes University
More informationInteger Programming, Part 1
Integer Programming, Part 1 Rudi Pendavingh Technische Universiteit Eindhoven May 18, 2016 Rudi Pendavingh (TU/e) Integer Programming, Part 1 May 18, 2016 1 / 37 Linear Inequalities and Polyhedra Farkas
More informationMath 5593 Linear Programming Week 1
University of Colorado Denver, Fall 2013, Prof. Engau 1 Problem-Solving in Operations Research 2 Brief History of Linear Programming 3 Review of Basic Linear Algebra Linear Programming - The Story About
More informationAppendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS
Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS Here we consider systems of linear constraints, consisting of equations or inequalities or both. A feasible solution
More information3. THE SIMPLEX ALGORITHM
Optimization. THE SIMPLEX ALGORITHM DPK Easter Term. Introduction We know that, if a linear programming problem has a finite optimal solution, it has an optimal solution at a basic feasible solution (b.f.s.).
More informationMAT-INF4110/MAT-INF9110 Mathematical optimization
MAT-INF4110/MAT-INF9110 Mathematical optimization Geir Dahl August 20, 2013 Convexity Part IV Chapter 4 Representation of convex sets different representations of convex sets, boundary polyhedra and polytopes:
More informationRobust Solutions to Multi-Objective Linear Programs with Uncertain Data
Robust Solutions to Multi-Objective Linear Programs with Uncertain Data M.A. Goberna yz V. Jeyakumar x G. Li x J. Vicente-Pérez x Revised Version: October 1, 2014 Abstract In this paper we examine multi-objective
More informationElementary maths for GMT
Elementary maths for GMT Linear Algebra Part 2: Matrices, Elimination and Determinant m n matrices The system of m linear equations in n variables x 1, x 2,, x n a 11 x 1 + a 12 x 2 + + a 1n x n = b 1
More informationIntroduction to Integer Linear Programming
Lecture 7/12/2006 p. 1/30 Introduction to Integer Linear Programming Leo Liberti, Ruslan Sadykov LIX, École Polytechnique liberti@lix.polytechnique.fr sadykov@lix.polytechnique.fr Lecture 7/12/2006 p.
More informationLinear Programming Inverse Projection Theory Chapter 3
1 Linear Programming Inverse Projection Theory Chapter 3 University of Chicago Booth School of Business Kipp Martin September 26, 2017 2 Where We Are Headed We want to solve problems with special structure!
More informationThe Simplex Algorithm and Goal Programming
The Simplex Algorithm and Goal Programming In Chapter 3, we saw how to solve two-variable linear programming problems graphically. Unfortunately, most real-life LPs have many variables, so a method is
More informationAM 121: Intro to Optimization! Models and Methods! Fall 2018!
AM 121: Intro to Optimization Models and Methods Fall 2018 Lecture 15: Cutting plane methods Yiling Chen SEAS Lesson Plan Cut generation and the separation problem Cutting plane methods Chvatal-Gomory
More informationAM 121: Intro to Optimization
AM 121: Intro to Optimization Models and Methods Lecture 6: Phase I, degeneracy, smallest subscript rule. Yiling Chen SEAS Lesson Plan Phase 1 (initialization) Degeneracy and cycling Smallest subscript
More informationNonlinear Programming (NLP)
Natalia Lazzati Mathematics for Economics (Part I) Note 6: Nonlinear Programming - Unconstrained Optimization Note 6 is based on de la Fuente (2000, Ch. 7), Madden (1986, Ch. 3 and 5) and Simon and Blume
More informationA Parametric Simplex Algorithm for Linear Vector Optimization Problems
A Parametric Simplex Algorithm for Linear Vector Optimization Problems Birgit Rudloff Firdevs Ulus Robert Vanderbei July 9, 2015 Abstract In this paper, a parametric simplex algorithm for solving linear
More information16 Chapter 3. Separation Properties, Principal Pivot Transforms, Classes... for all j 2 J is said to be a subcomplementary vector of variables for (3.
Chapter 3 SEPARATION PROPERTIES, PRINCIPAL PIVOT TRANSFORMS, CLASSES OF MATRICES In this chapter we present the basic mathematical results on the LCP. Many of these results are used in later chapters to
More informationTRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN. School of Mathematics
JS and SS Mathematics JS and SS TSM Mathematics TRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN School of Mathematics MA3484 Methods of Mathematical Economics Trinity Term 2015 Saturday GOLDHALL 09.30
More informationSpring 2017 CO 250 Course Notes TABLE OF CONTENTS. richardwu.ca. CO 250 Course Notes. Introduction to Optimization
Spring 2017 CO 250 Course Notes TABLE OF CONTENTS richardwu.ca CO 250 Course Notes Introduction to Optimization Kanstantsin Pashkovich Spring 2017 University of Waterloo Last Revision: March 4, 2018 Table
More informationLinear Programming Notes
Linear Programming Notes Carl W. Lee Department of Mathematics University of Kentucky Lexington, KY 40506 lee@ms.uky.edu Fall 2007 i Contents 1 References 1 2 Exercises: Matrix Algebra 2 3 Polytopes 5
More information58 Appendix 1 fundamental inconsistent equation (1) can be obtained as a linear combination of the two equations in (2). This clearly implies that the
Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS Here we consider systems of linear constraints, consisting of equations or inequalities or both. A feasible solution
More informationANALYTICAL MATHEMATICS FOR APPLICATIONS 2018 LECTURE NOTES 3
ANALYTICAL MATHEMATICS FOR APPLICATIONS 2018 LECTURE NOTES 3 ISSUED 24 FEBRUARY 2018 1 Gaussian elimination Let A be an (m n)-matrix Consider the following row operations on A (1) Swap the positions any
More information{ move v ars to left, consts to right { replace = by t wo and constraints Ax b often nicer for theory Ax = b good for implementations. { A invertible
Finish remarks on min-cost ow. Strongly polynomial algorithms exist. { Tardos 1985 { minimum mean-cost cycle { reducing -optimality { \xing" arcs of very high reduced cost { best running running time roughly
More informationLinear Programming. 1 An Introduction to Linear Programming
18.415/6.854 Advanced Algorithms October 1994 Lecturer: Michel X. Goemans Linear Programming 1 An Introduction to Linear Programming Linear programming is a very important class of problems, both algorithmically
More informationMATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian.
MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian. Spanning set Let S be a subset of a vector space V. Definition. The span of the set S is the smallest subspace W V that contains S. If
More informationAssignment 1: From the Definition of Convexity to Helley Theorem
Assignment 1: From the Definition of Convexity to Helley Theorem Exercise 1 Mark in the following list the sets which are convex: 1. {x R 2 : x 1 + i 2 x 2 1, i = 1,..., 10} 2. {x R 2 : x 2 1 + 2ix 1x
More informationMathematical Preliminaries
Chapter 33 Mathematical Preliminaries In this appendix, we provide essential definitions and key results which are used at various points in the book. We also provide a list of sources where more details
More informationIt is convenient to introduce some notation for this type of problems. I will write this as. max u (x 1 ; x 2 ) subj. to. p 1 x 1 + p 2 x 2 m ;
4 Calculus Review 4.1 The Utility Maimization Problem As a motivating eample, consider the problem facing a consumer that needs to allocate a given budget over two commodities sold at (linear) prices p
More information