3. Vector spaces 3.1 Linear dependence and independence 3.2 Basis and dimension. 5. Extreme points and basic feasible solutions

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1 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 3.2 Basis and dimension 4. Convex sets 5. Extreme points and basic feasible solutions OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.1

2 1. Matrices and vectors The elements of R are called scalars. An m n matrix A is a rectangular array of scalars: A = a 11 a 12 a 1n a 21 a 22 a 2n a m1 a m2 a mn If a matrix has just one column, m 1, it is said to be a column vector. a = a 11 a 21. a m1 OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.2

3 1.1 Matrix operations Addition of matrices Given matrices A = (a ij ) R m n, B = (b ij ) R m n, the addition of A and B, denoted by A + B, is the matrix C = (c ij ) R m n obtained by performing the addition componentwise: Properties c ij = a ij + b ij, i = 1,..., m, j = 1,..., n. 1. The addition of matrices is an inner operation. A, B R m n A + B R m n. 2. The addition of matrices is commutative. A, B R m n A + B = B + A. 3. The addition of matrices is associative. A, B, C R m n (A + B) + C = A + (B + C). 4. There exists a neutral element for the addition. A R m n A+0 = 0+A = A, where 0 R m n. 5. There exist opposite elements for the addition. A R m n A + ( A) = ( A) + A = 0, where A R m n. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.3

4 Scalar multiplication Let α R be a scalar and A = (a ij ) R m n a matrix. The operation of multiplying A by α, is represented by α A and is performed by multiplying every element of A by α. The result is a matrix where Inner product B = (b ij ) R m n b ij = α a ij, i = 1,..., m, j = 1,..., n. The multiplication of a row vector a T and a column vector b is called the inner product of the two vectors, and is denoted by a T b. The result of this multiplication is a real number, and it is obtained in the following way: b 1 a T = (a 1 a n ) R 1 n, b =. R n. a T b = (a 1 a n ) b 1. b n = b n n a i b i. i=1 OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.4

5 Matrix multiplication Let A R m n n p matrix. be an m n matrix and B R n p an The product A B is defined as the m p matrix C = A B R m p, where the entry (i, j) in C is the inner product of the ith row of A and the jth column of B. Properties 1. It is associative. A R m n, B R n p, C R p q (A B) C = A (B C). 2. It is distributive with respect to the sum. A, B R m n, C R n p, A R m n, B, C R n p, (A+B) C = A C+B C. A (B+C) = A B+A C. 3. A R m n, A 0 n p = 0 m p, 0 q m A = 0 q n. 4. A R m n, I m A = A I n = A. 5. α R, A R m n, B R n p, α (A B) = (α A) B = A (α B). OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.5

6 1.2 The rank of a matrix Definition 1 Let A R m n be a matrix, and let U be the resulting matrix after performing Gaussian elimination. The rank of matrix A is denoted by rank A and is equal to the number of pivot elements of matrix U. 2. Systems of linear equations Let us consider a system with m linear equations of n variables Ax = b, where A R m n, rank A = r and b R m. We shall solve the system by performing Gaussian elimination. The following cases may arise: * rank A = rank (A b). The system has no solution. It is inconsistent. * rank A = rank (A b) = r. There exists at least one solution to the system. It is consistent. * r = number of variables. There exists a unique (one and only one) solution. * r < number of variables. There exists an infinite number of solutions. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.6

7 2.1. Basic solutions Let Ax = b be a system, where A R m n, m < n, and rank A = rank (A b) = m. Assume that the first m columns of A are linearly independent. Let B be the m m submatrix of A formed by its first m columns. Let N denote the last n m columns of A. We can write the system Ax = b in the following way: or (B N) x B x N = b, Bx B + Nx N = b. Variables in x B : basic variables. Variables in x N : nonbasic variables. Bx B = b Nx N. Setting all the nonbasic variables equal to zero, x N = 0, we obtain a system whose solution is unique: Bx B = b. The solution thus calculated is called a basic solution of the system. The maximum number of basic solutions: n n! = m m! (n m)! OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.7

8 3. Vector spaces Definition 2 (Linear combination) A linear combination of vectors v 1, v 2,..., v n R m is: α 1 v 1 + α 2 v α n v n, where α 1,..., α n R are real numbers. 3.1 Linear dependence and independence Definition 3 The vectors v 1,..., v n R m are said to be linearly independent if for every linear combination such that α 1 v α n v n = 0 it implies that α 1 = = α n = 0. Definition 4 The vectors v 1,..., v n R m are said to be linearly dependent if there exist α 1,, α n R not all of them zero, such that α 1 v α n v n = 0. Obviously, they are not linearly independent. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.8

9 3.2 Basis and dimension Definition 5 A set of vectors S = {v 1,..., v p } R m is said to be a spanning set of R m if every vector v R m can be represented as a linear combination of the vectors in S, that is, if there exist α 1,..., α p R such that v = α 1 v α p v p. Definition 6 A collection B = {v 1,..., v m } R m of vectors forms a basis in R m if the following conditions hold: The vectors of B are linearly independent. B is a spanning set in R m. There are infinite bases in a vector space. However, all of them contain the same number of vectors. This number is the dimension of the vector space. Theorem 1 Let B = {v 1,..., v m } be a basis in R m. Then, every vector v R m can be expressed as a linear combination of v 1,..., v m, and the coefficients of that linear combination are unique. Theorem 2 Given a basis B for R m and a vector v R m, v B and v = 0, it is always possible to form another basis, by replacing a vector in B by the vector v. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.9

10 4. Convex sets An equation of the form a 1 x 1 + a 2 x 2 = c, where a 1, a 2 and c are constants, represents a straight line in R 2. An inequality of the form a 1 x 1 + a 2 x 2 c is the set of all points lying on the line a 1 x 1 + a 2 x 2 = c, together with all those points lying to one side of the line. x 2 2x 1 + 3x 2 6 x 1 A half-space of R 2 is the set of all points of R 2 which satisfies an inequality of the form a 1 x 1 + a 2 x 2 c or a 1 x 1 + a 2 x 2 c, where at least one of the constants a 1 or a 2 is nonzero. In R n the equation a 1 x 1 +a 2 x 2 + +a n x n = c, where a 1,..., a n, c R are constants, represents a hyperplane. A half-space of R n is the set of all points which satisfies an inequality of the form a 1 x 1 + a 2 x a n x n c or a 1 x 1 + a 2 x a n x n c. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.10

11 Definition 7 A subset C of R n is a convex set if C is empty, if C contains a single point, or if for every two distinct points in C, the line segment connecting them lies entirely in C. The sets (a), (b) and (c) in the Figure are convex, (d) is not convex. (a) (b) (c) (d) The following results can be proved: (1) A hyperplane is a convex set. (2) A half-space is a convex set. (3) The intersection of a finite number of convex sets is a convex set. In linear programming, convex sets such as hyperplanes, half-spaces and the intersection of a finite number of convex sets are of special significance, because they appear in the study of linear models. The intersection of a finite number of half-spaces is a convex set of the form (a), where the vertices of the set are called extreme points. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.11

12 5. Extreme points and basic feasible solutions Consider the following inequations, x 1 0 and x 2 0: x 1 + 4x 2 4 x 1 x 2 3 The intersection of the two half-spaces, together with x 1 0 and x 2 0, is a convex set; a polygon in this case. The polygon has a finite number of vertices: extreme points. x 2 x 1 + 4x 2 = 4 B A O x 1 x 2 = 3 C x 1 O = (0, 0), A = (0, 1), B = ( 16 3, 7 ), C = (3, 0). 3 OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.12

13 Convert inequations into equations by adding nonnegative variables x 3 and x 4. x 1 + 4x 2 + x 3 = 4 x 1 x 2 + x 4 = 3 Compute the basic solutions and choose the ones with all the variables greater than or equal to zero; we can confirm that they correspond to the extreme points of the polygon. x 3 = x 4 = 0, solve x 1 + 4x 2 = 4, x 1 x 2 = 3: x 1 = 16 3 and x 2 = 7. Extreme point B. 3 x 2 = x 4 = 0, solve x 1 + x 3 = 4, x 1 = 3: x 1 = 3 and x 3 = 7. Extreme point C. x 2 = x 3 = 0, solve x 1 = 4, x 1 + x 4 = 3: x 1 = 4 and x 4 = 7. It does not correspond to any extreme point. x 1 = x 4 = 0, solve 4x 2 + x 3 = 4, x 2 = 3: x 2 = 3 and x 3 = 16. It does not correspond to any extreme point. x 1 = x 3 = 0, solve 4x 2 = 4, x 2 + x 4 = 3: x 2 = 1 and x 4 = 4. Extreme point A. x 1 = x 2 = 0, solve x 3 = 4, x 4 = 3: x 3 = 4 and x 4 = 3. Extreme point O. OpenCourseWare, UPV/EHU, Operations Research. Linear Programming. A.13

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