1. Euclidean m-Space & Linear Equations Systems of Linear Equations

2. A finite set of linear equations. A solution of a linear system in R n is an ordered n-tuple that satisfies each equation in the system.

3. Example x 1 +x 2 = -2 3x 1 +3x 2 = -7 x 1 +x 2 = 6 2x 1 +12 = -2x 2

4. Consistent & Inconsistent Systems Consistent systems have at least one solution. Inconsistent systems have no solutions.

5. Standard Form for Linear Systems Like variables are vertically aligned on the left hand side. Constants on the right hand side.

6. Elementary Operations 1. Multiply an equation in the system by a nonzero scalar. 2. Interchange the positions of two equations in the system. 3. Replace an equation in the system with the sum of itself and the multiple of another equation in the system.

7. Equivalent Systems Two systems are equivalent if and only if they have the same set of solutions.

8. Theorem 2.2.1 If one of the elementary operations is performed on a given system of linear equations, an equivalent linear system is obtained.

9. Solving Linear Systems x 1 + x 2 + x 3 = -4 x 1 + 2x 2 = 1 2x 2 + 3x 3 = -2 x 1 -3x 2 + x 3 = 0 x 1 + x 2 - 3x 3 = 0 x 1 - x 2 - x 3 = 0

10. Example Find 3v-2u for the vectors u = (2, 1, 0.-3) and v = (-1, 3, -7, 4)

11. Theorem 2.1.2 R m Let u, v and w be vectors in R m, and c and d scalars. Then 1. u + v = v + u 2. (u + v) + w = v + (u + w) 3. u + 0 = u 4. u + (-u) = 0 5. (cd)u = c(du)

12. Theorem 2.1.2 Cont’d. R m Let u, v and w be vectors in R m, and c and d scalars. Then 6. (c + d)u = cu + du 7. c(u + v) = cu + cv 8. 1u = u 9. (-1)u = -u 10. 0u = 0

13. R m Dot Product in R m Let u and v be two vectors in R m. Then the dot product (or scalar product or inner product), denoted by u. v, is defined as u. v = u 1 v 1 + u 2 v 2 + … + u m v m

14. Theorem 2.1.3 Let u, v and w be vectors in R m, and let c be a scalar. Then a. u. v = v. u b. c(u. v) = (cu). v = u. (cv) c. u. (v + w) = u. v + u. w d. u. 0 = 0 e. u. u = ||u|| 2

15. Orthogonal Vectors R m Two vectors u and v in R m are orthogonal if u. v = 0. Orthogonal, Normal, and Perpendicular, all mean the same.

16. R m Defining Points in R m If u is a vector in R m,the corresponding point is denoted using the same m-tuple that is used to denote the vector. Notice that this is a generalization of a point in R 2 and R 3.

17. R m Defining Lines in R m Let P and Q be two distinct points in R m,and let x(t) = (1-t)p + tq. Then, a.The set of all points x(t) for real values of is the line through P and Q. b.The set of all points x(t) for t between 0 and 1 (inclusive) is the line segment from P to Q. Notice that x(0) = p and x(1) = q.

18. R m Point-Parallel Form for Lines in R m The set of vectors x(t) = p + tv is the line that contains the point P and is parallel to v, where t is a real number and v not equal to 0.

19. Example Given points P(2,1,0,3,1) and Q(1,-1,3,0,5). 1.Find the two-point form of the line through P and Q. 2.Find the point-parallel form of the line through P and Q. 3.Find the parametric equation of the line through P and Q.

20. Point-Normal Form of Hyperplane Let P be a point, and n a nonzero vector in R m. The point-normal form for P and n is the equation n. (x-p) = 0.

21. Standard Form of Hyperplane Let P be a point, and n a nonzero vector in R m. The point-normal form for P and n is the equation n. (x-p) = 0. Let n = (a 1, a 2, …., a m ), p = (p 1, p 2, …., p m ), x = (x 1, x 2, …., x m ) and b = n. p. Then we get the standard form of the equation of the hyperplane a 1 x 1 + a 2 x 2 + … + a m x m = b

22. Linear Equations Equations of the form a 1 x 1 + a 2 x 2 + … + a m x m = b are called linear equations. Terms of a linear equation contains a product of a variable and a constant or just a constant.

23. Example Give the point-normal form of the hyperplane through (-2, 1, 4, 0) with normal (1,2,-1,3). Give the standard form of the above hyperplane.

24. Planes Determined by Two Vectors Let u and v be two non-collinear vectors. Let X be any point determined by u and v. Then x = su + tv. That is, any point can be written as a linear combination of the two vectors. Therefore, The plane determined by u and v is given by the function x(s,t) = su + tv, where s and t each range over all real numbers.

25. Space Determined by Three Vectors Let u, v and v be three non-coplanar vectors. The space (or 3-space) determined by u, v and w is given by the function x(r,s,t) = ru + sv + tw, where s, t and r each range over all real numbers.

26. Example Describe the 3-space determined by the points P(3,1,0,2,1); Q(2,1,4,2,0); R(-1,2,1,3,1) and S(0,2,0,1,0).

27. Homework 2.2