Chapter 1 - Mathematical Preliminaries

Chapter 1 - Mathematical Preliminaries

<p> 1. Mathematical Preliminaries</p><p>1.1. Sets . A set is a well-defined collection of elements. Example: A = {a, b, c} is a set with three elements. a  A means that the element a is in the set A d  A means that the element d is not in the set A. .  denotes the null set or empty set, i.e., the set with no element. . set A is a subset of set B if a  B for all a  A. This is denoted by A  B, or B  A. . union of sets: A  B = {x: x  A or x  B}. . intersection of sets: A  B = {x: x  A and x  B}. . A and B are disjoint sets if A  B = . . the complement of set A in set B is the set B\A = {x: x  B and x  A}. . Cartesian product: Let x  X and y  Y, and let (x, y) be an ordered pair. Then (x, y)  XY, where XY is the Cartesian product of X and Y.</p><p>1.2. Logic Consider two propositions P and Q. If P implies Q, then P is a sufficient condition for Q, and Q is a necessary condition for P. This is denoted by P  Q. If P implies Q and Q implies P, then “P holds if and only if Q holds”, P and Q are equivalent, and P is a necessary and sufficient condition for Q. This is denoted by P  Q. Let {not P} denote the statement that P is not true. Contrapositive: If P implies Q, then {not Q} implies {not P}.</p><p>Example 1: Let x be a real number, P = {x > 0} and Q = {x2 > 0}. Then, P  Q and {not Q} {not P} hold, but {Q  P} is false. Example 2: Let P = {x > 0} and Q = {x3 > 0}. Then P  Q, and {not P}  {not Q} hold.</p><p>1.3. Numbers . natural numbers: N = {1, 2, 3, ...} . integers: Z = {..., -3, -2, -1, 0, 1, 2, 3, ...} . rational numbers: Q = {a/b: a  Z, b  Z, b  0} . irrational numbers: 21/2, 31/2, e, , ... . real numbers: R = {x: x is rational or irrational}</p><p>R+ = {x: x  R, x  0}</p><p>R++ = {x: x  R, x > 0} . complex numbers: C = {a + b i: a  R, b  R, i = (-1)1/2}, where a is the real part, and b is the imaginary part n . the n-fold Cartesian product of R: R = R...R = {(x1, x2, ..., xn): xi  R, i = 1, ..., n}, where xi is the i-th coordinate of x = (x1, x2, ..., xn)</p><p>1.4. Functions Let X and Y be sets. Definition: f: X  Y is a mapping associating each element of X with an element of Y. X is the domain of f 2</p><p> f(x) is the image of x under f f(X) = {f(x): x  X} is the image of X under f . a function: if only one point in Y is associated with each point in X . a correspondence: if more than one point in Y can be associated with each point in X . inverse function: x = f-1(y) if and only if y = f(x) . a function f: X  Y is onto if f(X) = Y. It means that the equation f(x) = y has at least one solution for each y. . if f(x) and f-1(y) are both single-valued, then f is one-to-one. It means that the equation f(x) = y has at most one solution for each y. . composite function: h = g(f(x)) = g  f is the composition of f with g satisfying f: A  B  C, g: C  D, g  f: A  D.</p><p>1.5. Bounds Let S  R. . S is bounded from above (from below) if there exists a  R (b  R) such that x  a (x  b) for all x  S. Then, a is an upper bound of S, and b is a lower bound of S. . The least upper bound (lub) or supremum (sup) of S is the upperbound of S such that there does not exist a smaller upper bound. It is denoted by sup(S). . The supremum of S is called a maximum (max) of S if sup(S)  S. It is denoted by max(S). . The greatest lower bound (glb) or infimum (inf) of S is the lower bound of S such that there does not exist a larger lower bound. It is denoted by inf(S). . The infimum of S is called a minimum (min) of S if inf(S)  S. It is denoted by min(S). </p><p>. Property: If S  R and S has an upperbound, then S has a supremum. If S  R and S has a lowerbound, then S has an infimum.</p><p>1.6. Vector Space Consider a set V. L1- associative law: x + (y + z) = (x + y) + z, for all x, y, z,  V L2- identity: there exists 0  V such that x + 0 = x for all x  V L3- inverse: there exists (-x)  V such that x + (-x) = 0 for all x  V L4- commutative law: x + y = y + x for all x, y  V L5- associative law: (x) = () x for all ,   R, and for all x  V L6- identity: there exists 1  V such that 1x = x for all x  V L7- distributive law: (x + y) = x + y for all   R, and for all x, y  V L8- distributive law: ( + )x = x + x for all ,   R, and for all x  V L9- closure: x  V and y  V implies that (x + y)  V L10- closure: x  V and   R implies that (x)  V.</p><p>Definition: A set V is vector space (or linear space) if it satisfies L1-L10. Then x  V is called a vector.</p><p>Examples: Rn, or Cn is each a vector space.</p><p>1.7. Norms and distances Consider a function d(x, y) satisfying: M1: d(x, y) = 0 if and only if x = y M2: d(x, y) + d(y, z)  d(z, x) M3: d(x, y)  0 for all x, y 3</p><p>M4: d(x, y) = d(y, x).</p><p>Definition: For a given set X, if a function d: XX  R satisfies M1-M4, then: X is a metric space, denoted by (X, d) d is a metric d(x, y) is the distance between points x and y.</p><p>Examples: 2 1/2 d1(x, y) = [i (xi - yi) ] = Euclidian distance, denoted by ||x - y|| d2(x, y) = maxi |xi - yi|</p><p> d3(x, y) = i |xi - yi|</p><p>Note: Topology consists in studying the properties of sets that are independent of the distance measure chosen.</p><p>Definition: Let V be a vector space. A real value function N: V  R is called a norm on V if: N(x)  0 for all x  V N(x) = 0 if and only if x = 0 N(r x) = |r| N(x) for all r  R and x  V, and N(x + y)  N(x) + N(y) for all x, y  V.</p><p>2 1/2 Example: N(x) = d1(x, 0) = [i (xi) ] = ||x|| is the Euclidian norm of x in R.</p><p>Rn, with Euclidian norm and Euclidian metric, is a normed vector space.</p><p>Every normed vector space is a metric space with respect to the induced metric defined by d1(x, y) = ||x - y||.</p><p>1.8. Convex Sets Let X be a vector space (e.g., X = Rn).</p><p>Definition: A set S  X is convex if any x, y  S implies that ( x + (1-) y)  S, for all   R, 0    1.</p><p>Note: ( x + (1-) y) is called a linear combination of x and y.</p><p>Properties: . Any intersection of convex sets is convex.</p><p>. Let Si, i = 1, ..., m, be convex sets in vector space X. Then:</p><p>. (iI i Si) = {x: x = i=1,…,m i xi, xiSi, iR, i = 1, …, m} is a convex set.</p><p>. (S1S2...Sm) = i=1,…,m (Si) is a convex set.</p><p>1.9. Compact Sets Let S  Rn.</p><p> n Definition: An open ball about x0  R with radius r  R, r > 0, is defined as:</p><p>Br(x0) = {x: x  S, d(x, x0) < r}, where d(x, x0) is the Euclidian distance between points x and x0.</p><p> n Definition: An open set S  R is a set S such that, for each x  S, there exists an open ball Br(x) completely contained in S. 4</p><p>. The union of open sets is open. . A finite intersection of open sets is open.</p><p>Definition: The interior of a set S, denoted by int(S), is the union of all open sets contained in S.</p><p>. A set S is open if and only if S = int(S).</p><p>Definition: A set S  Rn is closed if the set (Rn\S) is open.</p><p>. The intersection of closed sets is closed. . A finite union of closed sets is closed.</p><p>Definition: The closure of a set S, denoted by cl(S), is the intersection of all closed sets containing S.</p><p>. A set S is closed if and only if S = cl(S).</p><p>Definition: The boundary of a set S  Rn is the set cl(S)cl(Rn/S).</p><p>Definition: A set S is bounded if there exists an open ball with a finite radius which contains S.</p><p>Definition: A collection of open sets (S)A in a metric space X is said to be an open cover of a n given set S  R if S  A S.</p><p>The open cover (S)A of S is said to admit a finite subcover if there exists a finite </p><p> subcollection (S)F such that S  F S.</p><p>Definition 1: A set S  Rn is compact if and only if it is closed and bounded.</p><p>Definition 2: A subset S of a metric space X is compact if and only if every open cover of S has a finite subcover.</p><p>Note: The definition 2 of compactness applies to sets in any metric space, while definition 1 applies only to sets in Rn.</p><p>1.10. Sequences Let (X, d) be a metric space (e.g., X = Rn), and let S  X.</p><p>Definition: A sequence {xj: j = 1, ..., } in S converges to y if, for any  > 0, there exists a </p><p> positive integer j’ such that j  j’ implies d(y, xj) < . </p><p>This is denoted by y = limj {xj}, where y is the limit of {xj}.</p><p>Note: It does not follow that y = limj {xj}  S. Examples of convergent series: n e = limn (1 + 1/n) , where n  {1, 2, 3, …}  2.71828… More generally, for x  R, x n e = limn {(1 + x/n) , where n  {1, 2, 3, …} x This defines the exponential function, e : R  R++. It satisfies e0 = 1 5</p><p> ex+y = ex  ey (ex)y = exy for x, y  R. x And for y > 0, y = e  ln(y) = x. This defines the logarithmic function, ln(y): R++  R, as the inverse function of ex. It satisfies ln(1) = 0 ln(e) = 1</p><p> ln(x  y) = ln(x) + ln(y), and ln(x/y) = ln(x) - ln(y), for x, y  R++ y ln(x ) = y  ln(x), for x  R++ , y  R.</p><p>Definition: A sequence {xj: j = 1, ..., } in S is a Cauchy sequence if for any  > 0, there exists a </p><p> positive integer j’ such that, for any i, j  j’, d(xi, xj) < . </p><p>Definition: If every Cauchy sequence in a metric space is also a convergent sequence, then the metric space is said to be complete. </p><p> n . A sequence {xj: j = 1, …,} in R is a Cauchy sequence if and only if it is a convergent n sequence, i.e. if and only if there is y  R such that limj {xj}  y.</p><p>By the above definition, this implies that Rn is complete (although not all metric spaces are complete).</p><p>Definition: Let m(j) be an increasing function: m: {1, 2, 3, ...}  {1, 2, 3, ...}, such that m(k+1) </p><p>> m(k). Given a sequence {xj: j = 1, 2, ..., }, {xm(j): m = 1, ..., } is a subsequence of </p><p>{xj: j = 1, 2, ..., }. </p><p>. A set S  Rn is closed if and only if every convergent sequence of points in S converges to a point in S.</p><p>. A set S  Rn is compact if and only if every sequence in S has a convergent subsequence whose limit is in S.</p><p> n . A sequence {xj: j = 1, 2, ..., } in R converges to y if and only if every subsequence of {xj: j = 1, ..., } converges to y.</p><p>. Every bounded sequence contains a convergent subsequence.</p><p>Definition: A sequence {xj: j = 1, 2, ..., } is (strictly) increasing if, for all m > n, xm  (>) xn for all n.</p><p>A sequence {xj: j = 1, 2, ..., } is (strictly) decreasing if, for all m > n, xm  (<) xn for all n . Let X  R, X  . If X is bounded from above (below), there exists an increasing (decreasing) sequence in X converging to sup(X) (inf(X)).</p><p>Definition: Assume that  are allowed as limits of a sequence. </p><p>The lim sup of the sequence {xj: j = 1, 2,…} in R is defined as limj {aj: j = 1, 2 …}, </p><p> where aj = sup{xj, xj+1, xj+2, …}. It is denoted by limj supkj xk, or simply by lim supj xj. 6</p><p>The lim inf of the sequence {xj: j = 1, 2,…} in R is defined as limj {bj: j = 1, 2 …}, </p><p> where bj = inf{xj, xj+1, xj+2, …}. It is denoted by limj infkj xk, or simply by lim inf j xj.</p><p>. A sequence xj in R converges to a limit y  R if and only if y = lim supj xj = lim infj xj.</p>

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