SIMPLICITY OF PSL n (F )

Size: px
Start display at page:

Download "SIMPLICITY OF PSL n (F )"

Transcription

1 SIMPLICITY OF PSL n F KEITH CONRAD Introduction For any field F and integer n 2, the projective special linear group PSL n F is the quotient group of SL n F by its center: PSL n F SL n F /ZSL n F In 83, Galois claimed that PSL 2 F p is a simple group for any prime p > 3, although he didn t give a proof He had to exclude p 2 and p 3 since PSL 2 F 2 S 3 and PSL 2 F 3 A 4, and these groups are not simple It turns out that PSL n F is a simple group for any n 2 and any field F except when n 2 and F F 2 and F 3 The proof of this was developed over essentially 3 years, from 87 to 9: Jordan [4] for n 2 and F F p except n, p 2, 2 and 2,3 Moore [5] for n 2 and F any finite field of size greater than 3 Dickson for n > 2 and F finite [], and for n 2 and F infinite [2] We will prove simplicity of PSL n F using a criterion of Iwasawa [3] from 94 that relates simple quotient groups and doubly transitive group actions This criterion will be developed in Section 2, and applied to PSL 2 F in Section 3 and PSL n F for n > 2 in Section 4 2 Doubly transitive actions and Iwasawa s criterion An action of a group G on a set X is called transitive when, given any distinct x and y in X, there is a g G such that gx y We call the action doubly transitive if any pair of distinct points in X can be carried to any other pair of distinct points in X by some element of G That is, given any two pairs x, x 2 and y, y 2 in X X, where x x 2 and y y 2, there is a g G such that gx y and gx 2 y 2 Although the x i s are distinct and the y j s are distinct, we do allow an x i to be a y j For instance, if x, x, x are three distinct elements of X then there is a g G such that gx x and gx x Here x y x and x 2 x, y 2 x Necessarily a doubly transitive action requires #X 2 Example 2 The action of A 4 on {, 2, 3, 4} is doubly transitive Example 22 The action of D 4 on {, 2, 3, 4}, as vertices of a square, is not doubly transitive since a pair of adjacent vertices can t be sent to a pair of nonadjacent vertices Example 23 For any field F, the group AffF acts on F by a b x ax + b and this action is doubly transitive Example 24 For any field F, the group GL 2 F acts on F 2 { } by the usual way matrices act on vectors, but this action is not doubly transitive since linearly dependent vectors can t be sent to linearly independent vectors by a matrix Theorem 25 If G acts doubly transitively on X then the stabilizer subgroup of any point in X is a maximal subgroup of G

2 2 KEITH CONRAD A maximal subgroup is a proper subgroup contained in no other proper subgroup Proof Pick x X and let H x Stab x Step : For any g H x, G H x H x gh x For g G such that g H x, we will show g H x gh x Both gx and g x are not x, so by double transitivity with the pairs x, gx and x, g x there is some g G such that g x x and g gx g x The first equation implies g H x, so let s write g as h Then hgx g x, so g hgh x H x gh x Step 2: H x is a maximal subgroup of G The group H x is not all of G, since H x fixes x while G carries x to any element of X and #X 2 Let K be a subgroup of G strictly containing H x and pick g K H x By step, G H x H x gh x Both H x and H x gh x are in K, so G K Thus K G The converse of Theorem 25 is false If H is a maximal subgroup of G then left multiplication of G on G/H has H as a stabilizer subgroup, but this action is not doubly transitive if G has odd order because a finite group with a doubly transitive action has even order Theorem 26 Suppose G acts doubly transitively on a set X Any normal subgroup N G acts on X either trivially or transitively Proof Suppose N does not act trivially: nx x for some x X and some n in N Pick any y and y in X with y y By double transitivity, there is g G such that gx y and gnx y Then y gng gx gng y and gng N, so N acts transitively on X Example 27 The action of A 4 on {, 2, 3, 4} is doubly transitive and the normal subgroup {, 234, 324, 423} A 4 acts transitively on {, 2, 3, 4} Example 28 For any field F, let AffF act on F by a b x ax + b This is doubly transitive and the normal subgroup N { b : b F } acts transitively by translations on F Example 29 The action of D 4 on the 4 vertices of a square is not doubly transitive Consistent with Theorem 26, the normal subgroup {, r 2 } of D 4 acts on the vertices neither trivially nor transitively Here is the main group-theoretic result we will use to prove PSL n F is simple Theorem 2 Iwasawa Let G act doubly transitively on a set X Assume the following: For some x X the group Stab x has an abelian normal subgroup whose conjugate subgroups generate G 2 [G, G] G Then G/K is a simple group, where K is the kernel of the action of G on X The kernel of an action is the kernel of the homomorphism G SymX; it s those g that act like the identity on X Proof To show G/K is simple we will show the only normal subgroups of G lying between K and G are K and G Let K N G with N G Let H Stab x, so H is a maximal subgroup of G Theorem 25 Since N is normal, NH {nh : n N, h H} is a subgroup of G, and it contains H, so by maximality either NH H or NH G By Theorem 26, N acts trivially or transitively on X

3 SIMPLICITY OF PSL nf 3 If NH H then N H, so N fixes x Therefore N does not act transitively on X, so N must act trivially on X, which implies N K Since K N by hypothesis, we have N K Now suppose NH G Let U be the abelian normal subgroup of H in the hypothesis: its conjugate subgroups generate G Since U H, NU NH G Then for g G, gug gnug NU, which shows NU contains all the conjugate subgroups of U By hypothesis it follows that NU G Thus G/N NU/N U/N U Since U is abelian, the isomorphism tells us that G/N is abelian, so [G, G] N Since G [G, G] by hypothesis, we have N G Example 2 We can use Theorem 2 to show A 5 is a simple group Its natural action on {, 2, 3, 4, 5} is doubly transitive Let x 5, so Stab x A4, which has the abelian normal subgroup {, 234, 324, 423} The A 5 -conjugates of this subgroup generate A 5 since the 2,2-cycles in A 5 are all conjugate in A 5 and they generate A 5 The commutator subgroup [A 5, A 5 ] contains every 2,2-cycle: if a, b, c, d are distinct then Therefore [A 5, A 5 ] A 5, so A 5 is simple abcd abcabdabc abd 3 Simplicity of PSL 2 F Let F be a field The group SL 2 F acts on F 2 { }, but this action is not doubly transitive since linearly dependent vectors can t be sent to linearly independent vectors by a matrix We saw this for GL 2 F in Example 24, and the same argument applies for its subgroup SL 2 F Linearly dependent vectors in F 2 lie along the same line through the origin, so let s consider the action of SL 2 F on the linear subspaces of F 2 : let A SL 2 F send the line L F v to the line AL F Av Equivalently, we let SL 2 F act on P F, the projective line over F Theorem 3 The action of SL 2 F on the linear subspaces of F 2 is doubly transitive Proof An obvious pair of distinct linear subspaces in F 2 is F and F It suffices to show that, given any two distinct linear subspaces F v and F w, there is an A SL 2 F that sends F to F v and F to F w, because we can then use F and F as an intermediate step to send any pair of distinct linear subspaces to any other pair of distinct linear subspaces Let v a c and w b d Since F v F w, the vectors v and w are linearly independent, so D : ad bc is nonzero Let A /D, which has determinant ad/d b/dc c d/d D/D, so A SL 2 F Since A a c v and A b/d d/d /Dw, A sends F to F v and F to F /Dw F w We will apply Iwasawa s criterion Theorem 2 to SL 2 F acting on the set of linear subspaces of F 2 This action is doubly transitive by Theorem 3 It remains to check the following: the kernel K of this action is the center of SL 2 F, so SL 2 F /K PSL 2 F, the stabilizer subgroup of contains an abelian normal subgroup whose conjugate subgroups generate SL 2 F,

4 4 KEITH CONRAD [SL 2 F, SL 2 F ] SL 2 F It is only in the third part that we will require #F > 3 At some point we need to avoid F F 2 and F F 3, because PSL 2 F 2 and PSL 2 F 3 are not simple Theorem 32 The kernel of the action of SL 2 F on the linear subspaces of F 2 is the center of SL 2 F Proof A matrix a c d b SL 2F is in the kernel K of the action when it sends each linear subspace of F 2 back to itself If the matrix preserves the lines F and F then c and b, so a c d b a d a The determinant is, so d /a If /a preserves the line F then a /a, so a ± This means c d ± Conversely, the matrices ± both act trivially on the linear subspaces of F 2, so K {± } If a matrix a c d b is in the center of SL 2F then it commutes with and, which implies a d and b c check! Therefore a c d b a a Since this has determinant, a 2, so a ± Conversely, ± commutes with all matrices Let x F Its stabilizer subgroup in SL2 F is { Stab F A SL 2 F : A F { } SL d 2 F { : a F /a, b F This subgroup has a normal subgroup { } U which is abelian since λ µ λ+µ { λ } } : λ F, Theorem 33 The subgroup U and its conjugates generate SL 2 F More precisely, any matrix of the form is conjugate to a matrix of the form, and every element of SL 2 F is the product of at most 4 elements of the form or This is the analogue for SL 2 F of the 3-cycles generating A n Proof The matrix is in SL 2F and λ λ, so conjugates U { } to the group of lower triangular matrices { } Pick a c d b in SL 2F To show it is a product of matrices of type or, first suppose b Then c d If c then c d d /b a /c b c } a /b d /c

5 SIMPLICITY OF PSL nf 5 If b and c then the matrix is a /a, and a /a a/a a /a So far F has been any field Now we reach a result that requires #F 4 Theorem 34 If #F 4 then [SL 2 F, SL 2 F ] SL 2 F Proof We compute an explicit commutator: a b a b /a /a ba 2 Since #F 4, there is an a,, or in F, so a 2 Using this value of a and letting b run over F shows [SL 2 F, SL 2 F ] contains U Since the commutator subgroup is normal, it contains every subgroup conjugate to U, so [SL 2 F, SL 2 F ] SL 2 F by Theorem 33 Theorem 34 is false when #F 2 or 3: SL 2 F 2 GL 2 F 2 is isomorphic to S 3 and [S 3, S 3 ] A 3 In SL 2 F 3 there is a unique 2-Sylow subgroup, so it is normal, and its index is 3, so the quotient by it is abelian Therefore the commutator subgroup of SL 2 F 3 lies inside the 2-Sylow subgroup in fact, the commutator subgroup is the 2-Sylow subgroup Theorem 35 If #F 4 then the group PSL 2 F is simple Proof By the previous four theorems the action of SL 2 F on the linear subspaces of F 2 satisfies the hypotheses of Iwasawa s theorem, and its kernel is the center of SL 2 F 4 Simplicity of PSL n F for n > 2 To prove PSL n F is simple for any F when n > 2, we will study the action of SL n F on the linear subspaces of F n, which is the projective space P n F Theorem 4 The action of SL n F on P n F is doubly transitive with kernel equal to the center of the group and the stabilizer of some point has an abelian normal subgroup Proof For nonzero v in F n, write the linear subspace F v as [v] Pick [v ] [v 2 ] and [w ] [w 2 ] in P n F We seek an A SL n F such that A[v ] [w ] and A[v 2 ] [w 2 ] Extend v, v 2 and w, w 2 to bases v,, v n and w,, w n of F n Let L: F n F n be the linear map where Lv i w i for all i, so det L and on P n F we have L[v i ] [w i ] for all i In particular, L[v ] [w ] and L[v 2 ] [w 2 ] Alas, det L may not be For c F, let L c : F n F n be the linear map where L c v i w i for i n and L c v n cw n, so L L Then L c sends [v i ] to [w i ] for all i and det L c c det L, so L c SL n F for c / det L If A SL n F is in the kernel of this action then A[v] [v] for all nonzero v F n, so Av λ v v, where λ v F : every nonzero element of F n is an eigenvector of A The only matrices for which all vectors are eigenvectors are scalar diagonal matrices To prove this, use the equation Av λ v v when v e i, v e j, and v e i + e j for the standard basis e,, e n of F n The equation Ae i + e j Ae i + Ae j implies λ ei +e j e i + λ ei +e j e j λ ei e i +λ ej e j, so λ ei λ ei +e j λ ej Let λ be the common value of λ ei over all i, so Av λv when v runs through the basis By linearity, Av λv for all v F n, so A is a scalar diagonal matrix with determinant It is left to the reader to check that the center of SL n F is also the scalar diagonal matrices with determinant

6 6 KEITH CONRAD To show the stabilizer of some point in P n F has an abelian normal subgroup, we look at the stabilizer H of the point Pn F, which is the group of n n determinant matrices a M where a F, M GL n F, and is a row vector of length n For this to be in SL n F means a / det M The projection H GL n F sending a M onto M has abelian kernel { } 4 U : F I n n To conclude by Iwasawa s theorem that PSL n F is simple, it remains to show the subgroups of SL n F that are conjugate to U generate SL n F, [SL n F, SL n F ] SL n F This will follow from a study of the elementary matrices I n + λe ij where i j and λ F An example of such a matrix when n 3 is I 3 + λe 23 λ The matrix I n + λe ij has s on the main diagonal and a λ in the i, j position Therefore its determinant is, so such matrices are in SL n F The most basic example of such an elementary matrix in U is 42 I n + E 2 I n 2 Here are the two properties we will need about the elementary matrices I n + λe ij : For n > 2, any I n + λe ij is conjugate in SL n F to I n + E 2 2 For n > 2, the matrices I n + λe ij generate SL n F These properties imply the conjugates of I n + E 2 generate SL n F Since I n + E 2 U, the subgroups of SL n F that are conjugate to U generate SL n F, so the next theorem would complete the proof that PSL n F is simple for n > 2 Theorem 42 For n > 2, [SL n F, SL n F ] SL n F Proof We will show I n + E 2 is a commutator in SL n F Then, since the commutator subgroup is normal, the above two properties of elementary matrices imply that [SL n F, SL n F ] contains every I n + λe ij, and therefore [SL n F, SL n F ] SL n F

7 Set g SIMPLICITY OF PSL nf 7 and h Then ghg h, which is I 3 + E 2 For n 4, I n + E 2 is the block matrix I n 2 I n 3 g O O I n 3 h O O I n 3 g O h O O I n 3 O I n 3 All that remains is to prove the two properties we listed of the elementary matrices, and this is handled by the next two theorems Theorem 43 For n > 2, any I n + λe ij with λ F is conjugate in SL n F to I n + E 2 Proof Let T I n + λe ij For the standard basis e,, e n of F n, { e k, if k j, T e k λe i + e j, if k j We want asis e,, e n of F n in which the matrix representation of T is I n + E 2, ie, T e k e k for k 2 and T e 2 e + e 2 Define asis f,, f n of F n by f λe i, f 2 e j, and f 3,, f n is some ordering of the n 2 standard basis vectors of F n besides e i and e j Then T f λt e i λe i f, T f 2 T e j λe i + e j f + f 2, T f k f k for k 3, so relative to the basis f,, f n the matrix representation of T is I n + E 2 Therefore T AI n + E 2 A, where A is the matrix such that Ae k f k for all k Check T AI n + E 2 A by checking both sides take the same values at f,, f n There is no reason to expect det A, so the equation T AI n + E 2 A shows us T and I n + E 2 are conjugate in GL n F, rather than in SL n F With a small change we can get a conjugating matrix in SL n F, as follows For any c F, we have where T A c I n + E 2 A c, A c e k { f k, if k < n, cf n, if k n Check both sides of the equation T A c I n +E 2 A c are equal at f,, f n, cf n, where we need n > 2 for both sides to be the same at f 2 The columns of A c are the same as the columns of A except for the nth column, where A c is c times the nth column of A

8 8 KEITH CONRAD Therefore deta c c det A, so if we use c / det A then A c SL n F That proves T is conjugate to I n + E 2 in SL n F Example 44 Let T I 3 + λe 23 λ Starting from the standard basis e, e 2, e 3 of F 3, introduce a new basis f, f 2, f 3 by f λe 2, f 2 e 3, and f 3 e Since T f f, T f 2 f + f 2, and T f 3 f 3, we have λ λ λ, where the conjugating matrix λ has for its columns f, f 2, and f 3 in order The determinant of this conjugating matrix is λ, so it is usually not in SL 3 F If we insert a nonzero constant c into the third column then we get a more general conjugation relation between I 3 + λe 23 and I 3 + E 2 : λ c λ c λ The conjugating matrix has determinant λc, so using c /λ makes the conjugating matrix have determinant, which exhibits an SL 3 F -conjugation between I 3 + λe 23 and I 3 + E 2 Theorem 45 For n 2, the matrices I n + λe ij with i j and λ F generate SL n F Proof This will be a sequence of tedious computations By a matrix calculation, { E il, if j k, 43 E ij E kl δ jk E il O, if j k Therefore I n + λe ij I n + µe ij I n + λ + µe ij, so I n + λe ij λe ij, so the theorem amounts to saying that every element of SL n F is a product of matrices I n +λe ij We already proved the theorem for n 2 in Theorem 33, so we can take n > 2 and assume the theorem is proved for SL n F Pick A SL n F We will show that by multiplying A on the left or right by suitable elementary matrices I n + λe ij we can obtain lock matrix A Since this is in SL nf, taking its determinant shows det A, so A SL n F By induction A is a product of elementary matrices I n + λe ij, so A would be a product of block matrices of the form I n +λe ij, which is I n + λe i+ j+ Therefore product of some I n + λe ij Aproduct of some I n + λe ij product of some I n + λe ij, and we can solve for A to see that it is a product of matrices I n + λe ij

9 SIMPLICITY OF PSL nf 9 The effect of multiplying an n n matrix A by I n + λe ij on the left or right is an elementary row or column operation: a a n I n +λe ij A a i + λa j a in + λa jn ith row ith row of A + λjth row of A a n a nn and AI n + λe ij a a j + λa i a n a n a nj + λa ni a nn jth col jth col of A + λith col of A Looking along the first column of A, some entry is not since det A If some a k in A is not where k >, then 44 I n + a E k A a k If a 2,, a n are all, then a and I n + E 2 A a Then by 44 with k 2, I n + a E 2 a I n + E 2 A a, Once we have a matrix with upper left entry, multiplying it on the left by I n + λe i for i will add λ to the i, -entry, so with a suitable λ we can make the i, -entry of the matrix Thus multiplication on the left by suitable matrices of the form I n + λe ij produces lock matrix B whose first column is all s except for the upper left entry, which is Multiplying this matrix on the right by I n + λe j for j adds λ to the, j-entry without changing any column but the jth column With a suitable choice of λ we can make the, j-entry equal to, and carrying this out for j 2,, n leads to a block matrix A, which is what we need to conclude the proof by induction References [] L E Dickson, The analytic representation of substitutions on a power of a prime number of letters with a discussion of the linear group, Ann Math 897, 6 83 [2] L E Dickson, Theory of linear groups in an arbitrary field, Trans Amer Math Soc 2 9, [3] K Iwasawa, Über die Einfachkeit der speziellen projection Gruppen, Proc Imperial Acad Tokyo 7 94, [4] C Jordan, Traité des Substitutions, Gauthier-Villars, Paris, 87 [5] E H Moore, A doubly-infinite system of simple groups, Bull New York Math Soc 3 893, 73 78

GENERATING SETS KEITH CONRAD

GENERATING SETS KEITH CONRAD GENERATING SETS KEITH CONRAD 1 Introduction In R n, every vector can be written as a unique linear combination of the standard basis e 1,, e n A notion weaker than a basis is a spanning set: a set of vectors

More information

CONSEQUENCES OF THE SYLOW THEOREMS

CONSEQUENCES OF THE SYLOW THEOREMS CONSEQUENCES OF THE SYLOW THEOREMS KEITH CONRAD For a group theorist, Sylow s Theorem is such a basic tool, and so fundamental, that it is used almost without thinking, like breathing. Geoff Robinson 1.

More information

G = G 0 > G 1 > > G k = {e}

G = G 0 > G 1 > > G k = {e} Proposition 49. 1. A group G is nilpotent if and only if G appears as an element of its upper central series. 2. If G is nilpotent, then the upper central series and the lower central series have the same

More information

Notes on Determinant

Notes on Determinant ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without

More information

Similarity and Diagonalization. Similar Matrices

Similarity and Diagonalization. Similar Matrices MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 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 information

Inner Product Spaces and Orthogonality

Inner Product Spaces and Orthogonality Inner Product Spaces and Orthogonality week 3-4 Fall 2006 Dot product of R n The inner product or dot product of R n is a function, defined by u, v a b + a 2 b 2 + + a n b n for u a, a 2,, a n T, v b,

More information

NOTES ON LINEAR TRANSFORMATIONS

NOTES ON LINEAR TRANSFORMATIONS NOTES ON LINEAR TRANSFORMATIONS Definition 1. Let V and W be vector spaces. A function T : V W is a linear transformation from V to W if the following two properties hold. i T v + v = T v + T v for all

More information

ISOMETRIES OF R n KEITH CONRAD

ISOMETRIES OF R n KEITH CONRAD ISOMETRIES OF R n KEITH CONRAD 1. Introduction An isometry of R n is a function h: R n R n that preserves the distance between vectors: h(v) h(w) = v w for all v and w in R n, where (x 1,..., x n ) = x

More information

by the matrix A results in a vector which is a reflection of the given

by the matrix A results in a vector which is a reflection of the given Eigenvalues & Eigenvectors Example Suppose Then So, geometrically, multiplying a vector in by the matrix A results in a vector which is a reflection of the given vector about the y-axis We observe that

More information

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i.

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i. Math 5A HW4 Solutions September 5, 202 University of California, Los Angeles Problem 4..3b Calculate the determinant, 5 2i 6 + 4i 3 + i 7i Solution: The textbook s instructions give us, (5 2i)7i (6 + 4i)(

More information

1 Sets and Set Notation.

1 Sets and Set Notation. LINEAR ALGEBRA MATH 27.6 SPRING 23 (COHEN) LECTURE NOTES Sets and Set Notation. Definition (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most

More information

THE DIMENSION OF A VECTOR SPACE

THE DIMENSION OF A VECTOR SPACE THE DIMENSION OF A VECTOR SPACE KEITH CONRAD This handout is a supplementary discussion leading up to the definition of dimension and some of its basic properties. Let V be a vector space over a field

More information

GROUPS ACTING ON A SET

GROUPS ACTING ON A SET GROUPS ACTING ON A SET MATH 435 SPRING 2012 NOTES FROM FEBRUARY 27TH, 2012 1. Left group actions Definition 1.1. Suppose that G is a group and S is a set. A left (group) action of G on S is a rule for

More information

T ( a i x i ) = a i T (x i ).

T ( a i x i ) = a i T (x i ). Chapter 2 Defn 1. (p. 65) Let V and W be vector spaces (over F ). We call a function T : V W a linear transformation form V to W if, for all x, y V and c F, we have (a) T (x + y) = T (x) + T (y) and (b)

More information

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called

More information

1 0 5 3 3 A = 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0

1 0 5 3 3 A = 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0 Solutions: Assignment 4.. Find the redundant column vectors of the given matrix A by inspection. Then find a basis of the image of A and a basis of the kernel of A. 5 A The second and third columns are

More information

RINGS WITH A POLYNOMIAL IDENTITY

RINGS WITH A POLYNOMIAL IDENTITY RINGS WITH A POLYNOMIAL IDENTITY IRVING KAPLANSKY 1. Introduction. In connection with his investigation of projective planes, M. Hall [2, Theorem 6.2]* proved the following theorem: a division ring D in

More information

Matrix Representations of Linear Transformations and Changes of Coordinates

Matrix Representations of Linear Transformations and Changes of Coordinates Matrix Representations of Linear Transformations and Changes of Coordinates 01 Subspaces and Bases 011 Definitions A subspace V of R n is a subset of R n that contains the zero element and is closed under

More information

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 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. Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given

More information

Solution to Homework 2

Solution to Homework 2 Solution to Homework 2 Olena Bormashenko September 23, 2011 Section 1.4: 1(a)(b)(i)(k), 4, 5, 14; Section 1.5: 1(a)(b)(c)(d)(e)(n), 2(a)(c), 13, 16, 17, 18, 27 Section 1.4 1. Compute the following, if

More information

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1. MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

More information

13 MATH FACTS 101. 2 a = 1. 7. The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.

13 MATH FACTS 101. 2 a = 1. 7. The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions. 3 MATH FACTS 0 3 MATH FACTS 3. Vectors 3.. Definition We use the overhead arrow to denote a column vector, i.e., a linear segment with a direction. For example, in three-space, we write a vector in terms

More information

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively. Chapter 7 Eigenvalues and Eigenvectors In this last chapter of our exploration of Linear Algebra we will revisit eigenvalues and eigenvectors of matrices, concepts that were already introduced in Geometry

More information

LS.6 Solution Matrices

LS.6 Solution Matrices LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions

More information

University of Lille I PC first year list of exercises n 7. Review

University of Lille I PC first year list of exercises n 7. Review University of Lille I PC first year list of exercises n 7 Review Exercise Solve the following systems in 4 different ways (by substitution, by the Gauss method, by inverting the matrix of coefficients

More information

Section 1.7 22 Continued

Section 1.7 22 Continued Section 1.5 23 A homogeneous equation is always consistent. TRUE - The trivial solution is always a solution. The equation Ax = 0 gives an explicit descriptions of its solution set. FALSE - The equation

More information

Linear Algebra Notes for Marsden and Tromba Vector Calculus

Linear Algebra Notes for Marsden and Tromba Vector Calculus Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of

More information

The Determinant: a Means to Calculate Volume

The Determinant: a Means to Calculate Volume The Determinant: a Means to Calculate Volume Bo Peng August 20, 2007 Abstract This paper gives a definition of the determinant and lists many of its well-known properties Volumes of parallelepipeds are

More information

Systems of Linear Equations

Systems of Linear Equations Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and

More information

Mathematics Course 111: Algebra I Part IV: Vector Spaces

Mathematics Course 111: Algebra I Part IV: Vector Spaces Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are

More information

Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN. Part II: Group Theory

Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN. Part II: Group Theory Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN Part II: Group Theory No rights reserved. Any part of this work can be reproduced or transmitted in any form or by any means. Version: 1.1 Release: Jan 2013

More information

Direct Methods for Solving Linear Systems. Matrix Factorization

Direct Methods for Solving Linear Systems. Matrix Factorization Direct Methods for Solving Linear Systems Matrix Factorization Numerical Analysis (9th Edition) R L Burden & J D Faires Beamer Presentation Slides prepared by John Carroll Dublin City University c 2011

More information

Group Theory. Contents

Group Theory. Contents Group Theory Contents Chapter 1: Review... 2 Chapter 2: Permutation Groups and Group Actions... 3 Orbits and Transitivity... 6 Specific Actions The Right regular and coset actions... 8 The Conjugation

More information

Math 312 Homework 1 Solutions

Math 312 Homework 1 Solutions Math 31 Homework 1 Solutions Last modified: July 15, 01 This homework is due on Thursday, July 1th, 01 at 1:10pm Please turn it in during class, or in my mailbox in the main math office (next to 4W1) Please

More information

The Characteristic Polynomial

The Characteristic Polynomial Physics 116A Winter 2011 The Characteristic Polynomial 1 Coefficients of the characteristic polynomial Consider the eigenvalue problem for an n n matrix A, A v = λ v, v 0 (1) The solution to this problem

More information

Using row reduction to calculate the inverse and the determinant of a square matrix

Using row reduction to calculate the inverse and the determinant of a square matrix Using row reduction to calculate the inverse and the determinant of a square matrix Notes for MATH 0290 Honors by Prof. Anna Vainchtein 1 Inverse of a square matrix An n n square matrix A is called invertible

More information

Elements of Abstract Group Theory

Elements of Abstract Group Theory Chapter 2 Elements of Abstract Group Theory Mathematics is a game played according to certain simple rules with meaningless marks on paper. David Hilbert The importance of symmetry in physics, and for

More information

PYTHAGOREAN TRIPLES KEITH CONRAD

PYTHAGOREAN TRIPLES KEITH CONRAD PYTHAGOREAN TRIPLES KEITH CONRAD 1. Introduction A Pythagorean triple is a triple of positive integers (a, b, c) where a + b = c. Examples include (3, 4, 5), (5, 1, 13), and (8, 15, 17). Below is an ancient

More information

Solving Systems of Linear Equations

Solving Systems of Linear Equations LECTURE 5 Solving Systems of Linear Equations Recall that we introduced the notion of matrices as a way of standardizing the expression of systems of linear equations In today s lecture I shall show how

More information

Unit 18 Determinants

Unit 18 Determinants Unit 18 Determinants Every square matrix has a number associated with it, called its determinant. In this section, we determine how to calculate this number, and also look at some of the properties of

More information

IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL. 1. Introduction

IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL. 1. Introduction IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL R. DRNOVŠEK, T. KOŠIR Dedicated to Prof. Heydar Radjavi on the occasion of his seventieth birthday. Abstract. Let S be an irreducible

More information

Vector and Matrix Norms

Vector and Matrix Norms Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty

More information

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws. Matrix Algebra A. Doerr Before reading the text or the following notes glance at the following list of basic matrix algebra laws. Some Basic Matrix Laws Assume the orders of the matrices are such that

More information

LINEAR ALGEBRA W W L CHEN

LINEAR ALGEBRA W W L CHEN LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2008 This chapter is available free to all individuals, on understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied,

More information

Lectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain

Lectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain Lectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain 1. Orthogonal matrices and orthonormal sets An n n real-valued matrix A is said to be an orthogonal

More information

ZORN S LEMMA AND SOME APPLICATIONS

ZORN S LEMMA AND SOME APPLICATIONS ZORN S LEMMA AND SOME APPLICATIONS KEITH CONRAD 1. Introduction Zorn s lemma is a result in set theory that appears in proofs of some non-constructive existence theorems throughout mathematics. We will

More information

MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.

MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set. MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set. Vector space A vector space is a set V equipped with two operations, addition V V (x,y) x + y V and scalar

More information

GROUP ALGEBRAS. ANDREI YAFAEV

GROUP ALGEBRAS. ANDREI YAFAEV GROUP ALGEBRAS. ANDREI YAFAEV We will associate a certain algebra to a finite group and prove that it is semisimple. Then we will apply Wedderburn s theory to its study. Definition 0.1. Let G be a finite

More information

Lecture 4: Partitioned Matrices and Determinants

Lecture 4: Partitioned Matrices and Determinants Lecture 4: Partitioned Matrices and Determinants 1 Elementary row operations Recall the elementary operations on the rows of a matrix, equivalent to premultiplying by an elementary matrix E: (1) multiplying

More information

Chapter 19. General Matrices. An n m matrix is an array. a 11 a 12 a 1m a 21 a 22 a 2m A = a n1 a n2 a nm. The matrix A has n row vectors

Chapter 19. General Matrices. An n m matrix is an array. a 11 a 12 a 1m a 21 a 22 a 2m A = a n1 a n2 a nm. The matrix A has n row vectors Chapter 9. General Matrices An n m matrix is an array a a a m a a a m... = [a ij]. a n a n a nm The matrix A has n row vectors and m column vectors row i (A) = [a i, a i,..., a im ] R m a j a j a nj col

More information

Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

More information

4.5 Linear Dependence and Linear Independence

4.5 Linear Dependence and Linear Independence 4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then

More information

MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors. Jordan canonical form (continued).

MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors. Jordan canonical form (continued). MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors Jordan canonical form (continued) Jordan canonical form A Jordan block is a square matrix of the form λ 1 0 0 0 0 λ 1 0 0 0 0 λ 0 0 J = 0

More information

Abstract Algebra Cheat Sheet

Abstract Algebra Cheat Sheet Abstract Algebra Cheat Sheet 16 December 2002 By Brendan Kidwell, based on Dr. Ward Heilman s notes for his Abstract Algebra class. Notes: Where applicable, page numbers are listed in parentheses at the

More information

Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan

Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan 3 Binary Operations We are used to addition and multiplication of real numbers. These operations combine two real numbers

More information

Section 6.1 - Inner Products and Norms

Section 6.1 - Inner Products and Norms Section 6.1 - Inner Products and Norms Definition. Let V be a vector space over F {R, C}. An inner product on V is a function that assigns, to every ordered pair of vectors x and y in V, a scalar in F,

More information

GROUP ACTIONS KEITH CONRAD

GROUP ACTIONS KEITH CONRAD GROUP ACTIONS KEITH CONRAD 1. Introduction The symmetric groups S n, alternating groups A n, and (for n 3) dihedral groups D n behave, by their very definition, as permutations on certain sets. The groups

More information

Linear Algebra I. Ronald van Luijk, 2012

Linear Algebra I. Ronald van Luijk, 2012 Linear Algebra I Ronald van Luijk, 2012 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents 1. Vector spaces 3 1.1. Examples 3 1.2. Fields 4 1.3. The field of complex numbers. 6 1.4.

More information

1 Homework 1. [p 0 q i+j +... + p i 1 q j+1 ] + [p i q j ] + [p i+1 q j 1 +... + p i+j q 0 ]

1 Homework 1. [p 0 q i+j +... + p i 1 q j+1 ] + [p i q j ] + [p i+1 q j 1 +... + p i+j q 0 ] 1 Homework 1 (1) Prove the ideal (3,x) is a maximal ideal in Z[x]. SOLUTION: Suppose we expand this ideal by including another generator polynomial, P / (3, x). Write P = n + x Q with n an integer not

More information

Solving Linear Systems, Continued and The Inverse of a Matrix

Solving Linear Systems, Continued and The Inverse of a Matrix , Continued and The of a Matrix Calculus III Summer 2013, Session II Monday, July 15, 2013 Agenda 1. The rank of a matrix 2. The inverse of a square matrix Gaussian Gaussian solves a linear system by reducing

More information

ON GALOIS REALIZATIONS OF THE 2-COVERABLE SYMMETRIC AND ALTERNATING GROUPS

ON GALOIS REALIZATIONS OF THE 2-COVERABLE SYMMETRIC AND ALTERNATING GROUPS ON GALOIS REALIZATIONS OF THE 2-COVERABLE SYMMETRIC AND ALTERNATING GROUPS DANIEL RABAYEV AND JACK SONN Abstract. Let f(x) be a monic polynomial in Z[x] with no rational roots but with roots in Q p for

More information

Introduction to Matrix Algebra

Introduction to Matrix Algebra Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 1 Introduction to Matrix Algebra Definitions: A matrix is a collection of numbers ordered by rows and columns. It is customary

More information

Galois representations with open image

Galois representations with open image Galois representations with open image Ralph Greenberg University of Washington Seattle, Washington, USA May 7th, 2011 Introduction This talk will be about representations of the absolute Galois group

More information

Linear Algebra Notes

Linear Algebra Notes Linear Algebra Notes Chapter 19 KERNEL AND IMAGE OF A MATRIX Take an n m matrix a 11 a 12 a 1m a 21 a 22 a 2m a n1 a n2 a nm and think of it as a function A : R m R n The kernel of A is defined as Note

More information

7 Gaussian Elimination and LU Factorization

7 Gaussian Elimination and LU Factorization 7 Gaussian Elimination and LU Factorization In this final section on matrix factorization methods for solving Ax = b we want to take a closer look at Gaussian elimination (probably the best known method

More information

8 Square matrices continued: Determinants

8 Square matrices continued: Determinants 8 Square matrices continued: Determinants 8. Introduction Determinants give us important information about square matrices, and, as we ll soon see, are essential for the computation of eigenvalues. You

More information

( ) which must be a vector

( ) which must be a vector MATH 37 Linear Transformations from Rn to Rm Dr. Neal, WKU Let T : R n R m be a function which maps vectors from R n to R m. Then T is called a linear transformation if the following two properties are

More information

Linear Algebra. A vector space (over R) is an ordered quadruple. such that V is a set; 0 V ; and the following eight axioms hold:

Linear Algebra. A vector space (over R) is an ordered quadruple. such that V is a set; 0 V ; and the following eight axioms hold: Linear Algebra A vector space (over R) is an ordered quadruple (V, 0, α, µ) such that V is a set; 0 V ; and the following eight axioms hold: α : V V V and µ : R V V ; (i) α(α(u, v), w) = α(u, α(v, w)),

More information

Chapter 17. Orthogonal Matrices and Symmetries of Space

Chapter 17. Orthogonal Matrices and Symmetries of Space Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length

More information

Chapter 7: Products and quotients

Chapter 7: Products and quotients Chapter 7: Products and quotients Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 42, Spring 24 M. Macauley (Clemson) Chapter 7: Products

More information

Numerical Analysis Lecture Notes

Numerical Analysis Lecture Notes Numerical Analysis Lecture Notes Peter J. Olver 6. Eigenvalues and Singular Values In this section, we collect together the basic facts about eigenvalues and eigenvectors. From a geometrical viewpoint,

More information

Solutions to Math 51 First Exam January 29, 2015

Solutions to Math 51 First Exam January 29, 2015 Solutions to Math 5 First Exam January 29, 25. ( points) (a) Complete the following sentence: A set of vectors {v,..., v k } is defined to be linearly dependent if (2 points) there exist c,... c k R, not

More information

THE SIGN OF A PERMUTATION

THE SIGN OF A PERMUTATION THE SIGN OF A PERMUTATION KEITH CONRAD 1. Introduction Throughout this discussion, n 2. Any cycle in S n is a product of transpositions: the identity (1) is (12)(12), and a k-cycle with k 2 can be written

More information

Finite dimensional C -algebras

Finite dimensional C -algebras Finite dimensional C -algebras S. Sundar September 14, 2012 Throughout H, K stand for finite dimensional Hilbert spaces. 1 Spectral theorem for self-adjoint opertors Let A B(H) and let {ξ 1, ξ 2,, ξ n

More information

Lecture 3: Finding integer solutions to systems of linear equations

Lecture 3: Finding integer solutions to systems of linear equations Lecture 3: Finding integer solutions to systems of linear equations Algorithmic Number Theory (Fall 2014) Rutgers University Swastik Kopparty Scribe: Abhishek Bhrushundi 1 Overview The goal of this lecture

More information

The cover SU(2) SO(3) and related topics

The cover SU(2) SO(3) and related topics The cover SU(2) SO(3) and related topics Iordan Ganev December 2011 Abstract The subgroup U of unit quaternions is isomorphic to SU(2) and is a double cover of SO(3). This allows a simple computation of

More information

Methods for Finding Bases

Methods for Finding Bases Methods for Finding Bases Bases for the subspaces of a matrix Row-reduction methods can be used to find bases. Let us now look at an example illustrating how to obtain bases for the row space, null space,

More information

MAT 200, Midterm Exam Solution. a. (5 points) Compute the determinant of the matrix A =

MAT 200, Midterm Exam Solution. a. (5 points) Compute the determinant of the matrix A = MAT 200, Midterm Exam Solution. (0 points total) a. (5 points) Compute the determinant of the matrix 2 2 0 A = 0 3 0 3 0 Answer: det A = 3. The most efficient way is to develop the determinant along the

More information

THE AVERAGE DEGREE OF AN IRREDUCIBLE CHARACTER OF A FINITE GROUP

THE AVERAGE DEGREE OF AN IRREDUCIBLE CHARACTER OF A FINITE GROUP THE AVERAGE DEGREE OF AN IRREDUCIBLE CHARACTER OF A FINITE GROUP by I. M. Isaacs Mathematics Department University of Wisconsin 480 Lincoln Dr. Madison, WI 53706 USA E-Mail: isaacs@math.wisc.edu Maria

More information

3. INNER PRODUCT SPACES

3. INNER PRODUCT SPACES . INNER PRODUCT SPACES.. Definition So far we have studied abstract vector spaces. These are a generalisation of the geometric spaces R and R. But these have more structure than just that of a vector space.

More information

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH ZACHARY ABEL 1. Introduction In this survey we discuss properties of the Higman-Sims graph, which has 100 vertices, 1100 edges, and is 22 regular. In fact

More information

SUBGROUPS OF CYCLIC GROUPS. 1. Introduction In a group G, we denote the (cyclic) group of powers of some g G by

SUBGROUPS OF CYCLIC GROUPS. 1. Introduction In a group G, we denote the (cyclic) group of powers of some g G by SUBGROUPS OF CYCLIC GROUPS KEITH CONRAD 1. Introduction In a group G, we denote the (cyclic) group of powers of some g G by g = {g k : k Z}. If G = g, then G itself is cyclic, with g as a generator. Examples

More information

Name: Section Registered In:

Name: Section Registered In: Name: Section Registered In: Math 125 Exam 3 Version 1 April 24, 2006 60 total points possible 1. (5pts) Use Cramer s Rule to solve 3x + 4y = 30 x 2y = 8. Be sure to show enough detail that shows you are

More information

Lecture 1: Schur s Unitary Triangularization Theorem

Lecture 1: Schur s Unitary Triangularization Theorem Lecture 1: Schur s Unitary Triangularization Theorem This lecture introduces the notion of unitary equivalence and presents Schur s theorem and some of its consequences It roughly corresponds to Sections

More information

MATH2210 Notebook 1 Fall Semester 2016/2017. 1 MATH2210 Notebook 1 3. 1.1 Solving Systems of Linear Equations... 3

MATH2210 Notebook 1 Fall Semester 2016/2017. 1 MATH2210 Notebook 1 3. 1.1 Solving Systems of Linear Equations... 3 MATH0 Notebook Fall Semester 06/07 prepared by Professor Jenny Baglivo c Copyright 009 07 by Jenny A. Baglivo. All Rights Reserved. Contents MATH0 Notebook 3. Solving Systems of Linear Equations........................

More information

RESULTANT AND DISCRIMINANT OF POLYNOMIALS

RESULTANT AND DISCRIMINANT OF POLYNOMIALS RESULTANT AND DISCRIMINANT OF POLYNOMIALS SVANTE JANSON Abstract. This is a collection of classical results about resultants and discriminants for polynomials, compiled mainly for my own use. All results

More information

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation

More information

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix. MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix. Nullspace Let A = (a ij ) be an m n matrix. Definition. The nullspace of the matrix A, denoted N(A), is the set of all n-dimensional column

More information

Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix multiplication).

Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix multiplication). MAT 2 (Badger, Spring 202) LU Factorization Selected Notes September 2, 202 Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix

More information

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. Degrees of Sums in a Separable Field Extension Author(s): I. M. Isaacs Source: Proceedings of the American Mathematical Society, Vol. 25, No. 3 (Jul., 1970), pp. 638-641 Published by: American Mathematical

More information

1 Determinants and the Solvability of Linear Systems

1 Determinants and the Solvability of Linear Systems 1 Determinants and the Solvability of Linear Systems In the last section we learned how to use Gaussian elimination to solve linear systems of n equations in n unknowns The section completely side-stepped

More information

Lecture Notes 2: Matrices as Systems of Linear Equations

Lecture Notes 2: Matrices as Systems of Linear Equations 2: Matrices as Systems of Linear Equations 33A Linear Algebra, Puck Rombach Last updated: April 13, 2016 Systems of Linear Equations Systems of linear equations can represent many things You have probably

More information

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS KEITH CONRAD 1. Introduction The Fundamental Theorem of Algebra says every nonconstant polynomial with complex coefficients can be factored into linear

More information

Orthogonal Diagonalization of Symmetric Matrices

Orthogonal Diagonalization of Symmetric Matrices MATH10212 Linear Algebra Brief lecture notes 57 Gram Schmidt Process enables us to find an orthogonal basis of a subspace. Let u 1,..., u k be a basis of a subspace V of R n. We begin the process of finding

More information

Test1. Due Friday, March 13, 2015.

Test1. Due Friday, March 13, 2015. 1 Abstract Algebra Professor M. Zuker Test1. Due Friday, March 13, 2015. 1. Euclidean algorithm and related. (a) Suppose that a and b are two positive integers and that gcd(a, b) = d. Find all solutions

More information

FACTORING IN QUADRATIC FIELDS. 1. Introduction. This is called a quadratic field and it has degree 2 over Q. Similarly, set

FACTORING IN QUADRATIC FIELDS. 1. Introduction. This is called a quadratic field and it has degree 2 over Q. Similarly, set FACTORING IN QUADRATIC FIELDS KEITH CONRAD For a squarefree integer d other than 1, let 1. Introduction K = Q[ d] = {x + y d : x, y Q}. This is called a quadratic field and it has degree 2 over Q. Similarly,

More information

LINEAR ALGEBRA. September 23, 2010

LINEAR ALGEBRA. September 23, 2010 LINEAR ALGEBRA September 3, 00 Contents 0. LU-decomposition.................................... 0. Inverses and Transposes................................. 0.3 Column Spaces and NullSpaces.............................

More information

Inner product. Definition of inner product

Inner product. Definition of inner product Math 20F Linear Algebra Lecture 25 1 Inner product Review: Definition of inner product. Slide 1 Norm and distance. Orthogonal vectors. Orthogonal complement. Orthogonal basis. Definition of inner product

More information