# 1. 抽象代數導論 (Introduction to Abstract Algebra) - 2. 張量分析 (Tensor Analysis) - 3. 正交函數展開 (Orthogonal Function Expansion) - 4. 格林函數 (Green's Function) - 5.

## Presentation on theme: "1. 抽象代數導論 (Introduction to Abstract Algebra) - 2. 張量分析 (Tensor Analysis) - 3. 正交函數展開 (Orthogonal Function Expansion) - 4. 格林函數 (Green's Function) - 5."— Presentation transcript:

1. 抽象代數導論 (Introduction to Abstract Algebra) - 2. 張量分析 (Tensor Analysis) - 3. 正交函數展開 (Orthogonal Function Expansion) - 4. 格林函數 (Green's Function) - 5. 變分法 (Calculus of Variation) - 6. 攝動理論 (Perturbation Theory) N968200 高等工程數學 ※ 先修課程：微積分﹑工程數學 ( 一 )-( 三 )

Reference : 1. 1.Birkhoff, G., MacLane, S., A Survey of Modern Algebra, 2nd ed, The Macmillan Co, New York, 1975. 2. 2. 徐誠浩, 抽象代數 - 方法導引, 復旦大學, 1989. 3. 3.Arangno, D. C., Schaum’s Outline of Theory and Problems of Abstract Algebra, McGraw-Hill Inc, 1999. 4. 4.Deskins, W. E., Abstract Algebra, The Macmillan Co, New York, 1964. 5. 5.O’Nan, M., Enderton, H., Linear Algebra, 3rd ed, Harcourt Brace Jovanovich Inc, 1990. 6. 6.Hoffman, K., Kunze, R., Linear Algebra, 2nd ed, The Southeast Book Co, New Jersey, 1971. 7. 7.McCoy, N. H., Fundamentals of Abstract Algebra, expanded version, Allyn & Bacon Inc, Boston, 1972. 8. 8.Hildebrand, F. B., Methods of Applied Mathematics, 2nd ed, Prentice-Hall Inc, New Jersey, 1972.. 9. 9.Burton, D. M., An Introduction to Abstract Mathematical Systems, Addison-Wesley, Massachusetts, 1965. 10. 10.Grossman, S. I., Derrick, W. R., Advanced Engineering Mathematics, Happer & Row, 1988. 11. 11.Hilbert, D., Courant, R., Methods of Mathematical Physics, vol(1), 狀元出版社, 台北市, 民國六十二年. 12. 12.Jeffrey, A., Advanced Engineering Mathematics, Harcourt, 2002. 13. 13.Arfken, G. B., Weber, H. J., Mathematical Methods for Physicists, 5th ed, Harcourt, 2001. 14. 14.Morse, F. B., Morse, F. H., Feshbach, H., Methods of Theoretical Physics, McGraw-Hill College, 1953

David Hilbert Born January 23, 1862 Wehlau, East Prussia Died February 14, 1943 Göttingen, Germany Residence Germany Nationality German Field Mathematician Erdős Number 4 Institution University of Königsberg and Göttingen University Alma Mater University of Königsberg Doctoral Advisor Ferdinand von Lindemann Doctoral Students Otto Blumenthal Richard Courant Richard Courant Max Dehn Max Dehn Erich Hecke Erich Hecke Hellmuth Kneser Hellmuth Kneser Robert König Robert König Erhard Schmidt Erhard Schmidt Hugo Steinhaus Hugo Steinhaus Emanuel Lasker Emanuel Lasker Hermann Weyl Hermann Weyl Ernst Zermelo Ernst Zermelo Known for Hilbert's basis theorem Hilbert's axioms Hilbert's problems Hilbert's program Einstein-Hilbert action Hilbert space Societies Foreign member of the Royal Society Spouse Käthe Jerosch (1864-1945, m. 1892) Children Franz Hilbert (1893-1969) Handedness Right handed The finiteness theorem Axiomatization of geometry The 23 Problems Formalism ~ from Wikipedia

Philip M. Morse “ Operations research is an applied science utilizing all known scientific techniques as tools in solving a specific problem.” Founding ORSA President (1952) B.S. Physics, 1926, Case Institute; Ph.D. Physics, 1929, Princeton University. Faculty member at MIT, 1931-1969. Methods of Operations Research Queues, Inventories, and Maintenance Library Effectiveness Quantum Mechanics Methods of Theoretical Physics Vibration and Sound Theoretical Acoustics Thermal Physics Handbook of Mathematical Functions, with Formulas, Graphs, and Mathematical Tables

Francis B. Hildebrand George Arfken

Introduction to Abstract Algebra 抽象代數導論 Preliminary notions Preliminary notions Systems with a single operation Systems with a single operation Mathematical systems with two operations Mathematical systems with two operations Matrix theory: an algebraic view Matrix theory: an algebraic view

Ex. Consider the operation defined on the set S= {1,2,3} by the operation table below. From the table, we see 2 (1 3)=2 3=2 but (2 1) 3=3 3=1 The associative law fails to hold in this groupoid(S, ) 2 1 3 * 1 2 3 1 3 23 2 1

A semigroup is a groupoid whose operation satisfies the associative law. (groupoid) 半群 Semigroup

Ex. If the operation is defined on by a b = max{ a, b },that is a b is the larger of the elements a and b, or either one if a=b. a (b c) = max{ a, b, c } = (a b) c that shows to be a semigroup If and is a semigroup, then proof.

A semigroup having an identity element for the operation is called a monoid. (groupoid) (semigroup) 單子 Monoid

Ex. Both the semigroups and are instances of monoids for each The empty set is the identity element for the union operation. for each The universal set is the identity element for the intersection operation.

If is a group and,then Proof. all we need to show is that from the uniqueness of the inverse of we would conclude a similar argument establishes that

Commutative 可交換性 Commutative groupoid Commutative semigroup Commutative monoid Commutative group

Ex. consider the set of number and the operation of ordinary multiplication, and Z represents integer. 1.Closure: 2.Associate property 3.Identity element 4.Commutative property is a commutative monoid.

Ring 環 A ring is a nonempty set with two binary operations and on such that 1. is a commutative group 2. is a semigroup 3.The two operations are related by the distributive laws

A ring consists of a nonempty set and two operations, called addition and multiplication and denoted by and, respectively, satisfying the requirements: 1.R is closed under addition 2.Commutative 3.Associative 4.Identity element 0 5.Inverse 6.R is closed under multiplication 7.Associate 8.Distributive law

Monoid Ring 單子環 A monoid ring is a ring with identity that is a semigroup with identity Ring Monoid ring

Ring with commutative property Commutative Commutative monoid Ring

Subring 子環 The triple is a subring of the ring 1. is a nonempty subset of 2. is a subgroup of 3. is closed under multiplication

The minimal set of conditions for determining subrings Let be ring and Then the triple is a subring of if and only if 1.Closed under differences 2.Closed under multiplication Ex. Let then is a subring of, since This shows that is closed under both differences and products.

Field 域 A field is a commutative monoid ring in which each nonzero element has an inverse under Definition of Field

Vector 向量 An n-component, or n-dimensional, vector is an n tuple of real numbers written either in a row or in a column. Row vector Column vector called the components of the vector n is the dimension of the vector

Vector space 向量空間 A vector space( or linear space) over the field F consists of the following: 1.A commutative group whose elements are called vectors.

2.A field whose elements are called scalars.

3.An operation 。 of scalar multiplication connecting the group and field which satisfies the properties V is closed under left multiplication by scalars

← Vector Space When m = n, we denote the particular vector space by M n (R # ) Ex:

Subspace 向量子空間 Let V(F) be a vector space over the field F W(F) is a subspace of V(F) The minimum conditions that W(F) must satisfy to be a subspace are:

If V(F) and V’(F) are vector spaces over the same field, then the mapping f : V → V’ is said to be operation-preserving if f preserves V(F) and V’(F) are algebraically equivalent whenever there exists a one-to-one operation-preserving function from V onto V’

Linear Transformations 線性轉換 Let V and W be vector spaces. A linear transformation from V into W is a function T from the set V into W with the following two properties: x T(x) T V W T is function from V to W,

Let V and W be vector spaces over the field F and let T be a linear transformation from V into W. The null space (kernal) of T is the set of all vectors x in V such that T(x) = 0 If V is finite-dimensional, the rank of T is the dimension of the range of T and the nullity of T is the dimension of the null space of T. 0 T ker T V W ran T

The Algebra of Linear Transformations Let T : U → V and S : V → W be linear transformations, with U, V, and W vector spaces. The composition of S and V

Representation of Linear Transformations by Matrices 線性轉換的矩陣表示 Let V be an n -dimensional vector space over the field F. T is a linear transformation, and α 1, α 2,…,α n are ordered bases for V. If 其中 稱 A 為 Linear Transformation T 在 α 1, α 2,…,α n 下的矩陣

Inner Product 向量內積

It follows from the Pythagorean theorem that the length of the vector a x z y 1a11a1 a2a2 a3a3 x y b a θ | b - a |

Inner Product Space 向量內積空間

Eigenvalues and Eigenvectors 特徵值與特徵向量

1 12 3 1 2 I + 2 j I + j 1 2 3 4 2 I + 4 j 3 I + 3 j T

Diagonalization 對角化 A square matrix is said to be a diagonal matrix if all of its entries are zero except those on the main diagonal: A linear operator T on a finite-dimensional vector space V is diagonalizable if there is a basis vector for V each vector of which is an eigenvector of T.

Orthogonalization of Vector Sets 向量的正交化 Gram-Schmit orthogonalization procedure

Quadratic Forms 二次形式 Equivalent A x = y

Canonical Form 標準形式 ↑Diagonal matrix If the eigenvalues and corresponding eigenvectors of the real symmetric matrix A are known, a matrix Q having this property can be easily constructed Let a matrix Q be constructed in such a way that the elements of the unit vectors e 1, e 2,….,e n are the elements of the successive columns of Q: eigenvalue eigenvector

Ex: Let T be the linear operator on R 3 which is represented in the standard ordered basis by the matrix A ↑Diagonal matrix ↑ Orthogonal matrix

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