curtis linear algebra

This paper turns from the public number---meets the mathematical---graphic mathematical---linear algebra partThank you for meeting the Math Working Group to explain the obscure and acting knowledgeable mathematical knowledge of university textbooks in an easy-to-understand and lively and interesting way.This time we mainly do a review, and then further the determinant of the geometric meaning of the animati

Mastering linear algebra in 10 days: astoundin Translator: mapleflying Recently, my friend Scott Young made an amazing feat: within a year, he completed all 33 of the legendary mit computer science curriculum, from Linear Algebra to Computational Theory. The most important thing is that he is self-taught. He watches

of the lectures Started by this teacher are very good, because our linear algebra textbooks are similar. They all start from the determinant, followed by the matrix followed by the vector, I didn't quite understand the arrangement of books before, but now I am wearing it all at once: Linear Algebra is used as a too

For professional software such as engineering and graphics, a large amount of mathematical computation is required, and linear algebra is the most used. So, on top of. net, especially on. NET 4.0 and vs2010, how can we complete Related Linear Algebra computing? I want to use the following methods: I. self-built and

1. Determinant1.1 Second-order determinant1.2 third-order determinant1.3 Number of reverse order1.4 N-Step determinant2. The nature of the determinantProperty 1 The determinant is equal to its transpose determinant.Property 2 swaps the determinant of two rows (columns), determinant.Property 3 The determinant of a row (column) in which all elements are multiplied by the same multiplier K, equals the number k multiplied by this determinant.Property 4 Determinant If there are two rows (column) elem

This chapter begins with an introduction to another basic concept in linear algebra-the determinant.In fact, like the Matrix, the determinant is also a tool for simplifying the expression polynomial, about the historical origin of the determinant, as the following introduction.In introducing the inverse matrix, we have mentioned that the second-order matrix has a corresponding determinant based on matrix A

algebra review, I'll be the using one index vectors. Most vector subscripts in the course start from 1.When talking on machine learning applications, sometimes explicitly say if we need to switch to, when we need to use The zero index vectors as well. Discussion of machine learning applications will be converted to subscript starting from 0.Finally, by Convention,use upper case to refer to matrices. So we ' re going-letters like a, B, c.and usually w

In this chapter we discuss the relationship between the vectors defined in the R^n space, which is generally orthogonal, then the orthogonal projection, the best approximation theorem, and so on, these concepts will lay the foundation for the optimal approximate solution of the ax=b of linear equations with no solution.Orthogonality:To give the simplest example, in a plane, if the two-dimensional vector's point multiplication is 0, then we can determi

3 31 2 4 % % The basis of zero space: the Code of this zero space is worth looking at, reflecting the basic idea of column meaning in linear algebra. You may not know what the code is. A simple statement may contain many operations. I like to give an example when I don't quite understand it. Let's take a look at the code in one sentence. Note that The column in the zero space indicates the

transformation.Matrix elimination Element Method:determinant TypeCalculation (0 descending order method)Other properties of the determinant:The law of ClydeMatrixFollow the law1. Linear Properties2. Operational and polynomial of n-order matricesElementary matrix and its role in multiplicationFor the unit matrix, the matrix obtained by making an elementary transformation becomes the elementary matrix.Together there are three primary transformations:Th

inner product can also be applied naturally to any dimension of space.With the concept of distance, of course we have to do something interesting with it. or from the concept of geometry, we can use distance to calculate the perimeter and area of a plane graph and the volume of the spatial graph. For example, we can calculate the coordinates of a point in a line or a plane that is closest to the nearest point on the straight or flat plane. The two applications in mathematics, the former produce

I. Five Representation Methods of Matrix Multiplication 1. General Form 2. Multiply the matrix and column vector 3. Multiply a matrix and a row vector 4. Multiply Matrix Blocks Ii. Matrix Inversion For phalanx, left inverse = Right Inverse Multiply the original matrix by its inverse matrix to obtain the Unit Matrix Several methods to determine whether it is reversible:1. the determinant is 02. the columns of the matrix are linear combinations of

in the European coordinate system are obtained after the X vector (also a coordinate point in the M coordinate) is left multiplied by m in the Custom space. Space coordinates are converted. If the implementation of the European coordinate transformation to the M coordinate system, can be on both sides of the same time left multiplied by a m of the inverse matrix M-1, (M-1) * m * x = (M-1) * B is X = (M-1) * B. After B is used, X can be obtained, and then the coordinate of point X in the M coord

??Recently in the implementation of the algorithm, the feeling of lack of knowledge of mathematics, here -------------------------------------------------- calculus --------------------------------------------- ------------- Calculus Open Class: mit: univariate calculus http://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/ Http://v.163.com/special/sp/singlevariablecalculus.html mit: Many variable Calculus Http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-c

- voidInsert (Char*R) { About intLen=strlen (s), p=RT; $ for(intI=0; i) - if(Ch[p][id (S[i])) -p=Ch[p][id (S[i]); - Else AP=ch[p][id (S[i])]=++CNT; +tag[p]=true; the } - $ voidBuild () { thequeueint>Q; the for(intI=0;i4; i++) the if(Ch[rt][i]) thefail[ch[rt][i]]=Rt,q.push (Ch[rt][i]); - Else inch[rt][i]=RT; the the while(!Q.empty ()) { About intx=Q.front (); Q.pop (); th

[find (1)]!=1){ thememset (Vis,0,sizeof(VIS)); num=0; the for(intI=2; i1; i++){94 if(Vis[find (i)])Continue; theVis[find (i)]=++num; the } thenum=0;98 for(intI=2; i1; i++) Aboutnum=num*Ten+Vis[find (i)]; -a.mat[id[num]][id[mem[t]]]+=1;101 }102 }103 }104 return; the }106 107 intMain () {108 #ifndef Online_judge109Freopen ("count.in","R", stdin); theFreopen ("Count.out","W", stdout);111 #endif thesc

] ofRec; V:Array[0..2002000] ofBoolean; N,m,i,j,k,l,st,ed,ww,top,tar,ans,x:longint;functionmin (aa,bb:longint): Longint;begin ifAa Thenexit (AA); exit (BB);End;procedureAdd (st,ed,ww:longint);beginInc (top); A[TOP].S:=St; A[TOP].E:=Ed; A[TOP].W:=ww; A[top].next:=B[st]; B[ST]:=top;End;functionBfs:boolean;varHead,tail,x,u:longint; Y:rec;beginFillchar (v,sizeof (v), false); Tail:=1; head:=0; d[st]:=1; V[ST]:=true; q[1]:=St; whileHead Do beginInc (head); x:=Q[head]; U:=B[x]; whileU>0 Do begi

1.The calculate the slope:the covariance of X and Y divided by the variance of X　　From NumPy import CoVslope_density = CoV (wine_quality["quality"],wine_quality["density"]) [0,1]/wine_quality["Density"].var () #cov ( X, y) is the function from NumPy, which returns a 2*2 Metric,.var () is Pandas function.2.To get the INTERCEPT:B = Y-ax (x and Y is the mean value of each column)Intercept_density = wine_quality["Quality"].mean ()-wine_quality["Density"].mean () * (Calc_slope (wine_quality[) Density

[Linear algebra] matrix addition 1 # Include 2 Using Namespace STD; 3 4 5 Int Main () 6 { 7 Int Matrixa [ 100 ] [ 100 ]; // Matrixa 8 Int Matrixb [ 100 ] [ 100 ]; // Matrixb 9 Int Plusresult [ 100 ] [ 100 ]; // Matrixa + matrixb = plusresult (this is a maxtrix) 10 Int M, N; 11 Cout " Enter the required and number of rows and columns in the matrix. " 12 Ci

1. Ways to get a column of another column with max/min values:A. Most_bars_country = flags["name"][flags["Bars"].idxmax ()]B. bars_sorted = flags.sort_values ("Bars", ascending=[0])Most_bars_country = bars_sorted["Name"].iloc[0]2. The probability of a certain value in a column:orange_probability = flags[flags["Orange"]==1].shape[0]/flags.shape[0]3. The calculate combination by using factorial:　　　Import Mathdef find_outcome_combinations (N, k): # Calculate The numerator of our formula.Numerator =