diary off
help
HELP topics
matlab\general - General purpose commands.
matlab\ops - Operators and special characters.
matlab\lang - Programming language constructs.
matlab\elmat - Elementary matrices and matrix manipulation.
matlab\elfun - Elementary math functions.
matlab\specfun - Specialized math functions.
matlab\matfun - Matrix functions - numerical linear algebra.
matlab\datafun - Data analysis and Fourier transforms.
matlab\polyfun - Interpolation and polynomials.
matlab\funfun - Function functions and ODE solvers.
matlab\sparfun - Sparse matrices.
matlab\scribe - Annotation and Plot Editing.
matlab\graph2d - Two dimensional graphs.
matlab\graph3d - Three dimensional graphs.
matlab\specgraph - Specialized graphs.
matlab\graphics - Handle Graphics.
matlab\uitools - Graphical user interface tools.
matlab\strfun - Character strings.
matlab\imagesci - Image and scientific data input/output.
matlab\iofun - File input and output.
matlab\audiovideo - Audio and Video support.
matlab\timefun - Time and dates.
matlab\datatypes - Data types and structures.
matlab\verctrl - Version control.
matlab\codetools - Commands for creating and debugging code.
matlab\helptools - Help commands.
matlab\winfun - Windows Operating System Interface Files (COM/DDE)
matlab\demos - Examples and demonstrations.
matlab\timeseries - Time series data visualization and exploration.
matlab\hds - (No table of contents file)
toolbox\local - Preferences.
shared\controllib - Control Library
MATLAB704\work - (No table of contents file)
help help
HELP Display help text in Command Window.
HELP, by itself, lists all primary help topics. Each primary topic
corresponds to a directory name on the MATLABPATH.
HELP / lists a description of all operators and special characters.
HELP FUN displays a description of and syntax for the function FUN.
When FUN is in multiple directories on the MATLAB path, HELP displays
information about the first FUN found on the path and lists
PATHNAME/FUN for other (overloaded) FUNs.
HELP PATHNAME/FUN displays help for the function FUN in the PATHNAME
directory. Use this syntax to get help for overloaded functions.
HELP DIR displays a brief description of each function in the MATLAB
directory DIR. DIR can be a relative partial pathname (see HELP
PARTIALPATH). When there is also a function called DIR, help for both
the directory and the function are provided.
HELP CLASSNAME.METHODNAME displays help for the method METHODNAME of
the fully qualified class CLASSNAME. To determine CLASSNAME for
METHODNAME, use CLASS(OBJ), where METHODNAME is of the same class as
the object OBJ.
HELP CLASSNAME displays help for the fully qualified class CLASSNAME.
T = HELP('TOPIC') returns the help text for TOPIC as a string, with
each line separated by /n. TOPIC is any allowable argument for HELP.
REMARKS:
1. Use MORE ON before running HELP to pause HELP output after a
screenful of text displays.
2. In the help syntax, function names are capitalized to make them
stand out. In practice, always type function names in lowercase. For
Java functions that are shown with mixed case (for example,
javaObject) type the mixed case as shown.
3. Use DOC FUN to display help about the function in the Help
browser, which might provide additional information, such as graphics
and examples.
4. Use DOC HELP for information about creating help for your own
M-files.
5. Use HELPBROWSER to access online documentation in the Help
browser. Use the Help browser Index or Search tabs to find more
information about TOPIC or other terms.
EXAMPLES:
help close - displays help for the CLOSE function.
help database/close - displays help for the CLOSE function in the
Database Toolbox.
help database - lists all functions in the Database Toolbox and
displays help for the DATABASE function.
help general - lists all functions in the directory MATLAB/GENERAL.
help embedded.fi - displays help for the EMBEDDED.FI class in the
Fixed-Point Toolbox.
help embedded.fi.lsb displays help for the LSB method of the
EMBEDDED.FI class in the Fixed-Point Toolbox.
t = help('close') - gets help for the function CLOSE and stores it as
a string in t.
See also doc, docsearch, helpbrowser, helpwin, lookfor, matlabpath,
more, partialpath, which, whos, class.
Reference page in Help browser
doc help
help svd
SVD Singular value decomposition.
[U,S,V] = SVD(X) produces a diagonal matrix S, of the same
dimension as X and with nonnegative diagonal elements in
decreasing order, and unitary matrices U and V so that
X = U*S*V'.
S = SVD(X) returns a vector containing the singular values.
[U,S,V] = SVD(X,0) produces the "economy size"
decomposition. If X is m-by-n with m > n, then only the
first n columns of U are computed and S is n-by-n.
For m <= n, SVD(X,0) is equivalent to SVD(X).
[U,S,V] = SVD(X,'econ') also produces the "economy size"
decomposition. If X is m-by-n with m >= n, then it is
equivalent to SVD(X,0). For m < n, only the first m columns
of V are computed and S is m-by-m.
See also svds, gsvd.
Reference page in Help browser
doc svd
[U,S,V] = SVD([1 2; 3 4])
??? Undefined command/function 'SVD'.
ls
. .. oct5.txt
[U,S,V] = svd([1 2; 3 4])
U =
-0.4046 -0.9145
-0.9145 0.4046
S =
5.4650 0
0 0.3660
V =
-0.5760 0.8174
-0.8174 -0.5760
help help
HELP Display help text in Command Window.
HELP, by itself, lists all primary help topics. Each primary topic
corresponds to a directory name on the MATLABPATH.
HELP / lists a description of all operators and special characters.
HELP FUN displays a description of and syntax for the function FUN.
When FUN is in multiple directories on the MATLAB path, HELP displays
information about the first FUN found on the path and lists
PATHNAME/FUN for other (overloaded) FUNs.
HELP PATHNAME/FUN displays help for the function FUN in the PATHNAME
directory. Use this syntax to get help for overloaded functions.
HELP DIR displays a brief description of each function in the MATLAB
directory DIR. DIR can be a relative partial pathname (see HELP
PARTIALPATH). When there is also a function called DIR, help for both
the directory and the function are provided.
HELP CLASSNAME.METHODNAME displays help for the method METHODNAME of
the fully qualified class CLASSNAME. To determine CLASSNAME for
METHODNAME, use CLASS(OBJ), where METHODNAME is of the same class as
the object OBJ.
HELP CLASSNAME displays help for the fully qualified class CLASSNAME.
T = HELP('TOPIC') returns the help text for TOPIC as a string, with
each line separated by /n. TOPIC is any allowable argument for HELP.
REMARKS:
1. Use MORE ON before running HELP to pause HELP output after a
screenful of text displays.
2. In the help syntax, function names are capitalized to make them
stand out. In practice, always type function names in lowercase. For
Java functions that are shown with mixed case (for example,
javaObject) type the mixed case as shown.
3. Use DOC FUN to display help about the function in the Help
browser, which might provide additional information, such as graphics
and examples.
4. Use DOC HELP for information about creating help for your own
M-files.
5. Use HELPBROWSER to access online documentation in the Help
browser. Use the Help browser Index or Search tabs to find more
information about TOPIC or other terms.
EXAMPLES:
help close - displays help for the CLOSE function.
help database/close - displays help for the CLOSE function in the
Database Toolbox.
help database - lists all functions in the Database Toolbox and
displays help for the DATABASE function.
help general - lists all functions in the directory MATLAB/GENERAL.
help embedded.fi - displays help for the EMBEDDED.FI class in the
Fixed-Point Toolbox.
help embedded.fi.lsb displays help for the LSB method of the
EMBEDDED.FI class in the Fixed-Point Toolbox.
t = help('close') - gets help for the function CLOSE and stores it as
a string in t.
See also doc, docsearch, helpbrowser, helpwin, lookfor, matlabpath,
more, partialpath, which, whos, class.
Reference page in Help browser
doc help
lookfor svd
GSVD Generalized Singular Value Decompostion.
SVD Singular value decomposition.
SVDS Find a few singular values and vectors.
docsearch singular
docsearch('singular value decomposition')
r = [ 1 2 3]
r =
1 2 3
r = [ 1,2,3]
r =
1 2 3
c =[1;2;3]
c =
1
2
3
c =[4;5;6]
c =
4
5
6
c =[4;5;6];
c
c =
4
5
6
r*c
ans =
32
c*r
ans =
4 8 12
5 10 15
6 12 18
r*r
??? Error using ==> mtimes
Inner matrix dimensions must agree.
A = r*c
A =
32
A = c*r
A =
4 8 12
5 10 15
6 12 18
r*r
??? Error using ==> mtimes
Inner matrix dimensions must agree.
s = r.^2
s =
1 4 9
r
r =
1 2 3
exp(r)
ans =
2.7183 7.3891 20.0855
sin(r)
ans =
0.8415 0.9093 0.1411
exp(r)
ans =
2.7183 7.3891 20.0855
log(ans)
ans =
1 2 3
sqrt(r)
ans =
1.0000 1.4142 1.7321
format long
sqrt(r)
ans =
1.00000000000000 1.41421356237310 1.73205080756888
format
format short
sqrt(r)
ans =
1.0000 1.4142 1.7321
help format
FORMAT Set output format.
FORMAT with no inputs sets the output format to the default appropriate
for the class of the variable. For float variables, the default is
FORMAT SHORT.
FORMAT does not affect how MATLAB computations are done. Computations
on float variables, namely single or double, are done in appropriate
floating point precision, no matter how those variables are displayed.
Computations on integer variables are done natively in integer. Integer
variables are always displayed to the appropriate number of digits for
the class, for example, 3 digits to display the INT8 range -128:127.
FORMAT SHORT and LONG do not affect the display of integer variables.
FORMAT may be used to switch between different output display formats
of all float variables as follows:
FORMAT SHORT Scaled fixed point format with 5 digits.
FORMAT LONG Scaled fixed point format with 15 digits for double
and 7 digits for single.
FORMAT SHORT E Floating point format with 5 digits.
FORMAT LONG E Floating point format with 15 digits for double and
7 digits for single.
FORMAT SHORT G Best of fixed or floating point format with 5
digits.
FORMAT LONG G Best of fixed or floating point format with 15
digits for double and 7 digits for single.
FORMAT SHORT ENG Engineering format that has at least 5 digits
and a power that is a multiple of three
FORMAT LONG ENG Engineering format that has exactly 16 significant
digits and a power that is a multiple of three.
FORMAT may be used to switch between different output display formats
of all numeric variables as follows:
FORMAT HEX Hexadecimal format.
FORMAT + The symbols +, - and blank are printed
for positive, negative and zero elements.
Imaginary parts are ignored.
FORMAT BANK Fixed format for dollars and cents.
FORMAT RAT Approximation by ratio of small integers.
FORMAT may be used to affect the spacing in the display of all
variables as follows:
FORMAT COMPACT Suppresses extra line-feeds.
FORMAT LOOSE Puts the extra line-feeds back in.
Example:
format short, pi, single(pi)
displays both double and single pi with 5 digits as 3.1416 while
format long, pi, single(pi)
displays pi as 3.14159265358979 and single(pi) as 3.1415927.
format, intmax('uint64'), realmax
shows these values as 18446744073709551615 and 1.7977e+308 while
format hex, intmax('uint64'), realmax
shows them as ffffffffffffffff and 7fefffffffffffff respectively.
The HEX display corresponds to the internal representation of the value
and is not the same as the hexadecimal notation in the C programming
language.
See also disp, display, isnumeric, isfloat, isinteger.
Reference page in Help browser
doc format
2^-20
ans =
9.5367e-007
format short e
sqrt(r)
ans =
1.0000e+000 1.4142e+000 1.7321e+000
format
r
r =
1 2 3
sum(r)
ans =
6
mean(c)
ans =
5
A =c*r
A =
4 8 12
5 10 15
6 12 18
mean(A)
ans =
5 10 15
sum(A)
ans =
15 30 45
r = [3 8 1}
??? r = [3 8 1}
|
Error: Unbalanced or misused parentheses or brackets.
r = [3 8 1]
r =
3 8 1
sort(r)
ans =
1 3 8
pi
ans =
3.1416
format long
pi
ans =
3.14159265358979
tan(pi/6)
ans =
0.57735026918963
e
??? Undefined function or variable 'e'.
e = exp(1)
e =
2.71828182845905
sin(pi)
ans =
1.224646799147353e-016
tan(pi/2)
ans =
1.633123935319537e+016
inf
ans =
Inf
realmax
ans =
1.797693134862316e+308
9/inf
ans =
0
234 + inf
ans =
Inf
234 - inf
ans =
-Inf
inf + inf
ans =
Inf
inf - inf
ans =
NaN
0*inf
ans =
NaN
0*NaN
ans =
NaN
inf + inf
ans =
Inf
NaN + NaN
ans =
NaN
0/0
Warning: Divide by zero.
ans =
NaN
realmax
ans =
1.797693134862316e+308
2*realmax
ans =
Inf
realmax + 1
ans =
1.797693134862316e+308
1.00001*realmax
ans =
Inf
realmin
ans =
2.225073858507201e-308
eps
ans =
2.220446049250313e-016
A = [-3 0 1; 2 5 -7; -1 4 8]
A =
-3 0 1
2 5 -7
-1 4 8
b = [4, 5, 6]
b =
4 5 6
x = A\b
??? Error using ==> mldivide
Matrix dimensions must agree.
x = A/b
x =
-0.07792207792208
-0.11688311688312
0.83116883116883
A*x - b
??? Error using ==> minus
Matrix dimensions must agree.
b = b'
b =
4
5
6
x = A\b
x =
-1.37172774869110
1.38743455497382
-0.11518324607330
A*x - b
ans =
1.0e-015 *
0
-0.88817841970013
0
norm(A*x -b)
ans =
8.881784197001252e-016
norm([1, 2])
ans =
2.23606797749979
norm([1, 2],2)
ans =
2.23606797749979
norm([1, 2],1)
ans =
3
e = eig(A)
e =
-2.86009257161145
6.43004628580573 + 5.04336792200386i
6.43004628580573 - 5.04336792200386i
[V,D] = eig(A)
V =
Columns 1 through 2
0.98226538044422 0.03996828429398 + 0.04037676700870i
-0.12754903178851 -0.79223910300764
0.13742622337305 0.17326787933059 + 0.58232954466904i
Column 3
0.03996828429398 - 0.04037676700870i
-0.79223910300764
0.17326787933059 - 0.58232954466904i
D =
Columns 1 through 2
-2.86009257161145 0
0 6.43004628580573 + 5.04336792200386i
0 0
Column 3
0
0
6.43004628580573 - 5.04336792200386i
format
[V,D] = eig(A)
V =
0.9823 0.0400 + 0.0404i 0.0400 - 0.0404i
-0.1275 -0.7922 -0.7922
0.1374 0.1733 + 0.5823i 0.1733 - 0.5823i
D =
-2.8601 0 0
0 6.4300 + 5.0434i 0
0 0 6.4300 - 5.0434i
e = eig(A)
e =
-2.8601
6.4300 + 5.0434i
6.4300 - 5.0434i
help eig
EIG Eigenvalues and eigenvectors.
E = EIG(X) is a vector containing the eigenvalues of a square
matrix X.
[V,D] = EIG(X) produces a diagonal matrix D of eigenvalues and a
full matrix V whose columns are the corresponding eigenvectors so
that X*V = V*D.
[V,D] = EIG(X,'nobalance') performs the computation with balancing
disabled, which sometimes gives more accurate results for certain
problems with unusual scaling. If X is symmetric, EIG(X,'nobalance')
is ignored since X is already balanced.
E = EIG(A,B) is a vector containing the generalized eigenvalues
of square matrices A and B.
[V,D] = EIG(A,B) produces a diagonal matrix D of generalized
eigenvalues and a full matrix V whose columns are the
corresponding eigenvectors so that A*V = B*V*D.
EIG(A,B,'chol') is the same as EIG(A,B) for symmetric A and symmetric
positive definite B. It computes the generalized eigenvalues of A and B
using the Cholesky factorization of B.
EIG(A,B,'qz') ignores the symmetry of A and B and uses the QZ algorithm.
In general, the two algorithms return the same result, however using the
QZ algorithm may be more stable for certain problems.
The flag is ignored when A and B are not symmetric.
See also condeig, eigs, ordeig.
Reference page in Help browser
doc eig
[V,D] = eig(A)
V =
0.9823 0.0400 + 0.0404i 0.0400 - 0.0404i
-0.1275 -0.7922 -0.7922
0.1374 0.1733 + 0.5823i 0.1733 - 0.5823i
D =
-2.8601 0 0
0 6.4300 + 5.0434i 0
0 0 6.4300 - 5.0434i
norm(A*V - V*D)
ans =
4.2909e-015
v = 1:6
v =
1 2 3 4 5 6
v = -10:30
v =
Columns 1 through 12
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1
Columns 13 through 24
2 3 4 5 6 7 8 9 10 11 12 13
Columns 25 through 36
14 15 16 17 18 19 20 21 22 23 24 25
Columns 37 through 41
26 27 28 29 30
w = 2:3:10
w =
2 5 8
y = 1:-0.25:0
y =
1.0000 0.7500 0.5000 0.2500 0
w = linspace(2, 10, 4)
w =
2.0000 4.6667 7.3333 10.0000
x = 0:0.005:2*pi
x =
Columns 1 through 7
0 0.0050 0.0100 0.0150 0.0200 0.0250 0.0300
Columns 8 through 14
0.0350 0.0400 0.0450 0.0500 0.0550 0.0600 0.0650
Columns 15 through 21
0.0700 0.0750 0.0800 0.0850 0.0900 0.0950 0.1000
Columns 22 through 28
0.1050 0.1100 0.1150 0.1200 0.1250 0.1300 0.1350
Columns 29 through 35
0.1400 0.1450 0.1500 0.1550 0.1600 0.1650 0.1700
Columns 36 through 42
0.1750 0.1800 0.1850 0.1900 0.1950 0.2000 0.2050
Columns 43 through 49
0.2100 0.2150 0.2200 0.2250 0.2300 0.2350 0.2400
Columns 50 through 56
0.2450 0.2500 0.2550 0.2600 0.2650 0.2700 0.2750
Columns 57 through 63
0.2800 0.2850 0.2900 0.2950 0.3000 0.3050 0.3100
Columns 64 through 70
0.3150 0.3200 0.3250 0.3300 0.3350 0.3400 0.3450
Columns 71 through 77
0.3500 0.3550 0.3600 0.3650 0.3700 0.3750 0.3800
Columns 78 through 84
0.3850 0.3900 0.3950 0.4000 0.4050 0.4100 0.4150
Columns 85 through 91
0.4200 0.4250 0.4300 0.4350 0.4400 0.4450 0.4500
Columns 92 through 98
0.4550 0.4600 0.4650 0.4700 0.4750 0.4800 0.4850
Columns 99 through 105
0.4900 0.4950 0.5000 0.5050 0.5100 0.5150 0.5200
Columns 106 through 112
0.5250 0.5300 0.5350 0.5400 0.5450 0.5500 0.5550
Columns 113 through 119
0.5600 0.5650 0.5700 0.5750 0.5800 0.5850 0.5900
Columns 120 through 126
0.5950 0.6000 0.6050 0.6100 0.6150 0.6200 0.6250
Columns 127 through 133
0.6300 0.6350 0.6400 0.6450 0.6500 0.6550 0.6600
Columns 134 through 140
0.6650 0.6700 0.6750 0.6800 0.6850 0.6900 0.6950
Columns 141 through 147
0.7000 0.7050 0.7100 0.7150 0.7200 0.7250 0.7300
Columns 148 through 154
0.7350 0.7400 0.7450 0.7500 0.7550 0.7600 0.7650
Columns 155 through 161
0.7700 0.7750 0.7800 0.7850 0.7900 0.7950 0.8000
Columns 162 through 168
0.8050 0.8100 0.8150 0.8200 0.8250 0.8300 0.8350
Columns 169 through 175
0.8400 0.8450 0.8500 0.8550 0.8600 0.8650 0.8700
Columns 176 through 182
0.8750 0.8800 0.8850 0.8900 0.8950 0.9000 0.9050
Columns 183 through 189
0.9100 0.9150 0.9200 0.9250 0.9300 0.9350 0.9400
Columns 190 through 196
0.9450 0.9500 0.9550 0.9600 0.9650 0.9700 0.9750
Columns 197 through 203
0.9800 0.9850 0.9900 0.9950 1.0000 1.0050 1.0100
Columns 204 through 210
1.0150 1.0200 1.0250 1.0300 1.0350 1.0400 1.0450
Columns 211 through 217
1.0500 1.0550 1.0600 1.0650 1.0700 1.0750 1.0800
Columns 218 through 224
1.0850 1.0900 1.0950 1.1000 1.1050 1.1100 1.1150
Columns 225 through 231
1.1200 1.1250 1.1300 1.1350 1.1400 1.1450 1.1500
Columns 232 through 238
1.1550 1.1600 1.1650 1.1700 1.1750 1.1800 1.1850
Columns 239 through 245
1.1900 1.1950 1.2000 1.2050 1.2100 1.2150 1.2200
Columns 246 through 252
1.2250 1.2300 1.2350 1.2400 1.2450 1.2500 1.2550
Columns 253 through 259
1.2600 1.2650 1.2700 1.2750 1.2800 1.2850 1.2900
Columns 260 through 266
1.2950 1.3000 1.3050 1.3100 1.3150 1.3200 1.3250
Columns 267 through 273
1.3300 1.3350 1.3400 1.3450 1.3500 1.3550 1.3600
Columns 274 through 280
1.3650 1.3700 1.3750 1.3800 1.3850 1.3900 1.3950
Columns 281 through 287
1.4000 1.4050 1.4100 1.4150 1.4200 1.4250 1.4300
Columns 288 through 294
1.4350 1.4400 1.4450 1.4500 1.4550 1.4600 1.4650
Columns 295 through 301
1.4700 1.4750 1.4800 1.4850 1.4900 1.4950 1.5000
Columns 302 through 308
1.5050 1.5100 1.5150 1.5200 1.5250 1.5300 1.5350
Columns 309 through 315
1.5400 1.5450 1.5500 1.5550 1.5600 1.5650 1.5700
Columns 316 through 322
1.5750 1.5800 1.5850 1.5900 1.5950 1.6000 1.6050
Columns 323 through 329
1.6100 1.6150 1.6200 1.6250 1.6300 1.6350 1.6400
Columns 330 through 336
1.6450 1.6500 1.6550 1.6600 1.6650 1.6700 1.6750
Columns 337 through 343
1.6800 1.6850 1.6900 1.6950 1.7000 1.7050 1.7100
Columns 344 through 350
1.7150 1.7200 1.7250 1.7300 1.7350 1.7400 1.7450
Columns 351 through 357
1.7500 1.7550 1.7600 1.7650 1.7700 1.7750 1.7800
Columns 358 through 364
1.7850 1.7900 1.7950 1.8000 1.8050 1.8100 1.8150
Columns 365 through 371
1.8200 1.8250 1.8300 1.8350 1.8400 1.8450 1.8500
Columns 372 through 378
1.8550 1.8600 1.8650 1.8700 1.8750 1.8800 1.8850
Columns 379 through 385
1.8900 1.8950 1.9000 1.9050 1.9100 1.9150 1.9200
Columns 386 through 392
1.9250 1.9300 1.9350 1.9400 1.9450 1.9500 1.9550
Columns 393 through 399
1.9600 1.9650 1.9700 1.9750 1.9800 1.9850 1.9900
Columns 400 through 406
1.9950 2.0000 2.0050 2.0100 2.0150 2.0200 2.0250
Columns 407 through 413
2.0300 2.0350 2.0400 2.0450 2.0500 2.0550 2.0600
Columns 414 through 420
2.0650 2.0700 2.0750 2.0800 2.0850 2.0900 2.0950
Columns 421 through 427
2.1000 2.1050 2.1100 2.1150 2.1200 2.1250 2.1300
Columns 428 through 434
2.1350 2.1400 2.1450 2.1500 2.1550 2.1600 2.1650
Columns 435 through 441
2.1700 2.1750 2.1800 2.1850 2.1900 2.1950 2.2000
Columns 442 through 448
2.2050 2.2100 2.2150 2.2200 2.2250 2.2300 2.2350
Columns 449 through 455
2.2400 2.2450 2.2500 2.2550 2.2600 2.2650 2.2700
Columns 456 through 462
2.2750 2.2800 2.2850 2.2900 2.2950 2.3000 2.3050
Columns 463 through 469
2.3100 2.3150 2.3200 2.3250 2.3300 2.3350 2.3400
Columns 470 through 476
2.3450 2.3500 2.3550 2.3600 2.3650 2.3700 2.3750
Columns 477 through 483
2.3800 2.3850 2.3900 2.3950 2.4000 2.4050 2.4100
Columns 484 through 490
2.4150 2.4200 2.4250 2.4300 2.4350 2.4400 2.4450
Columns 491 through 497
2.4500 2.4550 2.4600 2.4650 2.4700 2.4750 2.4800
Columns 498 through 504
2.4850 2.4900 2.4950 2.5000 2.5050 2.5100 2.5150
Columns 505 through 511
2.5200 2.5250 2.5300 2.5350 2.5400 2.5450 2.5500
Columns 512 through 518
2.5550 2.5600 2.5650 2.5700 2.5750 2.5800 2.5850
Columns 519 through 525
2.5900 2.5950 2.6000 2.6050 2.6100 2.6150 2.6200
Columns 526 through 532
2.6250 2.6300 2.6350 2.6400 2.6450 2.6500 2.6550
Columns 533 through 539
2.6600 2.6650 2.6700 2.6750 2.6800 2.6850 2.6900
Columns 540 through 546
2.6950 2.7000 2.7050 2.7100 2.7150 2.7200 2.7250
Columns 547 through 553
2.7300 2.7350 2.7400 2.7450 2.7500 2.7550 2.7600
Columns 554 through 560
2.7650 2.7700 2.7750 2.7800 2.7850 2.7900 2.7950
Columns 561 through 567
2.8000 2.8050 2.8100 2.8150 2.8200 2.8250 2.8300
Columns 568 through 574
2.8350 2.8400 2.8450 2.8500 2.8550 2.8600 2.8650
Columns 575 through 581
2.8700 2.8750 2.8800 2.8850 2.8900 2.8950 2.9000
Columns 582 through 588
2.9050 2.9100 2.9150 2.9200 2.9250 2.9300 2.9350
Columns 589 through 595
2.9400 2.9450 2.9500 2.9550 2.9600 2.9650 2.9700
Columns 596 through 602
2.9750 2.9800 2.9850 2.9900 2.9950 3.0000 3.0050
Columns 603 through 609
3.0100 3.0150 3.0200 3.0250 3.0300 3.0350 3.0400
Columns 610 through 616
3.0450 3.0500 3.0550 3.0600 3.0650 3.0700 3.0750
Columns 617 through 623
3.0800 3.0850 3.0900 3.0950 3.1000 3.1050 3.1100
Columns 624 through 630
3.1150 3.1200 3.1250 3.1300 3.1350 3.1400 3.1450
Columns 631 through 637
3.1500 3.1550 3.1600 3.1650 3.1700 3.1750 3.1800
Columns 638 through 644
3.1850 3.1900 3.1950 3.2000 3.2050 3.2100 3.2150
Columns 645 through 651
3.2200 3.2250 3.2300 3.2350 3.2400 3.2450 3.2500
Columns 652 through 658
3.2550 3.2600 3.2650 3.2700 3.2750 3.2800 3.2850
Columns 659 through 665
3.2900 3.2950 3.3000 3.3050 3.3100 3.3150 3.3200
Columns 666 through 672
3.3250 3.3300 3.3350 3.3400 3.3450 3.3500 3.3550
Columns 673 through 679
3.3600 3.3650 3.3700 3.3750 3.3800 3.3850 3.3900
Columns 680 through 686
3.3950 3.4000 3.4050 3.4100 3.4150 3.4200 3.4250
Columns 687 through 693
3.4300 3.4350 3.4400 3.4450 3.4500 3.4550 3.4600
Columns 694 through 700
3.4650 3.4700 3.4750 3.4800 3.4850 3.4900 3.4950
Columns 701 through 707
3.5000 3.5050 3.5100 3.5150 3.5200 3.5250 3.5300
Columns 708 through 714
3.5350 3.5400 3.5450 3.5500 3.5550 3.5600 3.5650
Columns 715 through 721
3.5700 3.5750 3.5800 3.5850 3.5900 3.5950 3.6000
Columns 722 through 728
3.6050 3.6100 3.6150 3.6200 3.6250 3.6300 3.6350
Columns 729 through 735
3.6400 3.6450 3.6500 3.6550 3.6600 3.6650 3.6700
Columns 736 through 742
3.6750 3.6800 3.6850 3.6900 3.6950 3.7000 3.7050
Columns 743 through 749
3.7100 3.7150 3.7200 3.7250 3.7300 3.7350 3.7400
Columns 750 through 756
3.7450 3.7500 3.7550 3.7600 3.7650 3.7700 3.7750
Columns 757 through 763
3.7800 3.7850 3.7900 3.7950 3.8000 3.8050 3.8100
Columns 764 through 770
3.8150 3.8200 3.8250 3.8300 3.8350 3.8400 3.8450
Columns 771 through 777
3.8500 3.8550 3.8600 3.8650 3.8700 3.8750 3.8800
Columns 778 through 784
3.8850 3.8900 3.8950 3.9000 3.9050 3.9100 3.9150
Columns 785 through 791
3.9200 3.9250 3.9300 3.9350 3.9400 3.9450 3.9500
Columns 792 through 798
3.9550 3.9600 3.9650 3.9700 3.9750 3.9800 3.9850
Columns 799 through 805
3.9900 3.9950 4.0000 4.0050 4.0100 4.0150 4.0200
Columns 806 through 812
4.0250 4.0300 4.0350 4.0400 4.0450 4.0500 4.0550
Columns 813 through 819
4.0600 4.0650 4.0700 4.0750 4.0800 4.0850 4.0900
Columns 820 through 826
4.0950 4.1000 4.1050 4.1100 4.1150 4.1200 4.1250
Columns 827 through 833
4.1300 4.1350 4.1400 4.1450 4.1500 4.1550 4.1600
Columns 834 through 840
4.1650 4.1700 4.1750 4.1800 4.1850 4.1900 4.1950
Columns 841 through 847
4.2000 4.2050 4.2100 4.2150 4.2200 4.2250 4.2300
Columns 848 through 854
4.2350 4.2400 4.2450 4.2500 4.2550 4.2600 4.2650
Columns 855 through 861
4.2700 4.2750 4.2800 4.2850 4.2900 4.2950 4.3000
Columns 862 through 868
4.3050 4.3100 4.3150 4.3200 4.3250 4.3300 4.3350
Columns 869 through 875
4.3400 4.3450 4.3500 4.3550 4.3600 4.3650 4.3700
Columns 876 through 882
4.3750 4.3800 4.3850 4.3900 4.3950 4.4000 4.4050
Columns 883 through 889
4.4100 4.4150 4.4200 4.4250 4.4300 4.4350 4.4400
Columns 890 through 896
4.4450 4.4500 4.4550 4.4600 4.4650 4.4700 4.4750
Columns 897 through 903
4.4800 4.4850 4.4900 4.4950 4.5000 4.5050 4.5100
Columns 904 through 910
4.5150 4.5200 4.5250 4.5300 4.5350 4.5400 4.5450
Columns 911 through 917
4.5500 4.5550 4.5600 4.5650 4.5700 4.5750 4.5800
Columns 918 through 924
4.5850 4.5900 4.5950 4.6000 4.6050 4.6100 4.6150
Columns 925 through 931
4.6200 4.6250 4.6300 4.6350 4.6400 4.6450 4.6500
Columns 932 through 938
4.6550 4.6600 4.6650 4.6700 4.6750 4.6800 4.6850
Columns 939 through 945
4.6900 4.6950 4.7000 4.7050 4.7100 4.7150 4.7200
Columns 946 through 952
4.7250 4.7300 4.7350 4.7400 4.7450 4.7500 4.7550
Columns 953 through 959
4.7600 4.7650 4.7700 4.7750 4.7800 4.7850 4.7900
Columns 960 through 966
4.7950 4.8000 4.8050 4.8100 4.8150 4.8200 4.8250
Columns 967 through 973
4.8300 4.8350 4.8400 4.8450 4.8500 4.8550 4.8600
Columns 974 through 980
4.8650 4.8700 4.8750 4.8800 4.8850 4.8900 4.8950
Columns 981 through 987
4.9000 4.9050 4.9100 4.9150 4.9200 4.9250 4.9300
Columns 988 through 994
4.9350 4.9400 4.9450 4.9500 4.9550 4.9600 4.9650
Columns 995 through 1001
4.9700 4.9750 4.9800 4.9850 4.9900 4.9950 5.0000
Columns 1002 through 1008
5.0050 5.0100 5.0150 5.0200 5.0250 5.0300 5.0350
Columns 1009 through 1015
5.0400 5.0450 5.0500 5.0550 5.0600 5.0650 5.0700
Columns 1016 through 1022
5.0750 5.0800 5.0850 5.0900 5.0950 5.1000 5.1050
Columns 1023 through 1029
5.1100 5.1150 5.1200 5.1250 5.1300 5.1350 5.1400
Columns 1030 through 1036
5.1450 5.1500 5.1550 5.1600 5.1650 5.1700 5.1750
Columns 1037 through 1043
5.1800 5.1850 5.1900 5.1950 5.2000 5.2050 5.2100
Columns 1044 through 1050
5.2150 5.2200 5.2250 5.2300 5.2350 5.2400 5.2450
Columns 1051 through 1057
5.2500 5.2550 5.2600 5.2650 5.2700 5.2750 5.2800
Columns 1058 through 1064
5.2850 5.2900 5.2950 5.3000 5.3050 5.3100 5.3150
Columns 1065 through 1071
5.3200 5.3250 5.3300 5.3350 5.3400 5.3450 5.3500
Columns 1072 through 1078
5.3550 5.3600 5.3650 5.3700 5.3750 5.3800 5.3850
Columns 1079 through 1085
5.3900 5.3950 5.4000 5.4050 5.4100 5.4150 5.4200
Columns 1086 through 1092
5.4250 5.4300 5.4350 5.4400 5.4450 5.4500 5.4550
Columns 1093 through 1099
5.4600 5.4650 5.4700 5.4750 5.4800 5.4850 5.4900
Columns 1100 through 1106
5.4950 5.5000 5.5050 5.5100 5.5150 5.5200 5.5250
Columns 1107 through 1113
5.5300 5.5350 5.5400 5.5450 5.5500 5.5550 5.5600
Columns 1114 through 1120
5.5650 5.5700 5.5750 5.5800 5.5850 5.5900 5.5950
Columns 1121 through 1127
5.6000 5.6050 5.6100 5.6150 5.6200 5.6250 5.6300
Columns 1128 through 1134
5.6350 5.6400 5.6450 5.6500 5.6550 5.6600 5.6650
Columns 1135 through 1141
5.6700 5.6750 5.6800 5.6850 5.6900 5.6950 5.7000
Columns 1142 through 1148
5.7050 5.7100 5.7150 5.7200 5.7250 5.7300 5.7350
Columns 1149 through 1155
5.7400 5.7450 5.7500 5.7550 5.7600 5.7650 5.7700
Columns 1156 through 1162
5.7750 5.7800 5.7850 5.7900 5.7950 5.8000 5.8050
Columns 1163 through 1169
5.8100 5.8150 5.8200 5.8250 5.8300 5.8350 5.8400
Columns 1170 through 1176
5.8450 5.8500 5.8550 5.8600 5.8650 5.8700 5.8750
Columns 1177 through 1183
5.8800 5.8850 5.8900 5.8950 5.9000 5.9050 5.9100
Columns 1184 through 1190
5.9150 5.9200 5.9250 5.9300 5.9350 5.9400 5.9450
Columns 1191 through 1197
5.9500 5.9550 5.9600 5.9650 5.9700 5.9750 5.9800
Columns 1198 through 1204
5.9850 5.9900 5.9950 6.0000 6.0050 6.0100 6.0150
Columns 1205 through 1211
6.0200 6.0250 6.0300 6.0350 6.0400 6.0450 6.0500
Columns 1212 through 1218
6.0550 6.0600 6.0650 6.0700 6.0750 6.0800 6.0850
Columns 1219 through 1225
6.0900 6.0950 6.1000 6.1050 6.1100 6.1150 6.1200
Columns 1226 through 1232
6.1250 6.1300 6.1350 6.1400 6.1450 6.1500 6.1550
Columns 1233 through 1239
6.1600 6.1650 6.1700 6.1750 6.1800 6.1850 6.1900
Columns 1240 through 1246
6.1950 6.2000 6.2050 6.2100 6.2150 6.2200 6.2250
Columns 1247 through 1253
6.2300 6.2350 6.2400 6.2450 6.2500 6.2550 6.2600
Columns 1254 through 1257
6.2650 6.2700 6.2750 6.2800
x = 0:0.005:2*pi;
clear
x = 0:0.005:2*pi;
who
Your variables are:
x
whos
Name Size Bytes Class
x 1x1257 10056 double array
Grand total is 1257 elements using 10056 bytes
clear
whos
x = 0:0.005:2*pi;
y =sin(x);
plot(x,y)
xlabel('x value')
title('y = sin(x)')
hist(y)
x=0:.1:pi; y=0:.1:pi;
[X,Y] = meshgrid(x,y)
X =
Columns 1 through 7
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
0 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000
Columns 8 through 14
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
0.7000 0.8000 0.9000 1.0000 1.1000 1.2000 1.3000
Columns 15 through 21
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
Columns 22 through 28
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000
Columns 29 through 32
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
2.8000 2.9000 3.0000 3.1000
Y =
Columns 1 through 7
0 0 0 0 0 0 0
0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000
0.2000 0.2000 0.2000 0.2000 0.2000 0.2000 0.2000
0.3000 0.3000 0.3000 0.3000 0.3000 0.3000 0.3000
0.4000 0.4000 0.4000 0.4000 0.4000 0.4000 0.4000
0.5000 0.5000 0.5000 0.5000 0.5000 0.5000 0.5000
0.6000 0.6000 0.6000 0.6000 0.6000 0.6000 0.6000
0.7000 0.7000 0.7000 0.7000 0.7000 0.7000 0.7000
0.8000 0.8000 0.8000 0.8000 0.8000 0.8000 0.8000
0.9000 0.9000 0.9000 0.9000 0.9000 0.9000 0.9000
1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
1.1000 1.1000 1.1000 1.1000 1.1000 1.1000 1.1000
1.2000 1.2000 1.2000 1.2000 1.2000 1.2000 1.2000
1.3000 1.3000 1.3000 1.3000 1.3000 1.3000 1.3000
1.4000 1.4000 1.4000 1.4000 1.4000 1.4000 1.4000
1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000
1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000
1.7000 1.7000 1.7000 1.7000 1.7000 1.7000 1.7000
1.8000 1.8000 1.8000 1.8000 1.8000 1.8000 1.8000
1.9000 1.9000 1.9000 1.9000 1.9000 1.9000 1.9000
2.0000 2.0000 2.0000 2.0000 2.0000 2.0000 2.0000
2.1000 2.1000 2.1000 2.1000 2.1000 2.1000 2.1000
2.2000 2.2000 2.2000 2.2000 2.2000 2.2000 2.2000
2.3000 2.3000 2.3000 2.3000 2.3000 2.3000 2.3000
2.4000 2.4000 2.4000 2.4000 2.4000 2.4000 2.4000
2.5000 2.5000 2.5000 2.5000 2.5000 2.5000 2.5000
2.6000 2.6000 2.6000 2.6000 2.6000 2.6000 2.6000
2.7000 2.7000 2.7000 2.7000 2.7000 2.7000 2.7000
2.8000 2.8000 2.8000 2.8000 2.8000 2.8000 2.8000
2.9000 2.9000 2.9000 2.9000 2.9000 2.9000 2.9000
3.0000 3.0000 3.0000 3.0000 3.0000 3.0000 3.0000
3.1000 3.1000 3.1000 3.1000 3.1000 3.1000 3.1000
Columns 8 through 14
0 0 0 0 0 0 0
0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000
0.2000 0.2000 0.2000 0.2000 0.2000 0.2000 0.2000
0.3000 0.3000 0.3000 0.3000 0.3000 0.3000 0.3000
0.4000 0.4000 0.4000 0.4000 0.4000 0.4000 0.4000
0.5000 0.5000 0.5000 0.5000 0.5000 0.5000 0.5000
0.6000 0.6000 0.6000 0.6000 0.6000 0.6000 0.6000
0.7000 0.7000 0.7000 0.7000 0.7000 0.7000 0.7000
0.8000 0.8000 0.8000 0.8000 0.8000 0.8000 0.8000
0.9000 0.9000 0.9000 0.9000 0.9000 0.9000 0.9000
1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
1.1000 1.1000 1.1000 1.1000 1.1000 1.1000 1.1000
1.2000 1.2000 1.2000 1.2000 1.2000 1.2000 1.2000
1.3000 1.3000 1.3000 1.3000 1.3000 1.3000 1.3000
1.4000 1.4000 1.4000 1.4000 1.4000 1.4000 1.4000
1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000
1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000
1.7000 1.7000 1.7000 1.7000 1.7000 1.7000 1.7000
1.8000 1.8000 1.8000 1.8000 1.8000 1.8000 1.8000
1.9000 1.9000 1.9000 1.9000 1.9000 1.9000 1.9000
2.0000 2.0000 2.0000 2.0000 2.0000 2.0000 2.0000
2.1000 2.1000 2.1000 2.1000 2.1000 2.1000 2.1000
2.2000 2.2000 2.2000 2.2000 2.2000 2.2000 2.2000
2.3000 2.3000 2.3000 2.3000 2.3000 2.3000 2.3000
2.4000 2.4000 2.4000 2.4000 2.4000 2.4000 2.4000
2.5000 2.5000 2.5000 2.5000 2.5000 2.5000 2.5000
2.6000 2.6000 2.6000 2.6000 2.6000 2.6000 2.6000
2.7000 2.7000 2.7000 2.7000 2.7000 2.7000 2.7000
2.8000 2.8000 2.8000 2.8000 2.8000 2.8000 2.8000
2.9000 2.9000 2.9000 2.9000 2.9000 2.9000 2.9000
3.0000 3.0000 3.0000 3.0000 3.0000 3.0000 3.0000
3.1000 3.1000 3.1000 3.1000 3.1000 3.1000 3.1000
Columns 15 through 21
0 0 0 0 0 0 0
0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000
0.2000 0.2000 0.2000 0.2000 0.2000 0.2000 0.2000
0.3000 0.3000 0.3000 0.3000 0.3000 0.3000 0.3000
0.4000 0.4000 0.4000 0.4000 0.4000 0.4000 0.4000
0.5000 0.5000 0.5000 0.5000 0.5000 0.5000 0.5000
0.6000 0.6000 0.6000 0.6000 0.6000 0.6000 0.6000
0.7000 0.7000 0.7000 0.7000 0.7000 0.7000 0.7000
0.8000 0.8000 0.8000 0.8000 0.8000 0.8000 0.8000
0.9000 0.9000 0.9000 0.9000 0.9000 0.9000 0.9000
1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
1.1000 1.1000 1.1000 1.1000 1.1000 1.1000 1.1000
1.2000 1.2000 1.2000 1.2000 1.2000 1.2000 1.2000
1.3000 1.3000 1.3000 1.3000 1.3000 1.3000 1.3000
1.4000 1.4000 1.4000 1.4000 1.4000 1.4000 1.4000
1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000
1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000
1.7000 1.7000 1.7000 1.7000 1.7000 1.7000 1.7000
1.8000 1.8000 1.8000 1.8000 1.8000 1.8000 1.8000
1.9000 1.9000 1.9000 1.9000 1.9000 1.9000 1.9000
2.0000 2.0000 2.0000 2.0000 2.0000 2.0000 2.0000
2.1000 2.1000 2.1000 2.1000 2.1000 2.1000 2.1000
2.2000 2.2000 2.2000 2.2000 2.2000 2.2000 2.2000
2.3000 2.3000 2.3000 2.3000 2.3000 2.3000 2.3000
2.4000 2.4000 2.4000 2.4000 2.4000 2.4000 2.4000
2.5000 2.5000 2.5000 2.5000 2.5000 2.5000 2.5000
2.6000 2.6000 2.6000 2.6000 2.6000 2.6000 2.6000
2.7000 2.7000 2.7000 2.7000 2.7000 2.7000 2.7000
2.8000 2.8000 2.8000 2.8000 2.8000 2.8000 2.8000
2.9000 2.9000 2.9000 2.9000 2.9000 2.9000 2.9000
3.0000 3.0000 3.0000 3.0000 3.0000 3.0000 3.0000
3.1000 3.1000 3.1000 3.1000 3.1000 3.1000 3.1000
Columns 22 through 28
0 0 0 0 0 0 0
0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000
0.2000 0.2000 0.2000 0.2000 0.2000 0.2000 0.2000
0.3000 0.3000 0.3000 0.3000 0.3000 0.3000 0.3000
0.4000 0.4000 0.4000 0.4000 0.4000 0.4000 0.4000
0.5000 0.5000 0.5000 0.5000 0.5000 0.5000 0.5000
0.6000 0.6000 0.6000 0.6000 0.6000 0.6000 0.6000
0.7000 0.7000 0.7000 0.7000 0.7000 0.7000 0.7000
0.8000 0.8000 0.8000 0.8000 0.8000 0.8000 0.8000
0.9000 0.9000 0.9000 0.9000 0.9000 0.9000 0.9000
1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
1.1000 1.1000 1.1000 1.1000 1.1000 1.1000 1.1000
1.2000 1.2000 1.2000 1.2000 1.2000 1.2000 1.2000
1.3000 1.3000 1.3000 1.3000 1.3000 1.3000 1.3000
1.4000 1.4000 1.4000 1.4000 1.4000 1.4000 1.4000
1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000
1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000
1.7000 1.7000 1.7000 1.7000 1.7000 1.7000 1.7000
1.8000 1.8000 1.8000 1.8000 1.8000 1.8000 1.8000
1.9000 1.9000 1.9000 1.9000 1.9000 1.9000 1.9000
2.0000 2.0000 2.0000 2.0000 2.0000 2.0000 2.0000
2.1000 2.1000 2.1000 2.1000 2.1000 2.1000 2.1000
2.2000 2.2000 2.2000 2.2000 2.2000 2.2000 2.2000
2.3000 2.3000 2.3000 2.3000 2.3000 2.3000 2.3000
2.4000 2.4000 2.4000 2.4000 2.4000 2.4000 2.4000
2.5000 2.5000 2.5000 2.5000 2.5000 2.5000 2.5000
2.6000 2.6000 2.6000 2.6000 2.6000 2.6000 2.6000
2.7000 2.7000 2.7000 2.7000 2.7000 2.7000 2.7000
2.8000 2.8000 2.8000 2.8000 2.8000 2.8000 2.8000
2.9000 2.9000 2.9000 2.9000 2.9000 2.9000 2.9000
3.0000 3.0000 3.0000 3.0000 3.0000 3.0000 3.0000
3.1000 3.1000 3.1000 3.1000 3.1000 3.1000 3.1000
Columns 29 through 32
0 0 0 0
0.1000 0.1000 0.1000 0.1000
0.2000 0.2000 0.2000 0.2000
0.3000 0.3000 0.3000 0.3000
0.4000 0.4000 0.4000 0.4000
0.5000 0.5000 0.5000 0.5000
0.6000 0.6000 0.6000 0.6000
0.7000 0.7000 0.7000 0.7000
0.8000 0.8000 0.8000 0.8000
0.9000 0.9000 0.9000 0.9000
1.0000 1.0000 1.0000 1.0000
1.1000 1.1000 1.1000 1.1000
1.2000 1.2000 1.2000 1.2000
1.3000 1.3000 1.3000 1.3000
1.4000 1.4000 1.4000 1.4000
1.5000 1.5000 1.5000 1.5000
1.6000 1.6000 1.6000 1.6000
1.7000 1.7000 1.7000 1.7000
1.8000 1.8000 1.8000 1.8000
1.9000 1.9000 1.9000 1.9000
2.0000 2.0000 2.0000 2.0000
2.1000 2.1000 2.1000 2.1000
2.2000 2.2000 2.2000 2.2000
2.3000 2.3000 2.3000 2.3000
2.4000 2.4000 2.4000 2.4000
2.5000 2.5000 2.5000 2.5000
2.6000 2.6000 2.6000 2.6000
2.7000 2.7000 2.7000 2.7000
2.8000 2.8000 2.8000 2.8000
2.9000 2.9000 2.9000 2.9000
3.0000 3.0000 3.0000 3.0000
3.1000 3.1000 3.1000 3.1000
[X,Y] = meshgrid(x,y);
Z = sin(Y.^2 + X) - cos (Y-X.^2);
mesh(x,y,Z)
surf(x,y,Z)
zeros(3,5)
ans =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
zeros(3)
ans =
0 0 0
0 0 0
0 0 0
zeros(3,1)
ans =
0
0
0
eye(3)
ans =
1 0 0
0 1 0
0 0 1
eye(5,6)
ans =
1 0 0 0 0 0
0 1 0 0 0 0
0 0 1 0 0 0
0 0 0 1 0 0
0 0 0 0 1 0
ones(2)
ans =
1 1
1 1
ones(2,7)
ans =
1 1 1 1 1 1 1
1 1 1 1 1 1 1
rand(3)
ans =
0.9501 0.4860 0.4565
0.2311 0.8913 0.0185
0.6068 0.7621 0.8214
randn(3)
ans =
-0.4326 0.2877 1.1892
-1.6656 -1.1465 -0.0376
0.1253 1.1909 0.3273
randn(3, 7)
ans =
0.1746 -0.5883 0.1139 -0.0956 -1.3362 -0.6918 -1.5937
-0.1867 2.1832 1.0668 -0.8323 0.7143 0.8580 -1.4410
0.7258 -0.1364 0.0593 0.2944 1.6236 1.2540 0.5711
rand(3)
ans =
0.4447 0.9218 0.4057
0.6154 0.7382 0.9355
0.7919 0.1763 0.9169
rand(3)
ans =
0.4103 0.3529 0.1389
0.8936 0.8132 0.2028
0.0579 0.0099 0.1987
rand(3)
ans =
0.6038 0.0153 0.9318
0.2722 0.7468 0.4660
0.1988 0.4451 0.4186
rand('state', 20)
rand(3)
ans =
0.7062 0.3586 0.8468
0.5260 0.8488 0.3270
0.2157 0.0426 0.5541
rand('state', 20)
rand(3)
ans =
0.7062 0.3586 0.8468
0.5260 0.8488 0.3270
0.2157 0.0426 0.5541
whos
Name Size Bytes Class
X 32x32 8192 double array
Y 32x32 8192 double array
Z 32x32 8192 double array
ans 3x3 72 double array
x 1x32 256 double array
y 1x32 256 double array
Grand total is 3145 elements using 25160 bytes
g = 2;
for k = 1:10, g = 1 + 1/g; end
g
g =
1.6181
1 + sqrt(5)/2
ans =
2.1180
(1 + sqrt(5))/2
ans =
1.6180
x = 1; while x > 0, xmin = x; x = x/2; end
xmin
xmin =
4.9407e-324
realmin
ans =
2.2251e-308
eps*realmin
ans =
4.9407e-324
e=exp(1)
e =
2.7183
if pi^e > e^pi
disp('pi^e is bigger')
else
disp('e^pi is bigger')
end
e^pi is bigger
edit test.m
test
e^pi is bigger
edit maxentry.m
A = magic(4)
A =
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1
maxentry(A)
ans =
16
maxentry(randn(4))
ans =
1.6041
help maxentry
MAXENTRY Largest absolute value of matrix entries
x = 1 + 2 + 3 + ...
4 + 5 + 6
x =
21
edit test1.m
test1
x =
1 2
c =
1.1320
x = rand(7,1)
x =
0.9284
0.2398
0.9753
0.3691
0.7221
0.0477
0.1896
norm(x,1)
ans =
3.4719
norm(x,2)
ans =
1.6020
norm(x,inf)
ans =
0.9753
norm(x,7)
ans =
1.0634
norm(x,-inf)
ans =
0.0477
A = [ 1, 8; 7, -3]
A =
1 8
7 -3
toeplitz([1 0 -1 -2], [1, 2, 4,8])
ans =
1 2 4 8
0 1 2 4
-1 0 1 2
-2 -1 0 1
toeplitz([1 0 -1 -2])
ans =
1 0 -1 -2
0 1 0 -1
-1 0 1 0
-2 -1 0 1
hankel([1 0 -1 -2], [1, 2, 4,8])
Warning: Last element of input column does not match first element of input row.
Column wins anti-diagonal conflict.
> In hankel at 27
ans =
1 0 -1 -2
0 -1 -2 2
-1 -2 2 4
-2 2 4 8
hankel([1 0 -1 -2], [-2, 2, 4,8])
ans =
1 0 -1 -2
0 -1 -2 2
-1 -2 2 4
-2 2 4 8
blkdiag(2*eye(2), ones(2))
ans =
2 0 0 0
0 2 0 0
0 0 1 1
0 0 1 1
A = blkdiag(2*eye(2), ones(2))
A =
2 0 0 0
0 2 0 0
0 0 1 1
0 0 1 1
A(3,4)
ans =
1
A(3,4) = -900
A =
2 0 0 0
0 2 0 0
0 0 1 -900
0 0 1 1
A(2:3,2:3) = ones(2)
A =
2 0 0 0
0 1 1 0
0 1 1 -900
0 0 1 1
repmat(eye(2),2)
ans =
1 0 1 0
0 1 0 1
1 0 1 0
0 1 0 1
repmat(eye(2),2,3)
ans =
1 0 1 0 1 0
0 1 0 1 0 1
1 0 1 0 1 0
0 1 0 1 0 1
C = [ 1 2; 3 4]
C =
1 2
3 4
C = [ B zeros(2); ones(2) [4 1; 4 5]]
??? Undefined function or variable 'B'.
B = [ 1 2; 3 4]
B =
1 2
3 4
C = [ B zeros(2); ones(2) [4 1; 4 5]]
C =
1 2 0 0
3 4 0 0
1 1 4 1
1 1 4 5
tridiag
??? Undefined function or variable 'tridiag'.
help tridiag
tridiag.m not found.
Use the Help browser Search tab to search the documentation, or
type "help help" for help command options, such as help for methods.
norm(C)
ans =
7.7798
norm(C,'frob')
??? Error using ==> norm
The only matrix norms available are 1, 2, inf, and 'fro'.
norm(C,'fro')
ans =
9.5917
norm([8 9 0 6 ],1)
ans =
23
norm([8 9 0 6 ],2)
ans =
13.4536
norm([8 9 0 6 ],8)
ans =
9.4105
cond(C)
ans =
21.2974
cond(C,2)
ans =
21.2974
cond(C,'inf')
ans =
33.0000
cond(C,'frob')
??? Error using ==> norm
The only matrix norms available are 1, 2, inf, and 'fro'.
Error in ==> cond at 48
c = norm(A, p) * norm(inv(A), p);
cond(C,'fro')
ans =
26.7155
cond(C,1)
ans =
29.0000
A = randn(5)
A =
0.2193 0.6145 0.3803 -0.3179 0.7310
-0.9219 0.5077 -1.0091 1.0950 0.5779
-2.1707 1.6924 -0.0195 -1.8740 0.0403
-0.0592 0.5913 -0.0482 0.4282 0.6771
-1.0106 -0.6436 0.0000 0.8956 0.5689
b = randn(5,1)
b =
-0.2556
-0.3775
-0.2959
-1.4751
-0.2340
x = A\b
x =
0.7970
-3.5102
-4.5206
-3.8226
3.0518
eig(A)
ans =
-0.1530 + 1.7303i
-0.1530 - 1.7303i
0.6899 + 0.3810i
0.6899 - 0.3810i
0.6308
lu(A)
ans =
-2.1707 1.6924 -0.0195 -1.8740 0.0403
0.4656 -1.4316 0.0091 1.7681 0.5501
0.4247 0.1474 -1.0022 1.6302 0.4796
-0.1010 -0.5487 -0.3825 1.0866 1.2204
0.0273 -0.3808 0.0441 0.9946 -0.3494
[L,U] = lu(A)
L =
-0.1010 -0.5487 -0.3825 1.0000 0
0.4247 0.1474 1.0000 0 0
1.0000 0 0 0 0
0.0273 -0.3808 0.0441 0.9946 1.0000
0.4656 1.0000 0 0 0
U =
-2.1707 1.6924 -0.0195 -1.8740 0.0403
0 -1.4316 0.0091 1.7681 0.5501
0 0 -1.0022 1.6302 0.4796
0 0 0 1.0866 1.2204
0 0 0 0 -0.3494
[L,U,P] = lu(A)
L =
1.0000 0 0 0 0
0.4656 1.0000 0 0 0
0.4247 0.1474 1.0000 0 0
-0.1010 -0.5487 -0.3825 1.0000 0
0.0273 -0.3808 0.0441 0.9946 1.0000
U =
-2.1707 1.6924 -0.0195 -1.8740 0.0403
0 -1.4316 0.0091 1.7681 0.5501
0 0 -1.0022 1.6302 0.4796
0 0 0 1.0866 1.2204
0 0 0 0 -0.3494
P =
0 0 1 0 0
0 0 0 0 1
0 1 0 0 0
1 0 0 0 0
0 0 0 1 0
P*L*U-A
ans =
-1.1412 -0.1067 -1.3895 1.4129 -0.1531
0.8627 0.0835 0.9609 -0.6668 0.0992
1.1600 -2.3360 0.0196 2.7696 0.5286
-2.1115 1.1011 0.0287 -2.3022 -0.6368
1.2300 1.2581 0.3803 -1.2135 0.1621
L*U-P*A
ans =
1.0e-015 *
0 0 0 0 0
0 0 -0.0000 0.1110 0
0 0 0 0 0
0 0 -0.0555 -0.0555 0
0 0 0 0 0
lu(A)
ans =
-2.1707 1.6924 -0.0195 -1.8740 0.0403
0.4656 -1.4316 0.0091 1.7681 0.5501
0.4247 0.1474 -1.0022 1.6302 0.4796
-0.1010 -0.5487 -0.3825 1.0866 1.2204
0.0273 -0.3808 0.0441 0.9946 -0.3494
[L,U,P] = lu(A)
L =
1.0000 0 0 0 0
0.4656 1.0000 0 0 0
0.4247 0.1474 1.0000 0 0
-0.1010 -0.5487 -0.3825 1.0000 0
0.0273 -0.3808 0.0441 0.9946 1.0000
U =
-2.1707 1.6924 -0.0195 -1.8740 0.0403
0 -1.4316 0.0091 1.7681 0.5501
0 0 -1.0022 1.6302 0.4796
0 0 0 1.0866 1.2204
0 0 0 0 -0.3494
P =
0 0 1 0 0
0 0 0 0 1
0 1 0 0 0
1 0 0 0 0
0 0 0 1 0
[Q,R]=qr(A)
Q =
-0.0851 -0.4568 -0.2904 -0.4980 0.6721
0.3579 -0.0229 0.8733 -0.1622 0.2869
0.8427 -0.3664 -0.2833 0.2659 -0.0677
0.0230 -0.3527 0.0842 -0.6414 -0.6757
0.3924 0.7295 -0.2560 -0.4936 0.0691
R =
-2.5758 1.3167 -0.4111 -0.7990 0.4173
0 -1.5905 -0.1264 1.3091 -0.1858
0 0 -0.9903 1.3864 0.1923
0 0 0 -1.2342 -1.1621
0 0 0 0 0.2361
[U,S,V]=svd(A)
U =
0.1182 0.0765 -0.6805 -0.4593 0.5534
0.1037 -0.7710 -0.1623 0.5187 0.3153
0.9852 0.0608 0.1467 -0.0249 -0.0591
0.0524 -0.2592 -0.5725 -0.1076 -0.7686
-0.0430 -0.5735 0.4017 -0.7126 -0.0090
S =
3.3711 0 0 0 0
0 2.3368 0 0 0
0 0 1.3309 0 0
0 0 0 0.9503 0
0 0 0 0 0.1187
V =
-0.6431 0.5095 -0.5185 0.2121 0.1140
0.5492 -0.0110 -0.6382 0.3515 -0.4092
-0.0242 0.3502 -0.0528 -0.7287 -0.5856
-0.5299 -0.6877 -0.0914 0.0803 -0.4811
0.0585 -0.3804 -0.5593 -0.5422 0.4951
help svd
SVD Singular value decomposition.
[U,S,V] = SVD(X) produces a diagonal matrix S, of the same
dimension as X and with nonnegative diagonal elements in
decreasing order, and unitary matrices U and V so that
X = U*S*V'.
S = SVD(X) returns a vector containing the singular values.
[U,S,V] = SVD(X,0) produces the "economy size"
decomposition. If X is m-by-n with m > n, then only the
first n columns of U are computed and S is n-by-n.
For m <= n, SVD(X,0) is equivalent to SVD(X).
[U,S,V] = SVD(X,'econ') also produces the "economy size"
decomposition. If X is m-by-n with m >= n, then it is
equivalent to SVD(X,0). For m < n, only the first m columns
of V are computed and S is m-by-m.
See also svds, gsvd.
Reference page in Help browser
doc svd
help lu
LU LU factorization.
[L,U] = LU(X) stores an upper triangular matrix in U and a
"psychologically lower triangular matrix" (i.e. a product
of lower triangular and permutation matrices) in L, so
that X = L*U. X can be rectangular.
[L,U,P] = LU(X) returns unit lower triangular matrix L, upper
triangular matrix U, and permutation matrix P so that
P*X = L*U.
Y = LU(X) returns the output from LAPACK'S DGETRF or ZGETRF
routine if X is full. If X is sparse, Y contains the strict
lower triangle of L embedded in the same matrix as the upper
triangle of U. In both full and sparse cases, the permutation
information is lost.
[L,U,P,Q] = LU(X) returns unit lower triangular matrix L,
upper triangular matrix U, a permutation matrix P and a column
reordering matrix Q so that P*X*Q = L*U for sparse non-empty X.
This uses UMFPACK and is significantly more time and memory
efficient than the other syntaxes, even when used with COLAMD.
[L,U,P] = LU(X,THRESH) controls pivoting in sparse matrices,
where THRESH is a pivot threshold in [0,1]. Pivoting occurs
when the diagonal entry in a column has magnitude less than
THRESH times the magnitude of any sub-diagonal entry in that
column. THRESH = 0 forces diagonal pivoting. THRESH = 1 is
the default.
[L,U,P,Q] = LU(X,THRESH) controls pivoting in UMFPACK, where
THRESH is a pivot threshold in [0,1]. Given a pivot column j,
UMFPACK selects the sparsest candidate pivot row i such that
the absolute value of the pivot entry is greater than or equal
to THRESH times the largest entry in the column j. The magnitude
of entries in L is limited to 1/THRESH. A value of 1.0 results
in conventional partial pivoting. The default value is 0.1.
Smaller values tend to lead to sparser LU factors, but the
solution can become inaccurate. Larger values can lead
to a more accurate solution (but not always), and usually an
increase in the total work.
See also colamd, luinc, qr, rref, umfpack.
Reference page in Help browser
doc lu
diary off