I have been encouraging the smart people on the 34S to give us great matrix functionality in the machine. It's here.

A couple of years ago, Valentin gave us some simple-looking matrices of two digit integers whose determinants equaled 1. The inverses of the matrices were also composed of integers and the determinant of the inverses were exactly equal to 1.

AM3 was the most difficult matrix that Valentin generated and you can read about it here:

Valentin's AM1, AM2 and AM3 thread

I have just run the matrix determinant on AM3.

It comes out to exactly 1 with no Tiny Element flag, no "Hey the matrix has integers so the determinant must be an integer" adjustments. It is simply 1 to the entire precision of the result displayed.

Then, I computed the inverse of AM3 and the determinant of the resulting inverse.

The result?

1 to the entire precision of the result.

Incredible. Valentin needs to come back and get himself a WP-34S!

P.S. By the way, the determinants, inverses, etc are computed as near to instantly as I can see.

Edited: 5 Oct 2011, 12:33 p.m.

Quote:

---

I have been encouraging the SMART people on the 34S to give us great matrix functionality in the machine. It's here.

---

Lol. Now Steve... anyone can download the current revision and test to your heart's content!

Beta software can be troubling, however, as yesterday afternoon, a version of these commands locked my machine where I had to remove the batteries. Caveat FREE emptor.

This is great progress. i will have to check on other matrix challenges that I may have in my library.

X < > Y,

Richard

Quote:

---

This is great progress. i will have to check on other matrix challenges that I may have in my library.

---

 1.77600177600E-4	-5.59440559441E-3	 5.59440559441E-2	-0.256410256410  	0.615384615385     .....

-5.59440559441E-3	 0.234965034965 	-2.64335664336           12.9230769231		.....

 5.59440559441E-2	-2.64335664336  	 31.7202797203		-161.538461538		.....

-0.256410256410 	 12.9230769231  	-161.538461538		 346.153846154		.....

 0.615384615385 	-32.3076923077  	 415.384615385		.....

-0.8            	 43.2           	-567			.....	

 0.533333333333         -29.4           	 392			.....	

-0.142857142857        	 8              	-108			..... 

The following table presents the results for only the first column of the inverse (I get tired typing in all the numbers) as found by Stefan's program on the HP-35s, the HP-28s, the HP-28s with one iteration of refinement and the TI-85.

     True                    HP-35S                  HP-28S                  HP-28S+                   TI-85

 1.77600177600E-4	 1.77637306166E-4         1.77585836871E-4        1.77600204303E-4          1.77599778919E-4

-5.59440559441E-3	-5.5964307186E-3         -5.59363119661E-3       -5.59440698893E-3         -5.59438445849E-3

 5.59440559441E-2	 5.59708386791E-2         5.59338926659E-2        5.59440738932E-2          5.59437817772E-2

-0.256410256410 	-0.256556617662          -0.25635505057          -0.256410352726           -0.256408778659

 0.615384615385 	 0.615781713919    	 0.615235562229           0.615384873348           0.615380646744

-0.8            	-0.800565424016          -0.799788616834         -0.80000036382           -0.799994393047

 0.533333333333 	 0.533737824252           0.533182624964          0.533333591708            0.533329346788 

-0.142857142857 	-0.142971771814 	-0.142814556361          -0.14285721566            -0.142856018664.

Edited: 5 Oct 2011, 10:16 p.m.

Of course, if you did expansion in minors (iteratively), you should get the exact result as well ... right? Because then it's just multiplication and addition of integers, no division.

This would probably not be true for finding the determinant of the inverse, though, because in that case, the entries are so big that multiplying them out would lead to truncation error.

Expansion by minors is O(n!) time. The algorithm I've used is O(n³) time.

For a 10x10 matrix this is likely to be significant. For small matrices there is no real difference. For the 7x7 examples here, I've no real feeling what the speed differential would be.

Expansion by minors is also going to be at a great risk of bad cancellation if the intermediate results get truncated.

- Pauli

Nice! Congratulations to the WP 34S Team!

The 34S seems to be getting 16 digit accuracy for this example assuming I've done things properly:

001  LBL A

002  .

003  0

004  8

005  STO K

006  M.ALL

007  STO I

008  LBL 00

009  3

010  6

011  0

012  3

013  6

014  0

015  RCL I

016  RCL K

017  M.IJ

018  +

019  1

020  -

021  /

022  STO[->]I

023  ISG I

024  GTO 00

025  RTN

Then .08 M.INV to get the inverse.

- Pauli

For what it's worth, if you replace the 1,1 entry in matrix 1 (58) with 58+x, the determinant is

1+96360245x

For matrix 2, it would be

1+193969587x

and for matrix 3,

1+294228951x

giving some hint as to why these three matrices are "ill conditioned".

Valentin gave a matrix with an even higher condition number in this thread:
http://www.hpmuseum.org/cgi-sys/cgiwrap/hpmuseum/archv015.cgi?read=84694#84694

He didn't call it AM7 in that thread, but in this document:

http://membres.multimania.fr/albillo/calc/pdf/DatafileVA014.pdf

he identifies it as AM7.

is 1.0000... to the limits of precision of the machine.

The determinant of the inverse of this AM7 matrix is also 1.000... to the limits of the precision of the machine.

Amazing!

P.S. the timing is as near to instantaneous as I can imagine for the determinant and the display barely has time to flash "Wait..." or such before the inverse is computed. The determinant of the inverse is instantaneous.

Edited: 9 Oct 2011, 5:32 p.m.

Quote:

---

P.S. the timing is as near to instantaneous as I can imagine for the determinant and the display barely has time to flash "Wait..." or such before the inverse is computed. The determinant of the inverse is instantaneous.

---

I'll likely take out that wait display. I thought these would be slower than they are.

- Pauli

Maybe change it to say:

"Don't blink"

:-)

Good, take that thing out and it will look as fast as it is.

Quote:

---

I'll likely take out that wait display. I thought these would be slower than they are.

---

A second or less for a 10x10.

You do see the wait message long enough to read it.

- Pauli

Quote:

---

But does this still hold for a 10x10 matrix?

---

Who knows ... try this one and see how it fares:

Albillo Matrix #10 (AM#10):

  29   23   40   37   30   32   66   48   38   44

  24   22   45   49   16   39   65   72   38   56

  67   44   92   37   66   20   69   14   14   37

  37   28   63   70   36   35   52   43   26   72

  21   10   19   20   23   16   27   12   13   21

  49   65   93   71   65   39   83   57   42   77

  36   26   60   68   35   33   46   38   22   69

  51   42   63   39   71   16   57   13   15   40

  31   19   43   39   22   22   53   36   35   43

  57   28   76   38   37   23   89   45   38   44

Determinant :

   1

The HP-71B gives DET(AM#10) as 59.9605462429 instead of 1 so it's losing all significant digits and then some.

You can quickly check whether you've inputted it correctly by computing its Frobenius norm, which should give:

    FNORM(AM#10) ->  466.407547109

Best regards from V.

Buenas tardes, Valentin!

Long time no see - welcome back :-)

Walter

Since a lot of us use Excel these days, I took several of these ill conditioned matrices and tried them in Excel (version 2007, on a Windows 7 PC). The determinants are:

AM1 0.99999986183

AM2 0.99999977967

AM3 1.00000101670

AM10 1.00096388386

Edited: 9 Oct 2011, 7:10 p.m.

Quote:

---

I'll likely take out that wait display. I thought these would be slower than they are.

---

Quote:

---

Buenas tardes, Valentin!

Long time no see - welcome back :-)

Walter

---

Thanks, Walter. Regrettably, I can't visit the forum as frequently as in times past, just too busy ... :D

Best regards from V.

Welcome back.

The determinant returned is 1.000000000000000.

The internal working result is 0.999999999999999999998617199680615581171.

So fifteen or so digits are lost during the computation.

- Pauli

Valentín,

Unless you have been following the Forum closely, you may not know how much AM #1 has contributed to improvement in the WP-34S matrix-handling capability: a lot.

Testing that capability with AM #10 should be interesting to observe at least, and it may lead to further improvement.

Many thanks for those matrices, which continue to have beneficial effects even in your unfortunate absence from this Forum.

Peter Murphy
Livermore, CA

Valentin! Good to see you again.

Email me through the forum if you can or use an old email if you have one from the past. Thanks!

I didn't know it had AOS...

The jaw just drops...

11 or 12 ticks for a 10x10 matrix inversion in SLOW mode.
It should be okay to leave the waiting message out.

7 ticks in FAST mode.

- Pauli

Edited: 9 Oct 2011, 10:33 p.m.

It does not have Another Operating System but Another Operating Speed. :-)

SLOW reduces the speed but also the power draw on the poor button cells.

As log as the watchdog doesn't kick in you can leave out the message. If you see "Reset" but it back.

The condition number of AM10 is about 3e14, so you can expect to lose at least 14 digits, that's about right then.

Interestingly, even trying to estimate the condition number on a real 42S gives the wrong result since there, it loses all digits and the column norm of the inverse matrix is off by an order of magnitude... had to use Free42 ;-)

Werner

Well, the mystery is still there, det(AM10) on HP39gs gives...1 exactly. Even doing the trick (det(AM10)-1)*10000 gives 0!

Regards

Quote:

---

Well, the mystery is still there, det(AM10) on HP39gs gives...1 exactly. Even doing the trick (det(AM10)-1)*10000 gives 0!

---

I suggest people with a WP 34S (and other calcs as well) should try (in non-exact mode) these two nifty 10x10 matrices I've concocted for the occasion

#AM 11:

  65    66   -58    74    -3   -46    28    29    11     6

 -19    33    67     6    56    -6    25    20    57    49

  72    19    85   -20    46    14    39    -4    52    43

 -52    -4   -37    95    39    32    79    90     4    -4

 -16    29    71     2    60     3    30    17    61    52

 -39    24   -23    88    23   -12    63    57     4    -1

  63    82    42    28    20   -71    57    49    -7   -12

 -61   -26    47    30    77    63    77    27    26    16

  26   -43    45   -39    47    97    57   -29    43    32

  60    19    34    23    33    10    81     8   -12   -20

True determinant   = 1

HP-71B determinant = 754557.820054 

Frobenius norm     = 461.055311215 

Row norm           = 458

Column norm        = 536

Sum of elements    = 2540

#AM 12:

 -19   33   56   -6  -23   44   25   49   57   20

  26  -43   47   97  -32   13   57   32   43  -29

 -39   24   23  -12   77   54   63   -1    4   57

  65   66   -3  -46   97   39   28    6   11   29

 -52   -4   39   32   84   47   79   -4    4   90

  72   19   46   14  -33   52   39   43   52   -4

 -61  -26   77   63  -10   37   77   16   26   27

  63   82   20  -71   19   61   57  -12   -7   49

 -16   29   60    3  -26   45   30   52   61   17

  60   19   33   10   19   53   81  -20  -12    8

True determinant   = 1

HP-71B determinant = 683755.24004  

Frobenius norm     = 452.653288953

Row norm           = 441

Column norm        = 536

Sum of elements    = 2597

Despite the very small (2-digit or less) integer elements I expect non-exact calc algorithms to lose 20-23 significant digits while computing the determinant. The norms and sum of elements are included to check correct input.

Best regards from V.

Valentin, your guess as to the accuracy loss is spot on...

Matrix  Returned result       Internal result

 AM11   1.000000000000000     1.00000000000000004803708257807578997596

 AM12   1.000000000000000     1.00000000000000005764781563816555143605

                                               ^ 17th digit

On more lost digit and the 34S will get the answer wrong. No that isn't a challenge, I'm sure it is possible.

- Pauli

Of course, at one point you were computing internally with more than 39 digits... but at a VERY large expense of memory, which would *not* be worth it at all.

The real point (to me) is that the 34S matrix commands seem to be more accurate by far than anything we've ever had that was calculator-portable and *ought* to handle most anything that gets thrown at them.

Great job, and someone get Valentin a 34S !!

Currently, I'm doing the LU decomposition using temporaries of 34 digits and the multiply/accumulate calculations to 39. We're just fitting into the volatile RAM at the moment which is perfect.

- Pauli

Only two calculations are done to more than the internal 39 digits, they aren't used by the matrix code.

- Pauli

Here are the results in Excel 2007 for comparison. AM12 is really tough!

AM1 0.99999986183

AM2 0.99999977967

AM3 1.00000101670

AM10 1.00096388386

AM12 4.49647974387

WP 34S does an LU decomposition to 34 digits with pivoting and then computes the determinant from the diagonal. This seems to be quite stable.

Pauli, correct me, if have misread your code.

This is correct. I'm using Dolittle's algorithm like the 15C. This is stable but not the fastest.

The only extra thing to note is that the multiply/subtract steps used in the calculation of the lower triangular matrix are done using 39 digits which could help avoid a little extra cancellation if we're lucky.

- Pauli

Quote:

---

One more lost digit and the 34S will get the answer wrong. No that isn't a challenge, I'm sure it is possible.

---

Indeed it is. Enter AM#13, another one of my 10x10 matrices entirely consisting in very small (2-3 digit) integer elements:

AM #13:

   34    33   195   -18   213   238   -66    13    24   -56

   39   148   -51    95  -388   -11    28    31   -35    49

 -125   124  -129   130    86   -99   156   -31   -53   181

  248   -65   -44   128   107    28    71    84  -119   -10

   87   201    40   291   -25    40   -68   268   176    31

   28  -148   147    75    89   139   187  -146  -156   -22

  175    69   140   178  -143   306   182   -51  -108  -129

 -113   101  -126    97   165  -112   127    36    45   123

 -127   202   148   356   277   224   258    24    63    98

   52  -116   173    93    38   -14  -188   132    76    19



True determinant   = 1

HP-71B determinant = 288,676,439,828

Frobenius norm     = 1387.86490697 

Row norm           = 1777 

Column norm        = 1531

Sum of elements    = 5167

Despite its simplicity I expect non-exact calc algorithms to lose 25-28 digits while computing its determinant.

This will surely make the final 16-digit 34S result inexact in its last 4 or 5 digits. Less capable calcs or computing software (say Excel) will probably lose all their digits, as seen in the result given above for the HP-71B Math ROM's DET function.

Best regards from V.

Buenas tardes Valentin,

please forgive me if my following question is mathematically trivial. But are there any smaller matrices of similar "nastyness" like AM#13? E.g. an 8x8 matrix? I'd estimate the probability for somebody keying in a 10x10 matrix into a pocket calculator being <0.01 even in an high math environment like this forum, and <1e-5 elsewhere.

TIA for your response,

Walter

Yep, that does it.

The returned value is 0.9999999999908109 instead of 1.

- Pauli

Still slightly better then the 71B. ;)

There are 11 nines in the result so rounded to 10 digits it will still return a 1.

Walter, Pauli has committed a .wp34s source file that inputs the matrix. No need to type it in. :-)

condition nr of AM13 is (about) 4e23, so you get 11 digits correct with 34-digit arithmetic.

Free42 Decimal uses 25 digits and returns 1.01769242024 as determinant, so indeed 2 correct digits, and I guess I'm that one out of a hundred that did key in the matrix ;-)

An 8x8 matrix with a similar condition number is possible, but then the individual elements will have to be larger.

And that's my point: 34-digit arithmetic for 10x10 matrices is a bit of overkill. There will always be a matrix that returns completely wrong results. It would be better to return an estimate of the condition number instead, so that you know how many digits of the result can be trusted. If too many digits are lost, then the matrix is the problem (and the way it was obtained), not the algorithm or the number of digits the calculator uses.

Cheers, Werner

There's no need to go even that far.

Take a 2x2 matrix

[[  a   a-1 ]

 [ a+1   a  ]]

    0.956630091747

Cheers, Werner

Edited: 17 Oct 2011, 7:05 a.m. after one or more responses were posted

Try calculating this via:

    5

    0

    2x

    FILL

    DEC T

    INC Y

    cmplx *

:-)

The matrix determinant code isn't as good.

- Pauli

On the 42S I had to create the equivalent 3x3 matrix

[[  a  a-1  0 ]

 [ a+1  a   0 ]

 [  0   0   1 ]]

    a

 ( --- * a - (a-1))*(a+1)

   a+1

Cheers, Werner

Edited: 17 Oct 2011, 3:43 a.m.

that returns the condition number of a matrix?

a short and easy way would be what I use on a 42S:
(it lacks CNRM, so I have to use row norm on the transpose)

 TRAN

 RNRM

 LASTX

 INVRT

 RNRM

 *

Cheers, Werner

Quote:

---

that returns the condition number of a matrix?

---

Since I didn't know its exact definition I searched a bit and found that cond(A)=norm(A)*norm(A^-1).

Well, now I have a problem with this definition: if A is ill-conditioned then computing A^-1 will give a quite inaccurate result, so also norm(A^-1) and thus cond(A) will be inaccurate.

This is what we call in German "the cat biting its own tail".

So again my question: what's the usual way to compute this cond(A)?

Franz

The condition number does not have to be calculated to any great accuracy to be useful. If it is in the order of magnitude of 10^a, then we can expect to lose 'a' digits when calculating the determinant or solving a system of equations. If a is near the number of digits carried by your calculator, the matrix is said to be singular to working precision.

Cheers, Werner

Then it would be more appropriate to compute the log₁₀ of the condition number as an integer.

Quote:

---

The condition number does not have to be calculated to any great accuracy to be useful.

---

If you have to calculate A^-1 to get cond(A), then this A^-1 could already be SO wrong for a VERY-ill-conditioned matrix (just see some DET results in this thread!), that the computed cond(A) with this wrong A^-1 would not only be of "no great accuracy" but even completely wrong.

It's the same as if you would try to measure the precision of any measuring instrument with this (unprecise) instrument itself.

Franz

Edited: 17 Oct 2011, 10:28 a.m.

Quote:

---

It's the same as if you would try to measure the precision of any measuring instrument with this (unprecise) instrument itself.

---

Hi, Franz:

Quote:

---

Well, that doesn't change the principle problem at all! :-(
[...]
It's the same as if you would try to measure the precision of any measuring instrument with this (unprecise) instrument itself.

---

You're absolutely correct that this is kinda chicken-and-egg problem, you need the inverse to compute the condition number and if the matrix is very ill-conditioned your computed inverse will be practically useless.

The way out of this annoying conundrum is to simply estimate the necessary norm of the inverse as economically as possible without actually computing the inverse proper. You may want to have a look at this paper for a feasible approach:

http:/www.math.ufl.edu/~hager/papers/condition.pdf

Best regards from V.

Edited: 17 Oct 2011, 12:53 p.m.

Quote:

---

FWIW, this is one of the easiest jobs d:-)

---

Quote:

---

http:/www.math.ufl.edu/~hager/papers/condition.pdf

---

A short look at this paper tells me that it isn't worth the troubles. ;-)

I've also found some other estimation algorithms on the internet, quite a few of them use the LU-decomposition of the matrix A. This M-LU is already coded in WP34s, so bringing it back to the user might not be the worst idea. :-)

Franz

Hi, Walter:

Quote:

---

[...] are there any smaller matrices of similar "nastyness" like AM#13? E.g. an 8x8 matrix?

---

Certainly. Have a look at this 8x8 matrix o'mine. Not as nasty as it sticks to small (2-3 digit) integer elements but still pretty nasty nevertheless:

AM#8:

  -65   153  -222   257   306   520  -121   461

  131    13   184   -69  -202    13   253  -121

   11    27   -81    88    92    44   -71    99

  347   320   267   171  -328   577   463    88

   87   237   -21   277    55   336   107   104

 -354  -223  -337  -563   548   333    63   323

  208   306    73   243  -115   563   196   215

  165   243    19   112    61   566   453   109

True determinant =  1

Go and try your favourite calculator against it and see how it fares and how many digits are lost.

Quote:

---

I'd estimate the probability for somebody keying in a 10x10 matrix into a pocket calculator being <0.01 even in an high math environment like this forum, and <1e-5 elsewhere.

---

Maybe but you know what they say: "No pain, no gain".

The Spanish version of said proverb begins with "El que quiera peces ..." and common decency prevents me from posting the end ... XD.

Quote:

---

TIA for your response

---

You're welcome.

Best regards from V.

ha!

Quote:

---

"El que quiera peces ..."

---

definitely much more a poetic version than the prosaic saxon one :-)

Glad to see you're in top shape, as usual.

Best,
'AM

Hi, Pauli:

Quote:

---

(the underlining is mine)
Yep, that does it.

The returned value is 0.9999999999908109 instead of 1.

---

So the last 5 digits are lost as well, just as predicted ... :D

Now it's quite simple to concoct a 10x10 matrix with integer elements, still relatively small (6 digits or less), which should make the 34S result lose all its digits (and about 10 more if they were available !) and there's no need to painstakingly key in the 100 elements.

You simply use AM#13 squared, i.e., form a new matrix AM²#13 by multiplying AM#13 times itself. You'll get:

AM²#13:

 -20180   47048    7076   98251   41242   14030   64203   13262   -3203   47956

  11359  -73359  -11370  -94757  -41339   -5116   15477  -82406  -79528  -19197

 100607   -2350   18324   54227 -118108   11778  -46780   68188    6781  -32421

  70760   -7344   40129   10754   58137   61653  -23236   33140   -2354  -34295

  13986   62051  -18247  135133   83179   15409   97207   39458  -19488   67225

  74931  -41113   32420   12012    -678   57658   21586  -20364  -55150  -41049

  76033  -51699   37581    2730    3139  107847  114841  -98712 -138280  -36810

  69960   46252   16119  100837  -72889   21405  -18475   86423   35487  -10128

 143252   65026   33559  263485  -53355   94084  133288   73613  -61885   21603

 -54883   23173  -39308   40609  149155  -56851   20293   30429   26420   71570



True determinant   = 1

HP-71B determinant = -5.1369256228 E32 (!)

Frobenius norm     = 661395.105928 

Row norm           = 943150  

Column norm        = 812795  

Sum of elements    = 1928143

If I'm correct you should lose 45-50 digits at the very least so completely ruining the 34S result (let alone other models' !).

Of course it is possible to produce a 10x10 matrix with smaller integer elements and similarly high condition number but it would be necessary to key it in which would be a pain in the derriere.

Best regards from V.

I think you can do this ad nauseam. Squaring an ill conditioned matrix should roughly square its condition number thus doubling the number of lost digits. Still interesting that you can do this with such harmless looking integers.

0.9999999999999997

Which displays as 1 of course :-)

- Pauli

I think the numbers stopped looking harmless when they hit three digits positive and negative ;-)

- Pauli

The condition number is one I'd like to have included. We're out of flash again and scraping any back is getting much harder so it is unlikely to ever go native.

The reason I've not done this is as Franz has already pointed out: I don't know how without suffering the effects of any ill-conditioning.

Valentin's link looks interesting.

How many people want M-LU exposed again? Assuming we can squeeze it back in.

- Pauli

Quote:

---

Arrogant as usual ... ;-)

---

Quote:

---

It's really easy - it doesn't have to be complex just because you don't know it ;-) But since some folks earn their $$$ teaching that method I won't disclose it here.

---

Quote:

---

I think the numbers stopped looking harmless when they hit three digits positive and negative ;-)

---

Oh really ?

So I take it you're saying that the big bad 34-digit full-floating-point 34S is afraid of being harmed by those meanie three-digit positive and negative integers ? ... ;-)

Pathetic ! ...

Perhaps the 34S is not for me after all, I much prefer real-macho calculators which are afraid of nothing whatsoever I may throw at them ... XD

Best regards from V.

Quote:

---

I didn't expect anything else from you, because I already know you and your vacuous statements here since a few months.

---

Buenas dias Valentin,

Quote:

---

So I take it you're saying that the big bad 34-digit full-floating-point 34S is afraid of being harmed by those meanie three-digit positive and negative integers ? ... ;-)

...

I much prefer real-macho calculators which are afraid of nothing whatsoever I may throw at them ...

---

Personally, I think you took Valentin's post the wrong way :-)

Anyway, I never said the 34S was scared of anything, I only said I was. My years of pure mathematics never involved numbers as large as these, we'll not written out explicitly at any rate...

- Pauli

Quote:

---

Personally, I think you took Valentin's post the wrong way :-)

---

Yes, definitely. Doesn't matter, though, it happens all the time ...

Quote:

---

Anyway, I never said the 34S was scared of anything, [...]

---

Well, it should. Just try AM#13 squared, as above, and post what the computed result is, I'd be curious to know ...

TIA and best regards from V.

Too many program steps for the matrix itself :-( Make the matrix simpler for goodness sake....

Assuming I've squared the matrix properly & I'm not sure I have after more half a dozen semi-decent (export) German beers and a third of a bottle of bad wine, the determinant is -142456776964.0436.

Not really an unexpected result given the ill-conditioned nature of the matrix.

- Pauli

PS: Valentin, if you need a 34S, I'm willing to reflash one of mine and send it your way.

Quote:

---

...after more half a dozen semi-decent (export) German beers and a third of a bottle of bad wine...

---

Franz

Come on, most hard-core programmers code best after a bit of ethanol. Sadly, I think I'm a bit past that peak :-(

To reassure our user base, I'm not coding anything on the 34S tonight....

- Pauli

Quote:

---

How many people want M-LU exposed again? Assuming we can squeeze it back in.

---

I'm about the last person to be qualified to have a say on this but I would hazard that the LU decomposition should always be exposed (i.e., available) to end users, as it's pretty useful for many advanced matrix processing.

On the M-COND command, computing it reliably is not practical and a decent estimate (plus or minus one order of magnitude) is about the best that can be done within reasonable time and memory limits. However, on the other hand we should remember that what's important is the final goal and the condition number isn't it.

The final goal is to get a decent estimation on the number of digits lost in the final result when computing the matrix determinant. Abour three years ago I wrote a full 16-page article discussing and implementing a novel idea I came up with in order to achieve this goal, with worked examples aplenty and intended for its immediate publication in HPCC Datafile magazine (together with four or five other very worthwhile articles, even though I say so myself) but most regrettably things went South then and there through no fault of my own and the articles never saw the light.

They wanted both my articles and my money, though they had next to none of the former and simply way too much of the latter. They got neither.

Best regards from V.

Quote:

---

I'm about the last person to be qualified to have a say on this but I would hazard that the LU decomposition should always be exposed (i.e., available) to end users, as it's pretty useful for many advanced matrix processing.

---

I'd have said quite the opposite. When it comes to hard core mathematics, I value your opinion quite highly. I'll try hard to squeeze the exposed LU decomposition back in again.

Maybe not in the upcoming release, but the one after...

- Pauli

AM#8 has a condition number of about 5E19 according to Mathematica, but my HP50 says the condition number is about 5E15. If the goal is to determine the true condition number, then arithmetic with many more digits than the 15 digits internally in the HP50 would be necessary.

However, if one's goal is to solve some system using the given matrix, knowing that the condition number is at least 5E15 is enough to know that any solution derived from that matrix is likely to have no correct digits (on an HP50)--we don't need to know that the true condition number is 5E19.

Testing the COND function on the HP50, I have been unable to find a matrix with a true condition number greater than E12 which was inaccurately calculated to have a smaller condition number. The calculator doesn't seem to ever seriously underestimate the condition number.

Using a column matrix of:
b=[1289 202 209 1905 1182 -210 1689 1728]T along with A=AM#8, we have a linear system Ax=b. The HP50 solution of this system, using the / key, is:

x=[-85.97057 -1328.61068 452.27432 61.401457 1570.67801 1770.08111 2566.08833 471.67052]T

but, the exact solution is:

[1 1 1 1 1 1 1 1]T

We can see that the high condition number makes for no correct digits in the solution.

Using the LSQ function to solve the system rather than the / key method gives much better results on the HP50--showing the first 4 digits of the results:

[.7876 1.181 .7576 1.015 .7726 .8039 1.211 1.212]T

This shows the advantage of orthogonal methods of solution rather than the Gaussian method.

Hi, Rodger! Long time no hear.

Quote:

---

This shows the advantage of orthogonal methods of solution rather than the Gaussian method

---

Cheers, Werner