Re: [ojAlgo-user] Questions

[Anders P. <and...@op...>]

On 2004-04-19, at 20.56, Inger, Matthew wrote:

> Sorry,
>
> about 28% is spent in multiplyRight, and 20% is spent in the BigMatrix
> constructor.

Generally speaking; working with BigDecimal will be slower than working 
with double. That's the price you pay to get rid of the 
rounding/representation errors.

Would it help to implement multiplyRight in one of the MatrixStore 
subclasses?

> If i switch to JamaMatrix, the time goes back down to what it was
> using Jama by itself.

This is a feature of ojAlgo!

You can easily switch between three implementations with different 
characteristics:

BigMatrix is more precise
JamaMatrix is faster
JampackMatrix works with complex numbers

I'm sure each of the implementations can be improved in many respects, 
but you can't have it all.

> -----Original Message-----
> From: Anders Peterson [mailto:and...@op...]
> Sent: Monday, April 19, 2004 2:14 PM
> To: Inger, Matthew
> Subject: Re: [ojAlgo-user] Questions
>
>
> On 2004-04-19, at 20.04, Inger, Matthew wrote:
>
>> Let me at least see how turning the scale down helps.
>> It appears that the majority of the time is spent in multiply
>> and in the constructor.
>
> The BigMatrix or the QRDecomposition constructor?
>
>>  I can send you the screenshot of what
>> devpartner is telling me if you want.
>
> Yes, please.
>
> /Anders
>
>> -----Original Message-----
>> From: Anders Peterson [mailto:and...@op...]
>> Sent: Monday, April 19, 2004 2:02 PM
>> To: Inger, Matthew
>> Cc: 'oja...@li...'
>> Subject: Re: [ojAlgo-user] Questions
>>
>>
>> On 2004-04-19, at 18.33, Inger, Matthew wrote:
>>
>>> 1.  Have any performance tests been run with BigMatrix?
>>
>> Not really...
>>
>>>    I'm using
>>> BigMatrix to do a
>>>      LinearRegression algorithm.  I had previously been using Jama
>>> directly,
>>> but our FD
>>>      department noticed a difference between the results given by 
>>> java
>>> and
>>> the LINEST
>>>      function in Excel.  I have determined that the difference is
>>> DEFINATELY
>>> due to rounding
>>>      limitations in the java double primitive type.
>>>
>>>      These issues go away when i use ojAlgo library,
>>
>> That's great news to me - that is the reason I created ojAlgo!
>>
>>> however, the time
>>> required to do the
>>>      calculations increases to about 12-13x what it was using regular
>>> double
>>> calculations.
>>>
>>>      Doing 10,000 linear regressions with Jama took about .781s
>>> (according
>>> to junit), and
>>>      with BigMatrix, took 10.345s
>>
>> I wasn't aware the difference was that big. ;-)
>>
>>>      Any idea why such a hug disparity?
>>
>> Try altering the scale. A larger scale means better results, but it
>> takes longer. You can change a matrix' scale whenever you want - the
>> new scale will be used from then on. Elements are not rounded unless
>> you call enforceScale().
>>
>> Internally BigMatrix uses various decompositions for some of the more
>> complex calculations. These decompositions inherit a scale from the
>> parent matrix. It is however not the same - it's bigger. I believe the
>> formula is 2 * (3 + max(9, matrixScale)) and this formula is evaluated
>> when the decomposition is created. After the decomposition is created
>> its scale wont change even if you change the matrix' scale. The scale
>> of a decomposition is never smaller than 24 (according to the above
>> formula).
>>
>> I'm not happy with this, and have been meaning to change it.
>> Decompositions should have the same scale as its parent matrix, and
>> when it is changed, updated "everywhere".
>>
>> Would you prefer that?
>>
>>> 2.  Is there a way to use the solvers in OjAlg to do the linear
>>> regression?
>>> Or is that not
>>>      something that is provided?
>>
>> How are you currently doing it?
>>
>> I imagine the best way would be to simply build an over-determined set
>> of linear equations - Ax=B - and call A.solve(B). Internally BigMatrix
>> would then use the QR decomposition to give a least squares estimation
>> of x. That would be my first attempt.
>>
>> You could use the QP solver do the same thing, but I don't see how 
>> that
>> would simplify or improve anything.
>>
>> What version of ojAlgo are you using - v1, v2 or CVS?
>>
>> I recommend working from CVS. Unfortunately I have moved things around
>> a bit in between version...
>>
>> I don't have access to a profiling tool, but I know they can be very
>> useful in finding bottlenecks. If you can identify a problem, and/or
>> suggest a specific improvement, I'll be happy to help you implement 
>> it.
>>
>> /Anders
>>
>>> ----------------------
>>> Matthew Inger
>>> Design Architect
>>> Synygy, Inc
>>> 610-664-7433 x7770
>>>
>>>
>>>
>>> -------------------------------------------------------
>>> This SF.Net email is sponsored by: IBM Linux Tutorials
>>> Free Linux tutorial presented by Daniel Robbins, President and CEO of
>>> GenToo technologies. Learn everything from fundamentals to system
>>> administration.http://ads.osdn.com/?ad_id=1470&alloc_id=3638&op=click
>>> _______________________________________________
>>> ojAlgo-user mailing list
>>> ojA...@li...
>>> https://lists.sourceforge.net/lists/listinfo/ojalgo-user
>>>
>>
>