Re: Solve Feature?

*To*: mathgroup at smc.vnet.net*Subject*: [mg52846] Re: Solve Feature?*From*: DrBob <drbob at bigfoot.com>*Date*: Tue, 14 Dec 2004 06:00:11 -0500 (EST)*References*: <200412100123.UAA18967@smc.vnet.net> <opsir6lsn2iz9bcq@monster.ma.dl.cox.net> <002401c4dfb5$809b6a00$1802a8c0@Pentium4> <opsiu9j4nmiz9bcq@monster.ma.dl.cox.net> <opsiu90xstiz9bcq@monster.ma.dl.cox.net> <41BE2889.8030108@wolfram.com>*Reply-to*: drbob at bigfoot.com*Sender*: owner-wri-mathgroup at wolfram.com

OK, but it's a very simple polynomial system; if Solve and NSolve give wildly wrong answers without notifying us, it's difficult to justify ever using them at all (with machine-precision coefficients). I think I'm forced to use FindRoot virtually every time, already; I just hadn't thought about it. Bobby On Mon, 13 Dec 2004 17:40:57 -0600, Daniel Lichtblau <danl at wolfram.com> wrote: > DrBob wrote: >> By the way, it gets even worse if I use NSolve: >> >> solution = NSolve[{c1, c5}] >> {c1, c5} /. solution >> Apply[Subtract, {c1, c5}, {1}] /. solution >> >> {{x -> -4.27759132*^8, >> y -> 43.74999927054117}, >> {x -> 4.2775924*^8, >> y -> 43.75000072945884}} >> {{False, False}, {False, False}} >> {{1.82977917785309*^17, >> 1.82977917785309*^17}, >> {1.8297792462945597*^17, >> 1.8297792462945597*^17}} >> >> These are simple polynomial equations, so this is really puzzling. >> >> Bobby >> >> On Sat, 11 Dec 2004 17:15:30 -0600, DrBob <drbob at bigfoot.com> wrote: >> >>> HELP!! Here are two very simple quadratic equations (circles): >>> >>> {c1, c5} = {(-50 + x)^2 + (-50 + y)^2 == 156.25, >>> (-50.00000000000002 + x)^2 + >>> (-37.49999999999999 + y)^2 == 156.25}; >>> >>> If we rationalize before solving, we get accurate solutions: >>> >>> solution = Solve@Rationalize@{c1, c5} >>> solution // N >>> {c1, c5} /. solution >>> Subtract @@@ {c1, c5} /. solution >>> >>> {{x -> (25/4)*(8 - Sqrt[3]), y -> 175/4}, >>> {x -> (25/4)*(8 + Sqrt[3]), y -> 175/4}} >>> {{x -> 39.17468245269452, y -> 43.75}, >>> {x -> 60.82531754730548, y -> 43.75}} >>> {{True, True}, {True, True}} >>> {{-4.263256414560601*^-14, 4.973799150320701*^-13}, >>> {-4.263256414560601*^-14, -4.263256414560601*^-13}} >>> >>> You can check this visually with ImplicitPlot: >>> >>> ImplicitPlot[{c1, c5}, {x, -40, 70}] >>> >>> But if we solve without Rationalize, we get wildly inaccurate results: >>> >>> solution = Solve@{c1, c5} >>> {c1, c5} /. solution >>> Subtract @@@ {c1, c5} /. solution >>> >>> {{x -> 16., y -> 43.74999999999993}, >>> {x -> 84., y -> 43.75000000000004}} >>> {{False, False}, {False, False}} >>> {{1038.812500000001, 1038.8125000000005}, >>> {1038.8124999999995, 1038.8124999999993}} >>> >>> I'm using version 5.1. >>> >>> Bobby > > > The behavior of Solve with these approximate inputs is subject to the > vagaries of how it creates an infinite precision problem. As this one is > poorly conditioned it is not too surprising that it does not give a good > result. I'll point out that with VerifySolutions->True Solve will use > significance arithmetic throughout, and this is sufficient (in this > case) to get a viable result. > > The NSolve result is more troublesome. I tracked it to some difficulty > in assessing either of two important facts. > > (1) The precision used in an eigen decomposition is not adequate for the > problem at hand. > > (2) The result gives a large residual for the inputs when normalized in > what I believe to be a reasonable way (obviously we can make the > residual as large or small as we please by suitably rescaling the input). > > It is not too hard to fix the code for either of these but there are > drawbacks. > > The second issue came about from a poor choice on my part of what to > check: I used a numerical Groebner basis instead of the original inputs. > So this can (and will) be addressed. In those cases where it catches > trouble (as in this example) the fix is to redo certain computations at > higher precision. As the code for this is already present the only issue > is in the detection itself. The down side is that I am not certain it > will be adequate to assess poor solutions in general, though it works > reasonably well for this example. > > For the first issue I am right now at a loss. It seems that the numeric > EigenXXX code gives a very poor result for a particular 2x2 matrix that > appears in the course of the NSolve code. It is not too hard to figure > out for a given matrix that the numeric eigen decomposition might be > troublesome. Unfortunately that involves finding numeric singular > values, and I believe that tends to be typically more expensive than the > EigenXXX computation itself. So the problem becomes one of efficiency: > detection of the problem cases could make all NSolve calls slower just > to find the rare bad apples. > > I'll give this some more thought. > > Another possible issue is in how the numeric Groebner basis gets > computed. This is something I also need to look at more closely. It > appears that for one variable order the result is quite tame, but not > for the other (NSolve[polys, {y,x}] works fine). I'm not yet certain but > I think the nuemric instability at this step is essentially unavoidable. > I believe this to be the case because I can find "nearby" polynomial > systems for which a problematic Groebner basis will emerge (and in fact > that happens in this example). It is not actually incorrect, but causes > numeric problems at low precision for the eigen decomposition on a > matrix formed from that Groebner basis. > > Whatever the outcome of investigating that numeric Groebner basis part, > EigenXXX computation needs to sort out how to handle the resulting > matrix. So this part at least is a bug to be fixed. > > > Daniel Lichtblau > Wolfram Research > > > > -- DrBob at bigfoot.com www.eclecticdreams.net

**Follow-Ups**:**Re: Re: Solve Feature?***From:*Andrzej Kozlowski <akoz@mimuw.edu.pl>

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