Linear equation problem

Here is the problem:
I have 2 point A(x1,y1) & B(x2,y2) ,what i need is find a point C(x3,y3);
This point C should :
1)form a line AC that goes throught point B
2)C is intersectin point of the line AC & one of screen edges :
(left=0,right=ScreenWidht,top=0,bottom=ScreenHeight)
3) The C point form a line (vector?) A--->C & not C--->A
I need to fighure out the C ,so if A is a projectile source,the projectil
will fallow the Target (C point) from A---->C..
Hope that clear enought,i would realy appreciate if i get some help soling this math problem (im not good in math) , that will be great if i get some code ( routine that takes x1,y1,x2,y2 as parametres and calcule x3 and y3)
Thanks.

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I've implemented a modified Linear Levenberg Marquardt function for gauss fitting of spectral data, but have problems from time to time where I simply can't fit a specific data set.
I've traced the problem to the function "Solve Linear Equations" outputting NaN for one or more solution vectors (Not all) which then plays havok with my function.
I've located a data set which is initially OK, but which after solving the linear equations has NaN as the second element of the solution vector array. Changing the matrix type to something other than "generic" gets rid of the NaN for this particular data set, but gives problems with others which work with the "generic" matrix type.
Since my mathematics lectures in college are a few years away now, I'm asking someone out there if they can help me with my problem. I can supply a "corrupting" data set if this would help, or maybe someone knows this problem and has a work-around?
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Hi Altenbach,
The data can have a significant amount of overlap. Typically three to four groups of peaks fully resolved, but each group can have up to 4-5 peaks in them, partially visible only as shoulders.
The start value "problem" is more or less solved (but as usual, not yet finished).
I've got an approximate first-guess peak find running which does a pretty good job of finding the peaks I need, and I then fit the height and FWHM first before doing a full optimisation (The Sub-VI approach allows much more flexible fitting models).
The fitting works well on almost all data sets. There are simply some spectra which (although visibly hardly different to others which work perfectly) do not fit, i.e. generate the NaN response from the "solve linear equations" function. It's a numeric problem (bug?), I'm sure, and not strictly a peak location or resolution problem. then again, I may be wrong.
I refer to the standard "LevMar" VI as being linear, because it assumes a linear relationship between the variable variation and the end mse used for optimisation. This is where the "Solve linear Equations" comes in. Since the relationship is almost certainly not linear (foe example when peaks overlap), I thought maybe the non-linear coefficient guess may yield better results. I've had a quick look through the non-linear LEV-MAR function, but don't understand it yet ot the extent I understand the linear one. It does indeed seem to take a slightly different approach (once you look past the whole parsing code of the "linear" function.
I'll need some time to get some understanding of the non-linear code.
Attached are some example spectra (One which works, and one which doesn't).
Thanks again
Shane
Using LV 6.1 and 8.2.1 on W2k (SP4) and WXP (SP2)
Attachments:
Spectra.zip ‏3 KB

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As for typical values, they can change a lot based on materials. But, the original 3900 data point sets and the 4550 data point sets used practically identical gears. So, use the original 3900 sets I've included as references (check the RO array block numbers to compare).
I've included 3 3900 samples, 3 4550 samples, and 3 4550 samples that have been truncated to 3900 or so as constants on the block diagram.
Also, the check for additional runouts (like 3 per rev, 4 per rev, etc..) is optional, but if you choose to use it, use positive integers only.
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