(PC12xx~14xx) qthsh TanhSinh quadrature

04172021, 01:51 PM
(This post was last modified: 04172021 09:16 PM by robve.)
Post: #61




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04172021 12:03 AM)Albert Chan Wrote: The plot showed a nice bellshaped curve, easy to get area, simply by summing squares. Rather than doing that, I think we can do better to quickly estimate an optimal d using a geometric sequence of points. I ran a few tests with this change to 'quad' for ExpSinh: Code: else if (isfinite(a)) { Note what happens to 'diff' for these integrands: exp(.2*x) on [0,inf) d~45 is good exp(.1*x) on [0,inf) d~100 is good exp(.01*x) on [0,inf) d~700 is good exp(.001*x) on [0,inf) d~9000 is good 1/(x*x) on [1,inf) d~1 is good 1/(1+x*x) on [0,inf) d~1 is good log(1+(x*x))/(x*x) on [0,inf] d~1 is good (x*x*x)/(exp(x)1) on [0,inf) d~7 is best d~8 is good x/sinh(x) on [0,inf) d~9 is best d~8 is good (x*x)/sqrt(exp(x)1) on [0,inf) d~17 is best d~16 is good pow(x,7)*exp(x) on [0,inf) d~25 is best and d~32 is good exp(sqrt(x)) on [0,inf) d~81 is best and d~64 is good exp(x)*(x*x*x) on [0,inf) d~28 is good exp(x)*pow(x,5) on [0,inf) d~50 is good Note the \( 2^i \) value when the diff sign changes, if it changes. Periodic functions switch signs multiple times, these are not great to integrate with ExpSinh anyway e.g. sin(x)/(x*x) on [0,inf). The idea of using geometric sequences to determine d aligns with the ExpSinh rule. This method is not perfect yet and certainly needs some tweaking.  Rob Edit: I checked 208 ExpSinh integrals from the benchmarks and they all show that this conceptually works. I've added some more interesting cases to the list. "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04172021, 11:45 PM
Post: #62




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04172021 01:51 PM)robve Wrote: printf("%d diff=%g\n", 1 << i, f(a + d/v)/v  f(a + v*d)*v); There may be a flaw with this ... Example, we know exp(0.01*x) on [0,inf], d ~ 700 is good But, we can rewrite the integrand as exp(0.01/x)/x^2, that has optimal d ~ 1/700 With sign checking method, both will suggest optimal d ~ 700. lua> Q = require 'quad' lua> f = function(x) return exp(0.01*x) end lua> g = function(x) return exp(0.01/x)/(x*x) end lua> Q.quad(Q.count(f), 0, huge, 700), Q.n 100 71 lua> Q.quad(Q.count(g), 0, huge, 1/700), Q.n 100 71 If we build g(x) = f(1/x)/x^2, both would use the *same* points, with orders swapped. (points might have rounding errors with 1/x, but otherwise equivalent.) The formula is just simple substitution, x = 1/y, dx = dy/y^2 \(\displaystyle \int_a^∞ f(x)\,dx = \int_0^∞ f(x+a)\,dx = \int_0^∞ {f(1/y+a) \over y^2} dy\) 

04182021, 01:30 AM
Post: #63




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04172021 11:45 PM)Albert Chan Wrote: There may be a flaw with this ... Not so fast. In your example the left side's magnitude f(a+d/v)/v is larger than f(a+d*v)*v which means that the sign changes for a different reason. In my test this shows that d should be small (because the test also takes the magnitudes of the difference into account). So your example is not a counter example of my earlier post on why this might work. Furthermore, as you will notice, the sign change is not always the exact place for an optimal d, but close. The magnitude change is a critical indicator (and more reliable) in the neighborhood of the sign change. So far it works every time. I was surprised that when I discovered this that for all integrals of the 208 the sign change suggests there is a solid predictor based on this that can be perfected with some tweaking.  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04182021, 01:42 AM
(This post was last modified: 04182021 02:14 AM by robve.)
Post: #64




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04182021 01:30 AM)robve Wrote: In your example the left side's magnitude f(a+d/v)/v is larger than f(a+d*v)*v which means that the sign changes for a different reason. In my test this shows that d should be small (because the test also takes the magnitudes of the difference into account). So your example is not a counter example of my earlier post on why this might work. Here are the numbers. For your example exp(0.01/x)/x^2 the change is not due to the right side's magnitude, but the left side: d diff (leftright) 2 diff=2.92578 (3.92079  0.995012) 4 diff=14.3751 (15.3726  0.997503) 8 diff=58.0807 (59.0794  0.998751) 16 diff=217.149 (218.149  0.999375) 32 diff=742.577 (743.577  0.999688) 64 diff=2158.79 (2159.79  0.999844) 128 diff=4554.36 (4555.36  0.999922) 256 diff=5065.24 (5066.24  0.999961) 512 diff=1565.58 (1566.58  0.99998) 1024 diff=36.4476 (37.4476  0.99999) 2048 diff=0.994646 (0.00534945  0.999995) 4096 diff=0.999998 (2.72909e11  0.999998) 8192 diff=0.999999 (1.77573e28  0.999999) 16384 diff=0.999999 (1.87946e63  0.999999) 32768 diff=1 (5.26361e134  1) 65536 diff=1 (1.03212e275  1) For exp(.01*x) the change is due to the right side's magnitude: d diff (leftright) 2 diff=2.92578 (0.995012  3.92079) 4 diff=14.3751 (0.997503  15.3726) 8 diff=58.0807 (0.998751  59.0794) 16 diff=217.149 (0.999375  218.149) 32 diff=742.577 (0.999688  743.577) 64 diff=2158.79 (0.999844  2159.79) 128 diff=4554.36 (0.999922  4555.36) 256 diff=5065.24 (0.999961  5066.24) 512 diff=1565.58 (0.99998  1566.58) 1024 diff=36.4476 (0.99999  37.4476) 2048 diff=0.994646 (0.999995  0.00534945) 4096 diff=0.999998 (0.999998  2.72909e11) 8192 diff=0.999999 (0.999999  1.77573e28) 16384 diff=0.999999 (0.999999  1.87946e63) 32768 diff=1 (1  5.26361e134) 65536 diff=1 (1  1.03212e275) Part of tweaking the test to get an optimal d is to observe the cliff: there is a sharp drop in magnitude in the right part, from 1566.58 to 37.4476 while the left side is still smooth, followed by a sign change. This suggests an optimal d is somewhere near 5121024. Edit: I just now realized I had changed the test earlier today with respect to the magnitude check included in the test as a filter. I had not published this change or said anything about it. To produce the numbers shown above: Code: else if (isfinite(a)) { Note that this scales the left fl and right fr by r just to make the value's magnitudes comparable to display, otherwise this is unnecessary. This is not the final version, since this can be further improved.  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04182021, 12:56 PM
(This post was last modified: 04192021 07:01 PM by robve.)
Post: #65




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04172021 11:45 PM)Albert Chan Wrote: With sign checking method, both will suggest optimal d ~ 700. Edit: I've updated the document with an improved version of the predictor (shown below) and an explanation how it works. The following update sets d=1/700 for this case, so d is now either increased or decreased towards an optimum: Code: else if (isfinite(a)) { This is a rough predictor that could be further improved. But this predictor is already reasonably effective to increase or decrease d in order to reduce the number of points evaluated by ExpSinh. The predictor evaluates points too, but quits when an optimal d is found. There are ways to reduce this overhead, e.g. taking larger steps for d and perhaps followed by a second refinement step to adjust d. With the simple predictor I've tested 208 integrals on [a,+inf) in the zip file with eps=1e9: 29 are improved with 61 fewer points on average to integrate each of those.  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04192021, 10:16 PM
Post: #66




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04182021 12:56 PM)robve Wrote: There is one bad case exp(3*(x*x)4/(x*x)) with 228 more points. The predictor pushes too hard on this one where the diff is close to zero already at the begin of the search, meaning there is no need to search for d in this case.Note: this quote was removed, but it is instructive to see why it failed ... Close to zero at the beginning of search is expected, for any f(x). When v=1, f(v)*v  f(1/v)/v = f(1)  f(1) = 0 This is just the trivial solution, and cannot be used to predict d = 1. To simplify, let v = e^t, instead of v = 2^t: \(\displaystyle \int_0^∞ f(x)\,dx = \int_{∞ }^∞ f(v)·v\,dt = \int_0^∞ \left[f(v)·v + f({1\over v})·{1\over v}\right]\,dt \) Except for v=1, if last 2 term are same size, we assumed both will be tiny. This should covered most of the (likely bellshaped) area, thus can estimate optimal d.  Back to why f(x) = exp(3*(x*x)4/(x*x)) failed sign change search. f(v)*v = f(1/v)/v, where v = e^t Take log both side: 3*exp(2t)  4*exp(2t) + t = 3*exp(2t)  4*exp(2t)  t 2t + exp(2t)  exp(2t) = 0 t + sinh(t) = 0 On the left, both terms are increasing function, only solution is t=0. Thus, except for the trivial v=1, there is no solution. And, there are many more bad cases, say, g(x) = exp(m*x), m ≥ 1 g(v)*v = g(1/v)/v Again, substitute v=e^t, and take log both side: m*e^t + t = m*e^t  t 2t = m*(e^t  e^t) t = m * sinh(t) sinh(t) = t + t^3/3! + t^5/5! + ... sinh(t)/t = 1 + t^2/3! + t^4/5! + ... ≥ 1 Removed trivial solution, (t = sinh(t) ⇒ t = 0), we have m < 1  Perhaps estimate of d based on peak of f(v)*v is safer ? (there is always a peak, unless integral diverges) f(x) = exp(3*(x*x)4/(x*x)) Searching for peak of f(v)*v (we can also do numerically): diff(f(v)*v, v) = 0 f(v) + v*f'(v) = 0 f(v) + v*f(v)*(6v + 8/v^3) = 0 1  6v^2 + 8/v^2 = 0 6v^4  v^2  8 = 0 // assumed v ≠ 0 v^2 = (1+√193)/12 // we throw away the negative root, v^2 > 0 v = √((1+√193)/12) ≈ 1.114 // again, throw away negative root, v=e^t > 0 lua> Q = require 'quad' lua> f = function(x) x=x*x; return exp(3*x4/x) end lua> Q.quad(Q.count(f), 0, huge, 1.114) 0.0005013071292635826 1.4667498799376226e010 lua> Q.n 41 Same treatment for g(x) = exp(m*x): diff(g(v)*v, v) = 0 g(v)*1 + (g(v)*m)*v = 0 1  m*v = 0 v = 1/m  when g(v)*v peaked Peak for d might not be "optimal", but may be good enough. 

04202021, 02:19 AM
Post: #67




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04192021 10:16 PM)Albert Chan Wrote:(04182021 12:56 PM)robve Wrote: There is one bad case exp(3*(x*x)4/(x*x)) with 228 more points. The predictor pushes too hard on this one where the diff is close to zero already at the begin of the search, meaning there is no need to search for d in this case.Note: this quote was removed, but it is instructive to see why it failed ... Sharp eye to notice Rather than posting every small change in a new post which is harder and more time consuming to go through, this small change in this post avoids this problematic case for the reason I had already mentioned earlier in that post, the case when the initial diffs are too close to zero. This small update also has another advantage. It allows the search for an optimized d from any initiallyspecified d instead of d=1. Perhaps this means that the method may have a fixed point that is the optimal d by invoking the predictor repeatedly with improved d and by using smaller steps. Interesting to investigate. (04192021 10:16 PM)Albert Chan Wrote: And, there are many more bad cases, say, g(x) = exp(m*x), m ≥ 1 Nope. The diff sign does not change for this integrand. And for larger m the search quits early. (04192021 10:16 PM)Albert Chan Wrote: Perhaps estimate of d based on peak of f(v)*v is safer ? I have not found a single counter example yet that does not work with the predictor. I also spent many hours to try various improvements and test them with >208 integrals. I have about a dozen tweaks of the method with mixed results. Not interesting to share. A small change was needed to avoid the "bad" case and others similar to it, which are not difficult to recognize from the valid cases by inspecting the diff h sequence. Note that v (now more appropriately renamed r) should not be too small initially and is r=2 for the \( 2^i \) sequence. I also tried regula falsi to improve d, but unfortunately this does not always help. I also found that larger estimates of d (in the 1000s) tend to overshoot the optimal d. In this case starting with a larger d and then repeating the predictor recursively should improve the estimate closer to optimal. However, keeping things simple pays off, because moving d at least in the right direction helps convergence, even when we don't hit the optimal d, so we don't evaluate more points than necessary (or more than ExpSinh needs!). In any case, the geometric sequence of r can be \( 2^i \) or \( e^i \) or another to take larger steps to reduce the number of evaluations. \( 2^i \) is just cheap to compute. (04192021 10:16 PM)Albert Chan Wrote: f(x) = exp(3*(x*x)4/(x*x)) I already tried searching for the "cliff" (or point after the peak). I had mentioned the cliff before as relevant. But it is easy to see that the search should continue until the sign changes. Only at the point when the sign changes the following comparison can be safely made to determine the direction that d should move in: if (fabs(fl) < fabs(fr)) d /= r; else d *= r; Doing this at the peak, or right after, fails the method, causing d to move in the wrong direction. However, the peak or cliff can be closer to where the optimal d is located for large d (in the 100s or 1000s), before the sign changes, but this adds more complexity. A simple condition is to check that the diff h is not zero and in that case move r back one step: if (h != 0) r /= 2; If the diff is zero, we are at a reasonably good or optimal r to adjust d. This can happen very early for example 1/(1+(x*x)) and log((1+(x*x))/x)/(1+(x*x)) or late with for example x/sinh(x). For the last integrand we get d=4 (131 points, est.rel.err=2e11) which improves the accuracy with the same number of points. Applying a regula falsi correction to r (instead of r/=2 see the code) we get d=6.7 (69 points, est.rel.err=6e10). That's incredible  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04202021, 01:58 PM
Post: #68




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04202021 02:19 AM)robve Wrote: I have not found a single counter example yet that does not work with the predictor. Is these two already counterexamples ? Both functions, two sides overlapped, f(v)*v = f(1/v)/v XCas> f1(x) := 1/(1+x*x) XCas> f2(x) := log((1+(x*x))/x)/(1+(x*x)) XCas> simplify([f1(v)*vf1(1/v)/v, f2(v)*vf2(1/v)/v]) → [0, 0] In other words, f(e^t)*(e^t) is even function, with respect to t. Technically, there were no sign changes. But, with rounding errors, if lucky, we expected sign changes early on. In other words, closed to whatever starting guess of d was. It just happpened we started with d=1, and d=1 is close to optimal. Now, try g1(x) = f1(x*10), g2(x) = f2(x*10) Since this is simple scaling, we expected both have optimal d ≈ 1/10 I don't think you'd catch a sign change here (except for v=1, useless to predict d) 

04202021, 07:25 PM
Post: #69




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04202021 01:58 PM)Albert Chan Wrote:(04202021 02:19 AM)robve Wrote: I have not found a single counter example yet that does not work with the predictor.Is these two already counterexamples ? I mentioned before that for cases like these the diff h is already zero, or very close to zero. If a sign change happens in that case it is noise/roundoff and should be ignored (as is done in the updated code). These "bad" integrands are easy to filter out without deteriorating the predictions for "good" integrands. Note that the diff h behavior has no peak/cliff with these. A relevant sign change is preceded by some (significant) curve with a peak, that cannot be produced by rounding error noise. Maybe I will be forced to eat my words later if there are many "bad" cases. But so far the hundreds of examples tested don't appear to suggest there is a fundamental problem with this approach, just minor details. Honestly, it is not too difficult to create some problematic "bad" cases that are based on periodic/oscillating functions over [0,∞) for which the diff h has a useless sign change more than once. Take \( \sin(40\pi x)/x^2 \) for example. But doing that is a bit "cheating", because these quadrature methods are known to perform poorly with such type of functions anyway (so it does not matter). IMHO it is more interesting to have a preprocessor to adjust d that works well for the ExpSinh integrands that we expect to work well with this quadrature. This should exclude "bad" integrands for which a good value of d cannot be determined to begin with, i.e. ExpSinh convergence is always bad with a large residual error. Also,guessing a suboptimal d is not fatal for the "good" integrands. There are several things that are intriguing to look into:  For large estimated d values the predicted d tends to overshoot the optimum nonlinearly for large d in the 1000s. I tried a correction method with interpolation, but the results were mixed. My hunch is that the peak+cliff location should be taken into account somehow to adjust for large d (likewise, adjust for tiny d=1/r).  Another way to address this suboptimality problem for large d is to use a recursive scheme to improve d. We can try a sequence of four values, say r=2,10,100,10000 to detect very roughly where a sign change occurs, then adjust d and try again with some smaller points (say powers of 2) and again with a few points of smaller powers, etc.  The predictor should quit early when d cannot be adjusted (no sign change) by testing the sequence begin/end points first e.g. r=2 and r=10000, to limit this overhead. Checking 1<r<2 is not useful. It only moves d upwards by a fraction which does not help much to improve convergence (downwards by a fraction is done with larger r, i.e. d = d/r).  A solid theoretical analysis of this method is needed to better understand how robust it is.  Can this test be used to quickly determine if an integrand is bad for ExpSinh to begin with?  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04242021, 02:31 AM
Post: #70




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
Some progress. The following updated and improved method predicts an optimal splitting point d for ExpSinh quadratures \( \int_a^\infty f(x)\,dx = \int_a^d f(x)\,dx + \int_d^\infty f(x)\,dx \). The method uses bisection with an exponential scale. The method inspects 2 or 4 points to check if an optimization is applicable. Bisection uses just 8 points to find a closetooptimal d. This reduces the overhead to only 5.95 evaluation points on average for the 208 integrals tested, requiring 179.38 points each on average with eps=1e9.
Code: // return optimized ExpSinh integral split point d This new method of optimization is generic and not specifically tailored to the 208 integrands. Integrands that are suitable for ExpSinh will benefit, but those that do not converge or very slowly converge such as periodic and oscillating functions will not benefit from an optimized splitting point d. Of the 208 integrals tested, 29 are improved significantly in accuracy and/or number of points evaluated (63 fewer points on average). Of these 208 integrands, 14 are slightly worse (11 more points on average). However, practically there is almost no impact because 8 of these 14 integrands are not suitable for ExpSinh quadrature and return a large error with and without the optimized d. The full ExpSinh quadrature results with eps=1e9 are listed in Appendix C.  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04242021, 08:24 PM
Post: #71




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04242021 02:31 AM)robve Wrote: Some progress. The following updated and improved method predicts an optimal splitting point d for ExpSinh quadratures \( \int_a^\infty f(x)\,dx = \int_a^d f(x)\,dx + \int_d^\infty f(x)\,dx \). The method uses bisection with an exponential scale. The method inspects 2 or 4 points to check if an optimization is applicable. Bisection uses just 8 points to find a closetooptimal d. Max. of 12 points ... impressive For illustration purpose, I tried 1 from Appendix C, where suggested d does most good. (there were 1 that does better, but integrand is oscillatory, so it might be just luck) lua> f = function(x) local y=sqrt(x*x1); return exp(y*11/1000)*x/y end lua> Q.exp_sinh_opt_d(f, 1)  default eps=1e9, d=1. 512 Code: N x f(1+x) f(1+x)*x sign(topbot) I also made a version based on peak, and extrapolate for xintercept. I've noticed after the peak, the curve mostly go straight down. (code use tangent line of last point, of the fitted quadratic) Of course, with peak, there are really 2 valleys. Code assumed optimal d is to the right of peak. Code: function Q.peak(f, a, d) lua> Q.peak(f, 1) 89.13375894266683 399.8238469921945 Note that code evaluated an extra point passes the peak (when points are too close) This is to get a more reasonable slope for the tangent line. Without the extra point, we would get peak, xintercept ≈ 88.25, 9106. Code: N x f(1+x) f(1+x)*x Let's see effect of suggested d, vs default d=1 lua> function quad(f,...) local s,e = Q.quad(Q.count(f),...); return s,e,Q.n end lua> quad(f, 1, huge)  default d=1 90.90909088146049 3.999609164190213e009 273 lua> quad(f, 1, huge, 512)  sign change based d 90.90909089915341 6.360683358493127e011 143 lua> quad(f, 1, huge, 400)  xintercept based d 90.90909089845148 2.0323842611238905e011 143 lua> quad(f, 1, huge, 89)  peak based d 90.90909089656193 4.928270670014641e011 141 The peak may be used for d. In that case, expsinh transformed plot, when folded, turn into half a bellshaped curve. 

04262021, 03:08 PM
(This post was last modified: 04272021 07:31 PM by Albert Chan.)
Post: #72




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
An improvement to previous post Q.peak().
Instead of calculating f(a+x)*x, this version use f(a+x), saving some computations. Bonus: this may avoid overflow/underflow issues, if x is huge/tiny. Code: function Q.peak(f, a, d)  find peak of f(a+x)*x, guess=d Code assumed peak is bellshaped, not bumpy. Also, code is optimized for peak close to user inputted d. Example, first integral from qthsh.pdf Appendix C: lua> f = function(x) return exp(x)/x end lua> Q.peak(f, 1)  default d=1 0.5980678336446792 3.218395385172793 With default d=1, code does 3 function evaluations. (note: code optimized away last column) Code: N x f(1+x) f(1+x)*x lua> Q.quad(Q.count(f), 1, huge), Q.n  d = 1 (default) 0.2193839343909888 129 lua> Q.quad(Q.count(f), 1, huge, 3.218), Q.n  d ≈ extrapolated xintercept 0.21938393439552076 69 Update: code adjusted to quit early, if detected sign changes. 

04292021, 07:33 PM
Post: #73




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04262021 03:08 PM)Albert Chan Wrote: An improvement to previous post Q.peak(). Great! The overhead cost to evaluate the peak per integrand is low, only 3.4 on average for the 208 integrals The overall results with respect to reducing the number of ExpSinh evaluations needed per integrand for eps=1e9: 91 improved by 65.9 fewer evaluations on average 91 are worse by 45.3 more evaluations on average Funny the split in 91 and 91. Coincidence? To compare, the current version of exp_sinh_opt_d for eps=1e9 gives: 29 improved by 63.2 fewer evaluations on average 14 are worse by 11.3 more evaluations on average (almost all oscillatory cases that are known to be problematic for ExpSinh and are marked N/A) I noticed that when a sign change is detected, peak() also does overall very well, except for one strange case that costs a lot more, 180 points more for log(1+9*(x*x))/(1+16*(x*x)) where d=10.9477 which for a small d<1 is better, meaning the movement of d is in the wrong direction? However, for the remaining 28 (or so) cases peak() can be closer to the optimum, simply because exp_sinh_opt_d() produces geometric values for d up to a large d=2^16 while peak() assumes the optimum is close to the given d (according to Albert). However, for exp_sinh_opt_d() we could refine d, e.g. with another step or with interpolation, which not done in the current exp_sinh_opt_d() version. The tentative(!) results are in the attached PDF for exp_sinh_opt_d() (left) and peak() (right), with yellow rows indicating exp_sinh_opt_d() was applicable due to a sign change and orange rows also having a sign change but are marked N/A for bad ACCURACY because of large errors of the integration due to oscillatory functions etc. so these are not interesting to compare. The column ACCURACY shows IMPROVED if the abs.err is lower than the original unoptimized ExpSinh integral abs.err (not shown in the PDF). However, ACCURACY can be noisy because the abs.err is so small. The abs.err of the relevant cases is mostly better than the specified eps=1e9 anyway, meaning we should be fine anyway: expsinh.pdf (Size: 127.29 KB / Downloads: 1) I feel that progress has been made for both methods. However, when I have time, I plan to take a closer look at further improvements and directions to go with this. Also, I assume something similar can be done for SinhSinh to split the integral at a more optimal point in some cases. Albert, you have the following "magic numbers" hardcoded without explanation: while L/C > 2.353 do while R/C > 0.425 do Are these empirically established?  Rob "I can count on my friends"  HP 71B,PrimeTi VOY200,Nspire CXII CASCasio fxCG50...Sharp PCG850,E500,2500,1500,14xx,13xx,12xx... 

04292021, 11:38 PM
Post: #74




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04292021 07:33 PM)robve Wrote: I noticed that when a sign change is detected, peak() also does overall very well, except for one strange case that costs a lot more, 180 points more for log(1+9*(x*x))/(1+16*(x*x)) where d=10.9477 which for a small d<1 is better, meaning the movement of d is in the wrong direction? Q.peak() got the direction wrong. (with points at 1/4, 1/2, 1. left side intercept ≈ 0.158) Q.peak() always go for the xintercept of the right. In other words, it tried to push bulk of the area to \(\int_a^{a+d}\) Sometimes that is not appropriate, when we really need more points close to a. Without knowing the shape of curve, peak for d is the safe choice. Quote:Albert, you have the following "magic numbers" hardcoded without explanation: With quadratic fit, last two points cannot be too flat, otherwise tangent line would be way off. I had based on \(\int_0^∞ e^{mx}\,dx\), with optimal d ≈ 6/m to 7/m. Let f(x) = exp(m*x), g(x) = f(x)*x With g(2x)/g(x) ≈ 85%, we considered it just passes the peak. f(2x)/f(x) ≈ 0.85/2 = 0.425, or reverse direction: 1/0.425 = 2.353 With 85% setting, xintercept roughly hit the sweet spot. lua> Q = require 'quad' lua> F = function(m) return function(x) return exp(m*x) end end lua> for m=1,9 do p,x=Q.peak(F(m)); print(m, m*x) end 1 5.734205371025561 2 5.734205371025561 3 4.41325021172599 4 5.734205371025561 5 4.509031895026867 6 4.41325021172599 7 8.628727480200146 8 5.734205371025561 9 4.841791367031226 

04302021, 05:13 PM
Post: #75




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
(04292021 11:38 PM)Albert Chan Wrote: Without knowing the shape of curve, peak for d is the safe choice. This is work in progress, but if we got peak and both xintercepts, we have an idea of curve shape. Code: function Q.peak2(f, a, d)  find peak of f(a+x)*x, guess=d lua> f = function(x) return log(1+9*(x*x))/(1+16*(x*x)) end  previous post example lua> Q.peak2(f1) 0.9456442962396107 2.580895653494933 3.5331822306621703 Peak for f(x)*x, at x ≈ 0.9456 The other 2 numbers are xintercepts, in log2 scale, relative to peak: Leftside xintercept ≈ 0.9456 * 2^2.581 ≈ 0.158 Rightside xintercept ≈ 0.9456 * 2^3.533 ≈ 10.9 We should pick the steeper side (xintercept with minimum log2 absolute value) If there is no steeper side, curve is relatively symmetrical, just use peak for d. Recommended d = leftside = 0.158  Both of these has peak at 1.0 ... which side to use for d ? lua> f1 = function(x) return exp(x) end lua> f2 = function(y) return f1(1/y)/y^2 end lua> Q.peak2(f1) 0.932573172731174 2.983219913988427 2.6203047422648424 lua> Q.peak2(f2) 1.072301916075236 2.6203047422648424 2.9832199139884263 Noted the symmetry of f1, f2 intercepts. This is expected, f2 ≡ f1, with x=1/y, dx=dy/y² Again, picking the steeper side, symmetry is carried to optimal d: f1 optimal d ≈ 2^2.62 ≈ 6.15 f2 optimal d ≈ 2^2.62 ≈ 0.163 

06282021, 01:05 AM
Post: #76




RE: (PC12xx~14xx) qthsh TanhSinh quadrature
I've finalized the "Improving the Double Exponential Quadrature TanhSinh, SinhSinh and ExpSinh Formulas" article (link to PDF here) based on the discussions and shared findings in this thread. A lot has been said on this thread, so not everything could (or should) be put in the article. This thread has it all and some more
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