search for: constrained

Hi all, During the review of https://reviews.llvm.org/D65997 an issue was revealed, which relates to the decision of how compiler should represents constrained floating point operations. If a floating point operation requires rounding mode or exception behavior different from the default, it should be represented by constrained intrinsic ( http://llvm.org/docs/LangRef.html#constrained-floating-point-intrinsics). An important point is that according to th...

...MFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=O_4M49EtSpZ_-BQYeigzGv0P4__noMcSu2RYEjS1vKs&m=fTfAlQ0FHnQez3xiw8VnBL1XaxmBqn_-WD5E0mh4GrY&s=muQ0CzykdJ9Nhg0UshJTnEQXPSKHdFGptZXwFvVD2l0&e=> >> an issue was revealed, which relates to the decision of how compiler should >> represents constrained floating point operations. >> >> If a floating point operation requires rounding mode or exception >> behavior different from the default, it should be represented by >> constrained intrinsic ( >> http://llvm.org/docs/LangRef.html#constrained-floating-point-intrinsics...

...there-is-a-pragma and there-is-not-a-pragma cases in some fundamental way. > > 2. I'm inclined to go with your choice (b) above because I think that we should treat these concepts as orthogonal Agreed. > (to the extent that is reasonable: by design, we don't want to reassociate constrained operations, so that flag just might have on effect on those intrinsics). This lets the later optimization passes decide how to treat the various combinations of flags and intrinsics (just as with all other intrinsics that might be present). I think I agree, but this needs clarification. My view is...

Hi all, A question came up in a code review (https://reviews.llvm.org/D72820) about whether or not to allow fast-math flags to be applied to constrained floating point intrinsics (http://llvm.org/docs/LangRef.html#constrained-floating-point-intrinsics). This has come up several times before, but I don't think we've ever made a decision about it. By default, the optimizer assumes that floating point operations have no side effects and use t...

...implements IEEE-754 operation `roundToIntegralTiesToEven`. When corresponding intrinsic will be implemented (I am working on such patch), llvm.rint and llvm.nearbyint will identical to llvm.roundeven in default environment and both can be dropped. We'll end up with a funny situation, there are constrained intrinsics (experimental!) but not corresponding 'usual' intrinsics. This demonstrates that splitting an operation into constrained and non-constrained variants does not work in the case of `rint` and `nearbyint`. As for the target-specific intrinsics, you are correct that we need a plan &...

...Twin1 Twin2 Sib1 Sib2 Twin1 1.00000 0.12027 0.02286 -0.10578 Twin2 0.12027 1.00000 0.08118 -0.08470 Sib1 0.02286 0.08118 1.00000 -0.05064 Sib2 -0.10578 -0.08470 -0.05064 1.00000 So I want these five correlations: 0.02286 -0.10578 0.08118 -0.08470 -0.05064 to be constrained to be the same value. My actual data is much more complicated than this, needing to constrain many different classes of parent-offspring and spousal correlations. Or is there some simpler alternative - e.g., transforming the correlations to a Fisher z and doing a weighted sum? Thanks in advance,...

...tions. You can't have > static rounding with exceptions. > > We're also talking about making the vector predicated floating point > intrinsics that Simon Moll is working on support both strict and non-strict > using operand bundles. So you're right we could probably merge constrained > and non-constrained versions of the existing intrinsics. > > One concern with replacing llvm.rint and llvm.nearbyint with > llvm.roundeven makes it difficult to turn back into a libcall if the > backend doesn't have an instruction for it. You can't just call the > rounde...

Hi: I am trying to implement interval arithmetic through llvm. I have a problem with the rounding mode with llvm.experimental.constrained.cos I have two pieces of codes: ; Function Attrs: norecurse nounwind readnone ssp uwtable define double @cosine_down(double returned) local_unnamed_addr #0 {   ; call the llvm intrinsic to perform downward cosine   %2 = call double @llvm.experimental.constrained.cos(double %0, metadata !"rou...

...nt to continue using the current encoding which tells the processor to use the current rounding mode. For other targets (including cases where x86 is forced to use x87 instructions), it may be much easier to leave this at the discretion of the backend. Also, we should take care to document the non-constrained forms of these intrinsics in a way that makes clear that we are “assuming” and not requiring that the operation has no side effects. For the constrained version of nearbyint, we will require that the inexact exception is not raised (to be consistent with iEEE 754-2019’s roundToIntegral operations)...

Dear list, The task view on optimization does not reference a package for non linear constrained optimization problems. Stefan Theussl told me to look at the Rsolnp package, but unfortunately it is not very clear what method is R ported. (The authors ported the matlab code of Yinyu Ye http://www.stanford.edu/~yyye/ <http://www.stanford.edu/%7Eyyye/>) Currently I'm looking for an imp...

Dear R-Users, Being an engineer and not a statistician, my desired course of action may either be impossible or very simple. I am attempting to fit a non-linear model to some measured data. One term in the model contains a square-root, but in the course of regression, this term turns negative and an error occurs. I started using Micrsoft's Excel Solver, and then I turned to NIST's

Hello everyone, I have 3 variables Y, X1 and X2. Each variables lies between 0 and 1. I want to do a constrained regression such that a>0 and (1-a) >0 for the model: Y = a*X1 + (1-a)*X2 I tried the help on the constrained regression in R but I concede that it was not helpful. Any help is greatly appreciated -- Thanks, Jim. [[alternative HTML version deleted]]

...` can be used instead. We could use more general intrinsics in all cases, as the special case of default environment is not of practical interest. There is another reason for special handling. Set of intrinsics includes things like `x86_sse_cvtss2si`. It is unlikely that all of them eventually get constrained counterpart. It looks more natural that such intrinsics are defined as accessing FP environment and can be optimized if the latter is default. These two intrinsics could be a good model for such cases. IIUC, splitting entities into constrained or non-constrained is a temporary solution, ideally the...

Dear All I am dealing at the moment with optimization problems with nonlinear constraints. Regenoud is quite apt to solve that kind of problems, but the precision of the optimal values for the parameters is sometimes far from what I need. Optim seems to be more precise, but it can only accept box-constrained optimization problems. I read in the list archives that optim can also be used with nonlinear constrains through penalizations. However, I am not familiar with the technique of penalizations. Could someone please indicate to me a site or a book to learn about that penalization technique? Thanks in...

This is fundamentally different than the problems we’re trying to handle with the constrained FP intrinsics. The constrained FP intrinsics were necessary because LLVM IR otherwise assumes that FP instructions do not have side effects. In the case of integer divide-by-zero, the optimizer generally recognizes this as a possibility and avoids introducing it (through speculation, for instance)....

I wonder whether R has methods for constrained fitting of linear models. I am trying fm<-lm(y~x+I(x^2), data=dat) which most of the time gives indeed the coefficients of an inverted parabola. I know in advance that it has to be an inverted parabola with the maximum constrained to positive (or zero) values of x. The help pages for lm do not...

Hi All, I have two questions: I am computing a linear regression model with 0 as Intercept. Well, I would like the sum of my predicted values be equal to a constant and therefore analyze if my coefficients are significatively different using or not this constraint. Does anyone know how I can constrain my model in a such way? Here is the code: data<-read.table ("input.txt",

Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly. Thanks, Spyros

Hi: Sorry I need to send email directly. I am new to llvm and trying to write interval arithmetic, which requires changing rounding mode during computation. The document I found https://llvm.org/docs/LangRef.html#constrained-floating-point-intrinsics, seems to be doing the trick. Here is the piece of code that I did: %5 = call double @llvm.experimental.constrained.fadd(double %0, double %2, metadata !"round.downward", metadata !"fpexcept.ignore") It is taking the first and third argument and addin...

...IR support is the most reasonable way to get correctness and performance. How should we translate this to get predicated instructions out? for (int i=...) { if( fabs(c[i]) > epsilon) { a[i] = b[i]/c[i]; } else { a[i] = 0; } } We can't use select even with constrained intrinsics, because the constrained intrinsics only tell the optimizer they can't be speculated. This is not a legal translation: %cond = fabs(c[i]) > epsilon %temp = select %cond, llvm.experimental.constrained.fdiv(b[i], c[i], tonearest, maytrap), 0 store a[i], %temp Accordi...