search for: constrains

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 (

Which optimization did you find unsafe? Thanks, --Serge ср, 21 авг. 2019 г. в 05:12, Cameron McInally <cameron.mcinally at nyu.edu>: > On Tue, Aug 20, 2019 at 1:02 PM Serge Pavlov via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > >> Hi all, >> >> During the review of https://reviews.llvm.org/D65997 >>

On Fri, Jan 17, 2020 at 8:09 PM Finkel, Hal J. <hfinkel at anl.gov> wrote: > Andy, thanks for writing this up. A few thoughts: > > 1. The mental model that I have is that there is always an FP_CONTRACT pragma: there's some default (implicit) pragma at the beginning, and what it says (off/on/fast) is controlled by the command-line flags (or the driver's default if no flags

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

> > The only issue I see is that since we also assume FP operations have no > side effects by default there is no difference between llvm.rint and > llvm.nearbyint. I wouldn’t have a problem with dropping llvm.rint > completely. The forthcoming C standard ( http://www.open-std.org/jtc1/sc22/wg14/www/docs/n2454.pdf, 7.12.9.8) defines new function, `roundeven`, which implements

Hello, I've searched for an answer to no avail. I am wondering if anyone knows how to constrain certain correlations to be equal. I have family data with 2 twins per family plus up to 2 siblings. I would like to somehow constrain all the sibling correlations (twin-sib and sib-sib) to be the same while allowing the twin-twin correlation to be different. Here is some simulated code:

> > 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 > roundeven library function since that wouldn't exist in older libm > implementations. So ideally you would know which function was originally > used in the

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

Some clarification after getting feedback from Craig Topper…. It’s probably best to say in the documentation that the llvm.nearbyint and llvm.rint functions “assume the default rounding mode, roundToNearest”. This will allow the optimizer to transform them as if they were rounding to nearest without requiring backends to use an encoding that enforces roundToNearest as the rounding mode for these

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

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]]

> > I'm not sure why this is an issue. Yes, these two intrinsics depend > on the current rounding mode according to the C standard, and yes, > LLVM in default mode assumes that the current rounding mode is the > default rounding mode. But the same holds true for many other > intrinsics and even the arithmetic IR operations like add. Any other intrinsic, like `floor`,

...uite 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 advance, Paul

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). But if

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 contain any info on constrained fitting. Does anyone

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.

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:

Philip Reames <listmail at philipreames.com> writes: > I was not suggesting that you rely on pattern matching predication for > correctness.  As you point out, that's obviously incorrect.  I was > assuming that you have a correct but slow lowering for the select > form.  I was suggesting your ISEL attempt to use a predicated > instruction where possible for performance.