search for: combinatorials

This is not an R question, but a question on some combinatorial mathematics. Apologies for the OT if it is wildy inappropriate. The traditional C(n.k) method tells me how many combinations k I can make with n objects. However, suppose I want the number of combinations where an object cannot be used more than Q times where Q is a parameter that changes? For instance: combn(LETTERS[1:5], 3) shows

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Preallocation Instruction Scheduling with Register Pressure Minimization Using a Combinatorial Optimization Approach G. Shobaki, M. Shawabkeh and N. Abu-Rmaileh ACM Transactions on Architecture and Code Optimization (TACO). vol. 10, issue 3, Article 14 (Sept. 2013) http://dx.doi.org/10.1145/2512432 Regards Ghassan Shobaki, PH.D Assistant Professor Department of Computer Science Princess Sumaya

Hello! I have the following combinatorial problem. Consider the cumulative sums of all permutations of a given weight vector 'w'. I need to know how often weight in a certain position brings the cumulative sums equal or above the given threshold 'q'. In other words, how often each weight is decisive in raising the cumulative sum above 'q'? Here is what I do: w <-

Hi all I am trying to solve a combinatorial optimization problem. Basically, I can reduce my problem into the next problem: 1.- Given a NxN grid of points, with some values in each cell 2.- Find the combination of K points on the grid such that, the maximum mean value is obtained I took the Travel SalesMan problem example in ?optim documentation. I am not sure if I have understood correctly

Dear Community, I have a problem I am trying to code in R. Suppose there are 7 products and each have a ROI value attached to it. There are 400 representatives who calls 150,000 customers with these product detailing and achieve sales through the calls. There is a cost per call and revenue earned from the call. Constraint is revenue should be greater than the cost. Now there can be at most 7

Hi all, I'm currently writing some unit tests for some debug line error handling code I'm working on (see e.g. https://reviews.llvm.org/D44382), and I just ran into an annoying disabled feature in gtest, specifically the "Combine" feature for use in combinatorially generating parameterised tests. A FIXME comment in ProfileData\CoverageMappingTest.cpp suggests that I'm not

I have been struggling trying to write some code to produce all combinations subject to some restrictions. I thought someone might have some bright ideas. I have 11 values which fall into 5 groups. I want all combinations of 2,3, and 4 values where each value must be from a different group. The numbers in the groups are different. Here is a definition of the groups: groups <- list(gp1 =

+Chandler who might have some thoughts on this. Could you provide an example here of the motivation for the feature you're missing? Might help motivate the discussion (and/or we'll end up nitpicking how it could be done differently without that feature... - which is sort of where I'm going with this. Combinatorial test case expansion does seem a bit suspicious to me - I'd hope we

...faxed copy I'll gladly provide one. If you have Krebs _Ecological_Methodology_ it's on page 415 of the second edition. It's on page 27 of Gotelli and Graves _Null_Models_in_Ecology_, and in lots of other references as well. The problem is not the calculations of binary coefficients (combinatorials) per se-- I use lgamma(x+1) to do it with logs of factorials-- but rather the 1-(quotient of combinatorials) term in the first summation which forces me to exp() the log terms before the calculation is complete. This invariably causes an overflow (Inf) with all but the most unrealistically small d...

On Thu, Mar 15, 2018 at 11:10 AM David Blaikie via llvm-dev < llvm-dev at lists.llvm.org> wrote: > +Chandler who might have some thoughts on this. > FWIW, I have no concerns about updating to a modern googletest. More modern the better IMO if someone is willing to do the work to make sure it works on all our platforms, etc. However: > Could you provide an example here of the

Hi, I have a complex-valued vector X in C^n. Given another complex-valued vector Y in C^n, I want to find a permutation of Y, say, Y*, that minimizes ||X - Y*||, the distance between X and Y*. Note that this problem can be trivially solved for "Real" vectors, since real numbers possess the ordering property. Complex numbers, however, do not possess this property. Hence the

Run this: >p <- c('a', 'c', '', ''); a <- c(10, 20, 30, 40); d1 <- >data.frame(Promoter=p, ip=a) # Note duplicate empty names in p. >p <- c('b', 'c', 'd', ''); a <- c(15, 20, 30, 40); d2 <- >data.frame(Promoter=p, ip=a) >all <- merge(x=d1, y=d2, by="Promoter", all=T) >all <-

Hello, While reading Dan North''s BDD tutorial <http://dannorth.net/introducing-bdd>, I tried to implement his ATM example as spec stubs. When I first implemented it creating a context for each of his scenarios, I noticed that there is duplication and a combinatorial explosion of the specs. I attached the full files to this email. For brevity, I will use scenario 1 in the body of

Hi R users: In R for windows. is there any package to work, in experimental design? With functions like in S-PLUS , fact.design and the other ones. How to implement in R, all theory about experimental design, I'm talking about ssType3, fact.design and the theory of orthogonal design, combinatory...... Thanks in advance and please excuse me, my "european english". My e-mail is

Hola! I am programming a class (S3) "symarray" for storing the results of functions symmetric in its k arguments. Intended use is for association indices for more than two variables, for instance coresistivity against antibiotics. There is one programming problem I haven't solved, making an inverse of the index function indx() --- se code below. It could for instance return the

On 19 March 2018 at 19:56, Chandler Carruth <chandlerc at gmail.com> wrote: > On Thu, Mar 15, 2018 at 11:10 AM David Blaikie via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > >> +Chandler who might have some thoughts on this. >> > > FWIW, I have no concerns about updating to a modern googletest. More > modern the better IMO if someone is willing to do

On Thu, Mar 15, 2018 at 9:09 PM, David Blaikie via llvm-dev <llvm-dev at lists.llvm.org> wrote: > +Chandler who might have some thoughts on this. > > Could you provide an example here of the motivation for the feature you're > missing? Might help motivate the discussion (and/or we'll end up nitpicking > how it could be done differently without that feature... - which

Hello, In a previous email, Matthias mentioned that register pressure estimates in the machine scheduler are not absolute; they only account for the registers that are used in the block.I assume that he meant that registers that are live-through (both live-in and live-out) are not accounted for in register pressure calculations. If a register is either live-in or live-out but not both, it must be