similar to: Optimization with linear constraints

Hi there, I have a major problem (major for me that is) with solve.QP and I'm new at this. You see, to solve my quadratic program I need to have the lagrange multipliers after each iteration. Solve.QP gives me the solution, the unconstrained solution aswell as the optimal value. Does anybody have an idea for how I could extract the multipliers? Thanx, Serge "Beatus qui prodest quibus

Dear All, I couple of weeks ago, I’ve asked for a package recommendation for nonlinear optimization. In my problem I have a fairly complicated non-linear objective function subject to one non-linear equality constrain. I’ve been suggested to use the *Rdonlp2* package, but I did not get any results after running the program for 5 hrs. Is it normal to run this type of programs for hours? Also,

Dear all, I have two functions (f1, f2) and 4 unknown parameters (p1, p2, p3, p4). Both f1 and f2 are functions of p1, p2, and p3, denoted by f1(p1, p2, p3) and f2(p1,p2,p3) respectively. The goal is to maximize f1(p1, p2, p3) subject to two constraints: (1) c = k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3)), where c and k1 are some known constants (2) p4 = f2(p1, p2, p3) In addition, each parameter

Hello All, I would like to optimize a (log-)likelihood function subject to a number of linear constraints between parameters. These constraints are equality constraints of the form A%*%theta=c, ie (1,1) %*% 0.8,0.2)^t = 1 meaning that these parameters should sum to one. Moreover, there are bounds on the individual parameters, in most cases that I am considering parameters are bound between zero

Hi Michael, A few comments 1. To add the constraint sum(wgt.vect=1) you would use the method of Lagrange multipliers. What this means is that in addition to the w_i (the components of the weight variables) you would add an additional variable, call it lambda. Then you would modify your optim.fun() function to add the term lambda * (sum(wgt.vect - 1) 2. Are you sure that you have defined

Hi, I need to solve an optimization problem in R having linear objective function and quadratic constraints(number of variables is around 80). What are the possible choices to do this in R. optim() function only allows box constrained problems. Is it possible in nlm()? Or please tell me if there is any other routine. Thanks Amit

Hello, First, sorry for my poor english, I will try to be understood. It's the first time I try this "r-help mailing list" and I hope it will be a success. I am working on heteroscedasticity analysis. I would like to get the "Box-Ljung" and the "Lagrange multipliers" test. I found the first one in the library "ts", but I can't find the second one.

Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

I'm having difficulty figuring out how to implement the following set of constraints in Quadprog: 1). x1+x2+x3+x4=a1 2). x1+x2+x5+x6=a2 3). x1+x3+x5+x7=a3 4). x1+x2=b1 5). x1+x3=b2 6). x1+x5=b3 for the problem: MIN (x1-c1)2+(x2-c2)2+...+(x8-c8)2. As far a I understand, "solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)" reads contraints using an element-by-element

Are there implementations of, e.g. Neville's algorithm, for interpolating polynomials through some data points? Nevilles' is an improvement on Lagrange interpolation. And how about interpolating rational functions? I could not find anything at rseek.org or at crantastic.org. thanks -- View this message in context:

Hi, I am using solve.QP (from quadprog) to solve a standard quadratic programming problem: min_w -0.5*w'Qw st ... I would like solve.QP to do two things: 1) to start the optimization from a user-supplied initial condition; i.e., from a vector w_0 that satisfies the constraints, and 2) to return the values of the lagrange multiplieres associated with the constraints. I did not find an obvious

Hello, I have a matrix equation, Ax=b, that I need to solve for x. x should be a vector of positive numbers (between 0 and 1). A is not a square matrix in general. This lead me to using the SVD. However, using the SVD gives me positive and negative numbers, as well. I have some constraints included in the A matrix itself (i.e., that the sum of some xi should be equal to 1) but I do not know how

Dear all, I need a suggest to obtain the max of this function: Max x1*0.021986+x2*0.000964+x3*0.02913 with these conditions: x1+x2+x3=1; sqrt((x1*0.114434)^2+(x2*0.043966)^2+(x3*0.100031)^2)=0.04; x1>=0; x1<=1; x2>=0; x2<=1; x3>=0; x3<=1; Any suggests ? Thanks in advanced, Massimiliano Questo messaggio di posta elettronica contiene informazioni di carattere

On Thu, May 3, 2018 at 2:03 PM, Michael Ashton <m.ashton at enduringinvestments.com> wrote: > Thanks Bert. But everyone on that forum wants to use finance tools rather than general optimization stuff! And I am not optimizing a traditional Markowitz mean-variance problem. Plus, smarter people here. :-) > I'm very confused by these statements. Most of the "finance tools"

Hello, All. We have Samba 2.0.7 running on Red Hat 6.2 (up for 351 days!) and have discovered the following anomaly: There is a share called "appsg" that contains a number of folders, including one called OfficeTemplates. The share definition in smb.conf is: [appsg] comment = Apps in Applications path=/home/applications/apps public = No read only =

Hi, I have the following hypothetical optimization problem: -det(t(x-A%*%x1)%*%(x-A%*%x1)) where A,x,x1 are matrices. A coefficients and x and x1 are variable matrices or vectors. I tried to apply optim and nlm functions but I kept receive the following massage: Error in A%*%x1 : non-conformable arguments. The massage appears even the -det() can be calculated and the dimensions are checked. here

Is there a simple method under R to solve an overdetermined system of linear equations Ax=b with A being a matrix m*n? I know that solve() seems to work for m*m matrices, but I had no luck with overdetermined systems. Mlod

Hi, Does anyone know if R has a Lagrange multiplier (LM) test for ARCH effects for univariant time series? Thanks! -- Tom [[alternative HTML version deleted]]

Hello, All. Is it possible to have a Samba 2.0.7 server provide printer drivers for both Windows 98 and 2000 clients? I'm currently delivering Win98 drivers with great success, but we're beginning to have Windows 2000 clients on the network and I've been unable to trick Samba 2.0.7 into providing the proper files. Note that the Samba server provides just file and print services;

Thanks Bert. But everyone on that forum wants to use finance tools rather than general optimization stuff! And I am not optimizing a traditional Markowitz mean-variance problem. Plus, smarter people here. :-) > On May 3, 2018, at 3:01 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > You can't -- at least as I read the docs for ?optim (but I'm pretty > ignorant