search for: lagrang

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 potest" - (Lycklig är den som hjälper andra) Dr. Se...

I'm curious to find out if there is an example of R code for optimization of two variable function, with contraints, using lagrange multiplier (using optim/nlm?). I have a problem that contains one discrete variable, but need a simple problem/example to start with. I haven't been able to find any examples and thought I should ask here before I plunged into writing a few miles of R code. Thanks, Jeff. -- Jeff D. Hamann...

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: http://r.789695.n4.nabble.com/functions-for-polynomial-and-rational-interplation-tp3595334p3595334.html Sent from the R help mailing...

...ve.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 way in help(solve.QP). Any thoughts? Thanks. Jorge [[alternative HTML version deleted]]

...Samba logs. Now that we have his Office configured properly, it's not an issue, but I'm at a loss for an explanation, and the NT guys here are laughing up their sleeves at this perceived security hole in Linux/Samba. Thanks very much for any clues. -- Bill Grzanich IT Manager ORGANICS/LaGrange, Inc.

...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, I’d like to ask the experts whether there is any other alternative I could use to solve this. For example, can I define a Lagrange function (add lambda as a parameter) and use optim() or any other optimization function? Many thanks in advance for your help. Lars. [[alternative HTML version deleted]]

Hello I''d like to know if there''s a way to minimize a non linear function in R subject to some linear restrictions Ax=b I have tried to use optim() but it doesn''t seem to be able to do it. Or, by Lagrange multipliers, solve some non linear equations simultaneously. Anyone can help? Thanks -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]...

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. Does anybody know how this test can be called. Vincent Spiesser

Dear all R users, I am wondering if there are any function for Constraint optimization in R. Especially i am looking for a R - equivalent of "fmincon" function in MATLAB. Thanks and regards, Arun [[alternative HTML version deleted]]

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]]

...pectively. 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 ranges from 0 to 1, and both f1 and f2 involve integrations. I tried to use lagrange multipliers to eliminate two equality constraints and then use optim() to find the maximum value and optimal parameter estimates. So I let fn be f1+lambda1*(c- k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3))) + lambda2(p4-f2(p1,p2,p3)). The error message I got was "Error in fn(par, ...) : recursive defaul...

...; it is part of an NT domain, but is not acting as a domain controller. Authentication is provided by an NT4 box. Perhaps the only solution is to upgrade to 2.2.3a? If so, are there any worries about migrating on a Red Hat 6.2 platform? Thanks in advance. -- Bill Grzanich IT Manager ORGANICS/LaGrange, Inc.

I am going to improve theora codec with dynamically changing way. In this case we want to change compression parameters like video_q, sharpness when a keyframe is generated. When i set video quality parameter using cpi-> pb.info.quality in CommpressKeyFrame in encoder_toplevel it will not change dynamically. Can you please help me to do this. Wich function should i cange to achieve my

Dear experts, I'm attempting to solve a constrained optimization problem using the Rdonlp2 package. I created a Lagrange function (L=f(x)-lambda(g(x)-c)), where x is a vector of 16 parameters. This is what I'm using as objective function in the code below. In addition, I set bounds on these parameters (par.u and par.l). When I run the code, I get the error message shown below. Any idea why or what does it mean?...

...onse. Now I'd like to compute the mixture corresponding the maximum response. Model for Y1 has the parameters p1=124.02 p2=60.973 p3=41.479 p12=106.824 p13=140.422 p23=81.012 Solving a system of linear equations (solve(A,b)) of the partial derivates and putting a constraint of x1+x2+x3=1 with Lagrange's multiplier finds the mixture x1=0.760208 x2=0.08256986 x3=0.15722207 that produces the maximum response 130.378 within the experimental region. Now, my model for another response Y2 has the parameters p1=35.8147 p2=42.1012 p3=51.7479 p12=19.9611 p13=0.2531 p23=-6.6498 Solving as above w...

...sum the problem up: one has a binary star system with a large star (e.g. red giant) and a small star (e.g. white dwarf). Gravitation between them is directly proportional to the mass and indirectly proportional to the square of the distance. If correctly plotted, one should be able to see the inner lagrange point L1 which is the point where the gravitational potentials of the stars "cancel out", e.g. an object would not be attracted to any star. Well, enough background information, here's my rookie code - please feel free to comment on anything :-) Kind regards, Johannes star1center...

Hi, I want find all roots for the following polynomial : a <- c(-0.07, 0.17); b <- c(1, -4); cc <- matrix(c(0.24, 0.00, -0.08, -0.31), 2); d <- matrix(c(0, 0, -0.13, -0.37), 2); e <- matrix(c(0.2, 0, -0.06, -0.34), 2) A1 <- diag(2) + a %*% t(b) + cc; A2 <- -cc + d; A3 <- -d + e; A4 <- -e fn <- function(z) { y <- diag(2) - A1*z - A2*z^2 - A3*z^3 - A4*z^4

...0.0152 7.35 2.2e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Total Sum of Squares: 28100 Residual Sum of Squares: 27900 F-statistic: 53.954 on 1 and 13187 DF, p-value: 2.17e-13 > plmtest(x, "individual") Lagrange Multiplier Test - (Honda) data: ibes1y.meanest ~ employee_kld normal = 1675.7, p-value < 2.2e-16 alternative hypothesis: significant effects ##fitting a within or random model fails > x <- plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model="within") Error in...

Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

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 Mo.v...