similar to: Quadratic Programming

I hope that someone can help me with the following question: I would like to solve the Markowitz optimization problem WITH short-sale constraints. Maybe a procedure to solve a quadratic optimization problem with convex constraints and positive variables is already implemented in R? Thank you very much, edg -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

Dear R users, Simply my question is that how it is possible to generate some random numbers using rnorm( ) but in log transformed values. Thank you, Tobias --------------------------------- [[alternative HTML version deleted]]

Hi, I need to maximize a quadratic function under constraints in R. For minimization I used solve.QP but for maximization it is not useful since the matrix D of the quadratic function should be positive definite hence I cannot simply change the sign. any suggestion ? thanks -- View this message in context:

Hi everybody, consider please an optimization problem: minimize sum S1+S2 Subject to : y - x =< A + S1 x - y =< A + S2 and we want to add two more constraints: y - x =< B - S3 x - y =< B - S4 where A is a small constant value and B is a large constant value, S1 and S2 are surplus and S3

Hello. I'm trying to solve a quadratic programming problem of the form min ||Hx - y||^2 s.t. x >= 0 and x <= t using solve.QP in the quadprog package but I'm having problems with Dmat not being positive definite, which is kinda okay since I expect it to be numerically semi-definite in most cases. As far as I'm aware the problem arises because the Goldfarb and Idnani method first

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

Recently, a package for convex optimization was announced for Python, based upon the LP solver GLPK, the SDP solver in DSDP5, and the LP and QP solvers in MOSEK. I'm aware GLPK is available for R, but wondered if anyone had good packages for convex optimization along these lines for R. TIA.

Hi, I'm quite new at R and I haven't found the answer to my question anywhere on the net, so either it is trivial or not documented. So, bare with be. I am using the quadprog package and its solve.QP routine to solve and quadratic programming problem with inconsistent constraints, which obviously doesn't work since the constraint matrix doesn't have full rank. A way to solve this

Hi, Does anybody have experience to solve an quadratic programming problem with quadratic constraints in R? It seems that the package "quadprog" only handles the quadratic programming with linear constraint. My probelm is to maximze x^T\Sigma_{xy} y, subject to x^Tx=1, y^T\Sigma_{yy} y=1, and sum(y)<t, or sum(y)=t, where x and y are the variable, and the Sigma's and t are

HI All, I am unable to solve a optimization Problem Please Help Me out of this to solve. The Optimization problem is as follows :- My objective function is linear and one of the constraint is quadratic. Min z = 5 * X1 + 9* X2 + 7.15 *X3 + 2 * X4 subject to X1 + X2 + X3 +X4 = 9 X1 + X4 < = 6.55 X3(X3 - 3.5) >=0 X1,X2,X3,X4 >=0 Now the problem is how to solve this kind of

I was playing around with a simple example using solve.qp ( function is in the quadprog package ) and the code is below. ( I'm not even sure there if there is a reasonable solution because I made the problem up ). But, when I try to use solve.QP to solve it, I get the error that D in the quadratic function is not positive definite. This is because Dmat is zero because I don't have a

-----Original Message----- Message: 6 Date: Mon, 20 Jan 2003 01:11:24 +0100 From: Martin Michlmayr <tbm at cyrius.com> To: r-help at stat.math.ethz.ch Subject: [R] quadratic trends and changes in slopes I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in

Hi R experts, Could you please help me to fit a linear, cubic and quadratic curve in a figure? I was trying to show all these three fitting curves with different colour in one figure. I spent substantial time to figure it out, but I could not. I have given here a example and what I did for linear, but no idea for cubic and quadratic fitting curve > dput(test) structure(list(sp = c(4L, 5L,

Hi, I'm using the package quadprog to solve the following quadratic programming problem. I want to minimize the function (b_1-b_2)^2+(b_3-b_4)^2 by the following constraints b_i, i=1,...,4: b_1+b_3=1 b_2+b_4=1 0.1<=b_1<=0.2 0.2<=b_2<=0.4 0.8<=b_3<=0.9 0.6<=b_4<=0.8 In my opinion the solution should be b_1=b_2=0.2 und b_3=b_4=0.8. Unfortunately R doesn't find

Dear R Users , As a beginner in QP, I'm trying to solve a Support Vector Machine problem by a QP. In particulare I am using the quadprog package. My questions are here: 1- In the document for the package (The quadprog Package), the inequality constraint is mentioned with >= , however in a standard QP, this usaully is written with <= . This constraint should be multiplied by a

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

Hi R, A quick question.... How can I optimize the objective function constrained to quadratic constraints? Which function of R is useful for quadratic constraints? Many Thanks, Shubha This e-mail may contain confidential and/or privileged i...{{dropped:13}}

Good day for all, I'm a beginner aRgonaut, thus I'm having a problem to plot a quadratic model of regression in a plot. First I wrote: >plot(Y~X) and then I tried: >abline(lm(Y~X+I(X^2)) but "abline" only uses the first two of three regression coefficients, thus I tried: >line(lm(Y~X+I(X^2)) but a message error is showed ("insufficient observations").

Hello, I have a system of quadratic equations (results of a Hamiltonian optimization) which I need to find the roots for. Is there a package and/or function which will find the roots for a quadratic system? Note that I am not opimizing, but rather solving the first order conditions which come from a Hamiltonian. I am basically looking for something in R that will do the same thing as fsolve in

I have an equation describing the best-fit model for a set of points (just 2 axes) that is in the form: y=b+mx+px^2 Where b is the intercept, m is the slope describing a linear term, and p is a slope of the quadratic term. I would like to plot this equation on a curve (I know the equation is y=(.1766x^2)+(.171x)+.101) on the original scatterplot. Any easy way to plot this equation and