SpG == Spencer Graves [EMAIL PROTECTED]
on Fri, 01 Dec 2006 17:29:56 -0800 writes:
SpG Unless I'm missing something, optimizing a linear
SpG function with quadratic constraints is almost trivial
SpG with Langrange multipliers.
yes. Good point, let's hope we're not solving
On Sat, 2 Dec 2006, Martin Maechler wrote:
SpG == Spencer Graves [EMAIL PROTECTED]
on Fri, 01 Dec 2006 17:29:56 -0800 writes:
SpG Unless I'm missing something, optimizing a linear
SpG function with quadratic constraints is almost trivial
SpG with Langrange multipliers.
Hi, Prof. Ripley:
snip
But that is a single equality quadratic constraint, and I believe
'quadratic constraints' (note, plural) conventionally means multiple
inequality constraints. That meaning is a hard problem that needs
specialized software (most likely using interior-point
: Saturday, December 2, 2006 1:19:06 PM
Subject: Re: [R] Quadratic Optimization
Hi, Prof. Ripley:
snip
But that is a single equality quadratic constraint, and I believe
'quadratic constraints' (note, plural) conventionally means multiple
inequality constraints. That meaning is a hard problem
Unless I'm missing something, optimizing a linear function with
quadratic constraints is almost trivial with Langrange multipliers.
Maximize a'x subject to x'Ax=c.
S = Lagrange objective = a'x+lam*(x'Ax-c).
dS/dx = a + 2*lam*Ax.
Given lam, x1 = solve(A,
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
