R help - Dec 2007 - using solve.qp without a quadratic term

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 quadratic term in my
objective function. So, I was wondering if
it was possible to use solve.QP when there isn't
a quadratic term in the objective function.  

I imagine that there are other functions in R that can be used but I would like
to use solve.QP because, in my real problem,
I will have a lot of fairly complex constraints
and solve.QP provides a very nice way for implementing
them. Maybe there is another linear solver that allows you to implement hundreds
of constraints just solve.QP that I am unaware of ? Thanks for any suggestions.

# IN THE CODE BELOW, WE MINIMIZE 

# -3*b1 + 4*b2 + 6*b3

# SUBJECT TO

# b1 + b2 + b3 >=0
# -(b1  b2 + b3) >= 0 
# IE : b1 + b2 + b3 = 0.

Dmat <- matrix(0,3,3)          # QUADRATIC TERM
dvec <- c(-3,4,6)              # LINEAR TERM
Amat <- matrix(c(1,-1,0,1,-1,0,1,-1,0),3,3)

#print(Amat)

bvec = c(0,0,0)                  # THIRD ZERO IS SAME AS NO CONSTRAINT

result <- solve.QP(Dmat, dvec, Amat)

>>>>> "ML" == Mark Leeds <markleeds at
verizon.net>
>>>>>     on Fri, 21 Dec 2007 19:38:19 -0600 (CST) writes:
    ML> 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  ).
    ML> But, when I try to use solve.QP to solve it, I get the error that D
in the quadratic function is not positive
    ML> definite. This is because Dmat is zero
    ML> because I don't have a quadratic term in my
    ML> objective function. So, I was wondering if
    ML> it was possible to use solve.QP when there isn't
    ML> a quadratic term in the objective function.  

    ML> I imagine that there are other functions in R that can be used but I
would like to use solve.QP because, in my real problem,
    ML> I will have a lot of fairly complex constraints
    ML> and solve.QP provides a very nice way for implementing
    ML> them. Maybe there is another linear solver that allows you to
implement hundreds of constraints just solve.QP that I am unaware of ? Thanks
for any suggestions.

    ML> # IN THE CODE BELOW, WE MINIMIZE 

    ML> # -3*b1 + 4*b2 + 6*b3

    ML> # SUBJECT TO

    ML> # b1 + b2 + b3 >=0
    ML> # -(b1  b2 + b3) >= 0 
    ML> # IE : b1 + b2 + b3 = 0.

So you want to solve a *linear* programming problem,
not a quadratic.  Linear is typically considerably easier.

The recommended (and hence always installed) package 'boot'
has function simplex() to do this
and I see two other CRAN packages 'linprog' and 'lpSolve' also
for the same problem;  since ?simplex says that it may not be
very efficient for large problems, you would e.g. lpSolve
instead.

Regards,
Martin

    ML> Dmat <- matrix(0,3,3)          # QUADRATIC TERM
    ML> dvec <- c(-3,4,6)              # LINEAR TERM
    ML> Amat <- matrix(c(1,-1,0,1,-1,0,1,-1,0),3,3)

    ML> #print(Amat)

    ML> bvec = c(0,0,0)                  # THIRD ZERO IS SAME AS NO
CONSTRAINT

    ML> result <- solve.QP(Dmat, dvec, Amat)

    ML> ______________________________________________
    ML> R-help at r-project.org mailing list
    ML> https://stat.ethz.ch/mailman/listinfo/r-help
    ML> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
    ML> and provide commented, minimal, self-contained, reproducible code.