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These are questions asked by MrMarc

Ok, I want to solve the below code as fast and efficient as possible.
Since it is an LP problem LPSolve(Matrix Form) should be the best.

The problem is that the default objective function in LPSolve(Matrix Form)
is c'x which creates a bit of a problem since my objective function is not a
function of x. My objective function is simply ur + dr. How can I solve that?!


In Igor's "Multivariate Distributions in Maple"
he shows how to calculate the Multivariate Normal Distribution. Below I have provided some data
for expected values, covariances etc for a 3 random variable distribution.

Now I try to find the Quantile (with probability 0.05) for such a distribution however Maple fails.
How can I find the Quantile (with probability 0.05...

I am "playing" around with the Maplet Builder and I am having some problems:
Let say I have three separate vector columns with data loaded in maple ie A, B and C.

i) I want the user of the maplet to be able to select ie highlight a vector from the three
available vectors which are presented in a drop down box.

ii) A button when clicked on plots the data in the selected vector.

How can I do this with the Maplebuilder??  I will need step-by-step instructions.

I want to minimze VaR (the 5th percentile for the return distribution) with LP.
However, the way I have specified the problem does not work for some reason, why?


nstock := 20:
n := 15:

R := RandomMatrix(n, nstock, generator = -15 .. 15, outputoptions = [datatype = float[8]]):
W := Matrix(nstock, 1, [seq(w[i], i = 1 .. nstock)]):
data1 := Array(Multiply(R, W));

Ok, when I run the below code which maximize the risk adjuested portfolio returns
(long and short positions) in QP matrix form on empirical data I get very strange
allocations ie we go 100% or 100% short in almost all stocks except for a few
where the allocations are more appropriate like 0.2 etc.

# Maximize Risk Adjuested Return Matrix Form
# Minimize W'.Cov.W−W'.EV
# R=Return Matrix

EV := Vector([seq(ExpectedValue(Column(R, i)), i = 1 .. N)], datatype = float[8]):

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