Hello,
I am hoping someone can help tackle the problem below, for which I require a
fast solution. It feels like there should be an elegant approach, but I am
drawing blanks.
Take a vector 'x' with random values > 0:
x = runif(10,1,5)
Assume some reasonably small positive value 'delta':
delta = 0.75
The task is to find a solution vector 'y' of same length as 'x' such that:
The absolute difference between y[i] and y[i-1] is >= 'delta'
and y[i] >= x[i]
and that the sum of y[i] - x[i] be as small as possible -- i.e. minimize
sum(y-x).
The real-world application that (loosely) inspires this problem is the case of
thermal power plants that face limits ('delta') in the speed with which they
can "ramp" output up (or down) in response to changing demand. The
period-to-period difference in output cannot exceed the absolute value of
'delta'. The other constraints I've imposed are specific to my application, but
also provide a more neatly defined problem. A real-world problem would not have
random starting values for 'x', but I figure the random values will present a
particular difficulty in terms of solution time.
SPEED IS CRITICAL here, as this example must handle 'x' with length=10,000 in
practice and is located within an optimization routine that requires it be
iterated over different 'x' vectors many times. My Neanderthal-ish solution
(below) may or may not give the theoretically optimal solution, but,
regardless, is too slow when 'x' becomes lengthy due to its reliance on loops.
Hope you can help!
x = runif(10,1,5)
delta = 0.75
chg = diff(c(x,x[1]))
y = x
while (any(abs(chg)>delta)) {
temp = sign(chg)*chg - delta
temp1=temp
temp1[chg>=(-delta)] = 0
temp1 = c(temp1[length(temp1)],temp1[-length(temp1)])
temp2 = temp
temp2[chg<=delta] = 0
y = y+temp1+temp2
chg = diff(c(y,y[1]))
}
#Solution vector:
y
Thank you,
Kevin
Kevin Ummel
CARMA Project Manager
Center for Global Development
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