Say I have 3 parameters x1, x2 and x3, where x1, x2 and x3 can all take integer values between 0 to 9. I use GA to minimize an objective function f(x1,x2,x3), but the values of x1, x2 and x3 obtained should be integers. Any R-based implementation of GA with such a function to obtain integer values instead of the usual real values would be helpful.
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How can I convert truth table of 4 variables into one 4:1 multiplexer
Here is an example how to convert it into 8:1 multiplexer
The truth-table can in fact be implemented with a 2-1 multiplexer:
A minimized expression for the function depicted by the truth-table is
Y = X1 X3 + X3' X4
In words:
Output Y is X1 for X3 true. For X3 false, Y is X4
Input X3 selects either X1 or X4 to be forwarded to output Y.
Input X2 is ignored (as don't care).
You could use a 4-1 multiplexer and set the lower select input to X3, while the higher select input is set to constant 0.
A logic circuit is given two 2-bit binary numbers A and Bas its inputs. The circuit consists of two outputs Y1 and Y2. The output values of Y1 and Y2 are obtained as follows:
If A<B, then Y1 and Y2 will be equal to A-B. Else Y1 and Y2 will be equal to A.
How To Determinate truth table for this
There are two inputs A[0:1] and B[0:1], 4 inputs in total. Your truth table will have 16 inputs(rows).Are Y1 and Y2 2-bit outputs and is it magnitude of A-B? If yes, The left two columns can be A and next two will be B. For 6 of these rows from 16 cases, A<B in 6 cases ([A,B] = {[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]}). these six rows will have Y1 = Y2 = [B-A]. All other rows will have Y1 = Y1 = A input. Seems straightforward, but I may be missing something here.
Warren’s Abstract Machine A Tutorial Reconstruction states the following for Variable Register Allocation rules:
Variable registers are allocated according to least available index.
Register X1 is always allocated to the outermost term.
A same register is allocated to all the occurrences of
a given variable.
Further in the tutorial, while building program queries, the following example is given:
p(f(X), h(Y, f(a)), Y).
X1 = p(X2, X3, X4)
X2 = f(X5)
X3 = h(X4, X6)
X4 = Y
X5 = X
X6 = f(X7)
X7 = a
My doubt is when considering the two occurrences of the f clause, both are f/1 structures, but with a different body and therefore needed to be instantiated differently. But what exactly is considered a variable in the WAM context, a prolog variable, or every term? How would the clause p(f(a), f(a)) be constructed:
X1 = p(X2, X2)
X2 = f(X3)
X3 = a
or
X1 = p(X2, X3)
X2 = f(X4)
X3 = f(X4)
X4 = a
Given a list of size 2n -1 elements and the list looks like this:
x1, x2, x3, ....., xn, y1, y2, y3, ....y(n-1)
Convert it to:
x1, y1, x2, y2, x3, y3, ........., y(n-1), xn
I can use two iterators for each of the lists and get the solution in O(n) time complexity and O(n) space complexity. But if my n was very large, is there a way to do this in lesser space complexity?
It feels like this can be done with O(1) space and O(n) time but the algorithm is far from trivial. Basically take an element that is out of place, say x2, look where it needs to be in the final arrangement take out the element that is there (i.e. x3) and put in x2.
Now look where x3 needs to go and so on.
When the cycle is closed, take the next element that is out of place (if there is any).
Lets do an example:
x1 x2 x3 y1 y2 x2 is out of place so take it into temp storage
x1 -- x3 y1 y2 temp: x2 needs to go where x3 currently is
x1 -- x2 y1 y2 temp: x3 needs to go where y2 currently is
x1 -- x2 y1 x3 temp: y2 needs to go where y1 currently is
x1 -- x2 y2 x3 temp: y1 needs to go into the empty slot
x1 y1 x2 y2 x3 all elements in place -> finished
If the array indices start at 0, the final position of the element at k is given by
2k if k < n
2(k-n) + 1 if k >= n
The difficulty is to find out an element of a cycle that is not yet handled. For example if n = 4 there are 3 cycles:
0 -> 0
1 -> 2 -> 4 -> 1
3 -> 6 -> 5 -> 3
I do not have an easy solution for that at the moment.
If you have one bit of storage available per array element it is trivial but then we are back to O(n) storage.
In Python:
lst = 'x1 x2 x3 x4 x5 y1 y2 y3 y4 y5'.split()
lst
Out[9]: ['x1', 'x2', 'x3', 'x4', 'x5', 'y1', 'y2', 'y3', 'y4', 'y5']
out = sum((list(xy) for xy in zip(lst[:len(lst)//2], lst[len(lst)//2:])), [])
out
Out[11]: ['x1', 'y1', 'x2', 'y2', 'x3', 'y3', 'x4', 'y4', 'x5', 'y5']
I'm trying to set up a linear program in which the objective function adds extra weight to the max out of the decision variables multiplied by their respective coefficients.
With this in mind, is there a way to use min or max operators within the objective function of a linear program?
Example:
Minimize
(c1 * x1) + (c2 * x2) + (c3 * x3) + (c4 * max(c1*x1, c2*x2, c3*x3))
subject to
#some arbitrary integer constraints:
x1 >= ...
x1 + 2*x2 <= ...
x3 >= ...
x1 + x3 == ...
Note that (c4 * max(c1*x1, c2*x2, c3*x3)) is the "extra weight" term that I'm concerned about. We let c4 denote the "extra weight" coefficient. Also, note that x1, x2, and x3 are integers in this particular example.
I think the above might be outside the scope of what linear programming offers. However, perhaps there's a way to hack/reformat this into a valid linear program?
If this problem is completely out of the scope of linear programming, perhaps someone can recommend an optimization paradigm that is more suitable to this type of problem? (Anything that allows me to avoid manually enumerating and checking all possible solutions would be helpful.)
Add in an auxiliary variable, say x4, with constraints:
x4 >= c1*x1
x4 >= c2*x2
x4 >= c3*x3
Objective += c4*x4