defining simple optimization constraint: When x1 > 0 then x2 has to equal 0
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I'm working on an optimization problem with 2 decision variables x1 and x2. The problem optimizes the values of x1 and x2 each hour over a 24 hour period to minimize the objective function.
I've already defined 2 constraints as follows (using the code below):
0 <= x1 <= 3
0 <= x2 <= 3
lb = zeros(2*N,1); % lower bound is 0
ub = 3*ones(2*N,1); % upper bound is 3
Now, I'm trying to define a constraint that says that only x1 or x2 can be greater than 0 at any one time. i.e. If x1 is greater than 0 then x2 has to equal 0 and vice versa. In the problem x1 and x2 refer to the energy flow in and out of a battery, therefore only one variable can be positive at a time.
if x1 > 0 then x2=0
if x2 > 0 then x1 = 0
I'm scratching my head but I can't seem to figure out how to define this constraint in matrix form.
I'd welcome any suggestions and thank you for your help.
Matt J on 30 Aug 2013
Edited: Matt J on 30 Aug 2013
This is a classical application of Dynamic Programming and that might be the most efficient way for you to go about this problem, see
One thing I don't understand, though, is why you have two separate variables, x1 and x2 for Energy_in and Energy_out? Can't you just have one decision variable x in each hourly period and treat x>0 as energy out and x<=0 as energy in? In other words, can't you just view Energy_out as positive energy consumption and Energy_in as negative energy consumption?
More Answers (2)
Alan Weiss on 30 Aug 2013
You can represent this as a simple nonlinear constraint
sum(x1.*x2) = 0
However, the resulting problem will very likely be extremely sensitive to initial conditions.
Sometimes it is worth reformulating your problem as a lower-dimensional one, say
y = x1 - x2
where there is a lower bound of -3 on y and an upper bound of 3. If the solution y > 0 then x1 > 0 and x2 = 0. If y < 0 then x1 = 0 and x2 > 0. Of course, you need to reformulate your objective function in terms of y, not x1 and x2. And you have to be careful that the derivative of the objective function is smooth at y = 0, which might not be possible, or might require some smoothing.
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