I am using AMPL for the optimization of my model, and just have started with that project.
I have two variables, say A and B that I utilize in my objective function:
A[d,t]*costA-B[d,t]*costB
Later on I have the following constraint:
G[d,t]-U[d,t]-R[d,t]=A[d,t]
Here I realized that I can use just A, but the problem is, depending on whether or not this variable will be positive or negative I should use costA or costB.
My question is, can I redefine A[d,t] as B[d,t] if A[d,t] is less than 0? And if I can, how can I do it? Or is there any other way?
I think what you are after is something like (in some math-like notation):
min sum((d,t), APlus[d,t]*CostA + AMin[d,t]*CostB)
s.t. A[d,t] = APlus[d,t]-AMin[d,t]
positive variables APlus,AMin
This is called "variable splitting".
Related
I am new to LabVIEW and I am trying to read a code written in LabVIEW. The block diagram is this:
This is the program to input x and y functions into the voltage input. It is meant to give an input voltage in different forms (sine, heartshape , etc.) into the fast-steering mirror or galvano mirror x and y axises.
x and y function controls are for inputting a formula for a function, and then we use "evaluation single value" function to input into a daq assistant.
I understand that { 2*(|-Mpi|)/N }*i + -Mpi*pi goes into the x value. However, I dont understand why we use this kind of formula. Why we need to assign a negative value and then do the absolute value of -M*pi. Also, I don`t understand why we need to divide to N and then multiply by i. And finally, why need to add -Mpi again? If you provide any hints about this I would really appreciate it.
This is just a complicated way to write the code/formula. Given what the code looks like (unnecessary wire bends, duplicate loop-input-tunnels, hidden wires, unnecessary coercion dots, failure to use appropriate built-in 'negate' function) not much care has been given in writing it. So while it probably yields the correct results you should not expect it to do so in the most readable way.
To answer you specific questions:
Why we need to assign a negative value and then do the absolute value
We don't. We can just move the negation immediately before the last addition or change that to a subtraction:
{ 2*(|Mpi|)/N }*i - Mpi*pi
And as #yair pointed out: We are not assigning a value here, we are basically flipping the sign of whatever value the user entered.
Why we need to divide to N and then multiply by i
This gives you a fraction between 0 and 1, no matter how many steps you do in your for-loop. Think of N as a sampling rate. I.e. your mirrors will always do the same movement, but a larger N just produces more steps in between.
Why need to add -Mpi again
I would strongly assume this is some kind of quick-and-dirty workaround for a bug that has not been fixed properly. Looking at the code it seems this +Mpi*pi has been added later on in the development process. And while I don't know what the expected values are I would believe that multiplying only one of the summands by Pi is probably wrong.
I have written an optimization problem in pyomo and need a constraint, which contains a summation that has a variable length:
u_i_t[i, t]*T_min_run - sum (tnewnew in (t-T_min_run+1)..t-1) u_i_t[i,tnewnew] <= sum (tnew in t..(t+T_min_run-1)) u_i_t[i,tnew]
T is my actual timeline and N my machines
usually I iterate over t, but I need to guarantee the machines are turned on for certain amount of time.
def HP_on_rule(model, i, t):
return model.u_i_t[i, t]*T_min_run - sum(model.u_i_t[i, tnewnew] for tnewnew in range((t-T_min_run+1), (t-1))) <= sum(model.u_i_t[i, tnew] for tnew in range(t, (t+T_min_run-1)))
model.HP_on_rule = Constraint(N, rule=HP_on_rule)
I hope you can provide me with the correct formulation in pyomo/python.
The problem is that t is a running variable and I do not know how to implement this in Python. tnew is only a help variable. E.g. t=6 (variable), T_min_run=3 (constant) and u_i_t is binary [00001111100000...] then I get:
1*3 - 1 <= 3
As I said, I do not know how to implement this in my code and the current version is not running.
TypeError: HP_on_rule() missing 1 required positional argument: 't'
It seems like you didn't provide all your arguments to the function rule.
Since t is a parameter of your function, I assume that it corresponds to an element of set T (your timeline).
Then, your last line of your code example should include not only the set N, but also the set T. Try this:
model.HP_on_rule = Constraint(N, T, rule=HP_on_rule)
Please note: Building a Constraint with a "for each" part, you must provide the Pyomo Sets that you want to iterate over at the begining of the call for Constraint construction. As a rule of thumb, your constraint rule function should have 1 more argument than the number of Pyomo Sets specified in the Constraint initilization line.
I need your help to solve this "Little" problem I'm having programming with GAMS.
In my objective function I have this member that is z = [...]-TWC(j)*HS(j).
Where HS(j)is a variable.
Now, TWC(j) should be a parameter that works like this:
TWC(j) = 0 when HS(j) < 1000
and
TWC(j) = 3.21 when HS(j) >=1000.
Any idea how to implement this in GAMS? my attempts all failed.
EDIT: this is what I tried I defined an equation called TWCup(j) that was:
TWCup(j)$(HS.l(j) >= 1000).. TWC(j) =e= 3.21;
Thanks ;)
Probably not relevant for the OP anymore (since the question is more than 3 years old), but maybe useful for someone else that looks at this question.
If TWC(j) is a function of your variable HS(j), it is not a parameter. It is another variable. So you should define TWC(j) as a variable and not as a parameter. This is probably the reason you were getting errors.
There are some ways to fix your problem: One is to actually turn TWC(j) into a variable. But this would turn your problem into non-linear which could be (or not) an issue. Also, this could need the use of binary variables, which could also become a problem (again, or not).
But I think this issue could be resolved with a different specification of the LP. The cost function f(HS(j)) = TWC(j)*HS(j) is linear by parts and convex, which you can represent in a standard LP using auxiliary variables (assuming you are minimizing).
* declare auxiliary variable
Variable
w(j);
* declare equations for linear by part cost function
Equation
costfun1(j)
costfun2(j);
;
* define costfun1 and costfun2
costfun1(j).. w(j) =g= 0;
costfun2(j).. w(j) =g= -3210 + 3.21*HS(j);
*redefine objective function (note that I changed to plus because I assumed this is a cost function that you are minimizing)
z = [...]+w(j)
This solution is very problem dependent. I assumed you were minimizing and I changed the sign in the objective function to '+'. If this was not the case, this would not work (would not be convex). Then we would need to check other approaches.
But the takeaway here is to stress that something that is a function of a variable is also a variable. But you may have options to reformulate your problem to address the problem.
I am trying to migrate my scripts from mathematica to sage. I am stuck in something that it seems elementary.
I need to work with arbitrarily large polynomials say of the form
a00 + a10*x + a01*y + a20 *x^2 + a11*x*y + ...
I consider them polynomials only on x and y and I need given such a polynomial P to get the list of its monomials.
For example if P = a20*x^2 + a12*x*y^2
I want a list of the form [a20*x^2,a12*x*y^2].
I figured out that a polynomial in sage has a class function called coefficients that returns the coefficients and a class function called monomials that returns the monomials without the coefficients. Multiplying these two list together, gives the result I want.
The problem is that for this to work I need to explicitly declare all the a's as variables with is something that is not always possible.
Is there any way to tell sage that anything of the form a[number][number] is a variable? Or is there any way to define a whole family of variables in sage?
In a perfect world I would like to make sage behave like mathematica, in the sense that anything which is not defines is considered a variable, but I guess this is too optimistic.
My answer is not fully addressing your question but one trick I found to define variables was to use the PolynomialRing(). For example:
sage: R = PolynomialRing(RR, 'c', 20)
sage: c = R.gens()
sage: pol=sum(c[i]*x^i for i in range(10));pol
c9*x^9 + c8*x^8 + c7*x^7 + c6*x^6 + c5*x^5 + c4*x^4 + c3*x^3 + c2*x^2 + c1*x + c0
and later on you can define them as variables to solve(), for example:
sage: variables=[SR(c[i]) for i in srange(0,len(eq_list))];
sage: solution = solve(eqs,variables);
You'll almost certainly need some very minor string processing; the answers
this way of getting lists of symbolic variables
this other way of getting them that is similar
this sage-support post
are better than anything I can say. Naturally, this is possible to implement, but ...
In a perfect world I would like to make sage behave like mathematica, in the sense that anything which is not defines is considered a variable, but I guess this is too optimistic.
True; indeed, that goes against Python's (and hence Sage's) philosophy of "explicit is better than implicit"; there were arguments for a long time over whether even x should be predefined as a symbolic variable (it is!).
(And truthfully, given how often I make typos, I'd really rather not have any arbitrary thing be considered a symbolic variable.)
I have written a finite volume model. The parameter n represents the number of volumes. After translating, the parameter can't be modified. Dymola gives this message:
Warning: Setting n has no effect in model.
After translation you can only set literal start-values and non-evaluated parameters.
I think the problem is that the parameter n is used in the equation section. There I use the following code:
equation
...
for i in 2:n-1 loop
T[i] = some equation
end for
I also use n for the calculation of the initial values of T.
The purpose is to make a script that repeatedly executes the model but with a different n.
How can I do this?
The issue here is that your parameter n affects the number of variables in the problem. Dymola (and all other Modelica compilers I know of) evaluate such parameters at compile time. In other words, they hard code the value at compile time into the model.
One potential workaround in your case is to perform the translation or simulation inside your loop. Note that in the translate and simulate commands in Dymola you can include modifications. Just add them after the model name. For example MyModel would become MyModel(n=10).