Suppose we have N (in this example N = 3) events that can happen depending on some variables. Each of them can generate certain profit or loses (event1 = 300, event2 = -100, event3 = 200), they are constrained by rules when they happen.
event 1 happens only when x > 5,
event 2 happens only when x = 2 and y = 3
event 3 happens only when x is odd.
The problem is to know the maximum profit.
Assume x, y are integer numbers >= 0
In the real problem there are many events and many dimensions.
(the solution should not be specific)
My question is:
Is this linear programming problem? If yes please provide solution to the example problem using this approach. If no please suggest some algorithms to optimize such problem.
This can be formulated as a mixed integer linear program. This is a linear program where some of the variables are constrained to be integer. Contrary to linear programs, solving the general integer program is NP-hard. However, there are many commercial or open source solvers that can solve efficiently large-scale problems. For up to 300 variables and constraints, you can use excel's solver.
Here is a way to formulate the above constraints:
If you go down this route, you might find this document useful.
the last constraint in an interesting one. I am assuming that x has to be integer, but if x can be either integer or continuous I will edit the answer accordingly.
I hope this helps!
Edit: L and U above should be interpreted as L1 and U1.
Edit 2: z2 needs to changed to (1-z2) on the 3rd and 4th constraint.
A specific answer:
seems more like a mathematical calculation than a programming problem, can't you just run a loop for x= 1->1000 to see what results occur?
for the example:
as x = 2 or 3 = -200 then x > 2 or 3, and if x < 5 doesn't get the 300, so all that is really happening is x > 5 and x = odd = maximum results.
x = 7 = 300 + 200 . = maximum profit for x
A general answer:
I don't see how to answer the question without seeing what the events are and how the events effect X ? Weather it's a linear or functional (mathematical) answer seems rather beside the point of finding the desired solution.
Related
To explain the question it's best to start with this
picture
I am modeling an optimization decision problem and a feature that I'm trying to implement is heat transfer between the process stages (a = 1, 2) taking into account which equipment type is chosen (j = 1, 2, 3) by the binary decision variable y.
The temperatures for the equipment are fixed values and my goal is to find (in the case of the picture) dT = 120 - 70 = 50 while keeping the temperature difference as a parameter (I want to keep the problem linear and need to multiply the temperature difference with a variable later on).
Things I have tried:
dT = T[a,j] - T[a-1,j]
(this obviously gives T = 80 for T[a-1,j] which is incorrect)
T[a-1] = sum(T[a-1,j] * y[a-1,j] for j in (1,2,3)
This will make the problem non-linear when I multiply with another variable.
I am using pyomo and the linear "glpk" solver. Thank you for reading my post and if someone could help me with this it is greatly appreciated!
If you only have 2 stages and 3 pieces of equipment at each stage, you could reformulate and let a binary decision variable Y[i] represent each of the 9 possible connections and delta_T[i] be a parameter that represents the temp difference associated with the same 9 connections which could easily be calculated and put into a model parameter.
If you want to keep in double-indexed, and assuming that there will only be 1 piece of equipment selected at each stage, you could take the sum-product of the selection variable and temps at each stage and subtract them.
dT[a] = sum(T[a, j]*y[a, j] for j in J) - sum(T[a-1, j]*y[a-1, j] for j in J)
for a ∈ {2, 3, ..., N}
I have 4 non negative real variable that are A, B, C and X. Based on the current problem that I have, I notice that the variable X must belong to the interval of [B,C] and the relation will be a bunch of if-else conditions like this:
If A < B:
x = B
elseif A > C:
x = C
elseif B<=A<=C:
x = A
As you can see, it quite difficult to reformulate as a Mixed Integer Programming problem with corresponding decision variable (d1, d2 and d3). I have try reading some instructions regarding if-then formulation using big M method at this site:
https://www.math.cuhk.edu.hk/course_builder/1415/math3220/L2%20(without%20solution).pdf but it seem that this problem is more challenging than their tutorial.
Could you kindly provide me with a formulation for this situation ?
Thank you very much !
For Colombia's Observatorio Fiscal[1], I am coding a simple tax minimization problem, using CLP(R) (in SWI-Prolog). I want to use minimize/1 to find the least solution first. It is instead listing the bigger solution first. Here is the code:
:- use_module(library(clpr)).
deduction(_,3). % Anyone can take the standard deduction.
deduction(Who,D) :- itemizedDeduction(Who,D). % Or they can itemize.
income(joe,10). % Joe makes $10 a year.
itemizedDeduction(joe,4). % He can deduct more if he itemizes.
taxableIncome(Who,TI) :-
deduction(Who,D),
income(Who,I),
TI is I - D,
minimize(TI).
Here is what an interactive session looks like:
?- taxableIncome(joe,N).
N = 7 ;
N = 6 ;
false.
If I switch the word "minimize" to "maximize" it behaves identically. If I include no minimize or maximize clause, it doesn't look for a third solution, but otherwise it behaves the same:
?- taxableIncome(joe,N).
N = 7 ;
N = 6.
[1] The Observatorio Fiscal is a new organization that aims to model the Colombian economy, in order to anticipate the effects of changes in the law, similar to what the Congressional Budget Office or the Tax Policy Center do in the United States.
First, let's add the following definition to the program:
:- op(950,fy, *).
*_.
Using (*)/1, we can generalize away individual goals in the program.
For example, let us generalize away the minimize/1 goal by placing * in front:
taxableIncome(Who,TI) :-
deduction(Who,D),
income(Who,I),
TI #= I - D,
* minimize(TI).
We now get:
?- taxableIncome(X, Y).
X = joe,
Y = 7 ;
X = joe,
Y = 6.
This shows that CLP(R) in fact has nothing to do with this issue! These answers show that everything is already instantiated at the time minimize/1 is called, so there is nothing left to minimize.
To truly benefit from minimize/1, you must express the task in the form of CLP(R)—or better: CLP(Q)— constraints, then apply minimize/1 on a constrained expression.
Note also that in SWI-Prolog, both CLP(R) and CLP(Q) have elementary mistakes, and you cannot trust their results.
Per Mat's response, I rewrote the program expressing the constraints using CLP. The tricky bit was that I had to first collect all (both) possible values for deduction, then convert those values into a CLP domain. I couldn't get that conversion to work in CLP(R), but I could in CLP(FD):
:- use_module(library(clpfd)).
deduction(_,3). % Anyone can take the same standard deduction.
deduction(Who,D) :- % Or they can itemize.
itemizedDeduction(Who,D).
income(joe,10).
itemizedDeduction(joe,4).
listToDomain([Elt],Elt).
listToDomain([Elt|MoreElts],Elt \/ MoreDom) :-
MoreElts \= []
, listToDomain(MoreElts,MoreDom).
taxableIncome(Who,TI) :-
income(Who,I)
, findall(D,deduction(Who,D),DList)
, listToDomain(DList,DDomain)
% Next are the CLP constraints.
, DD in DDomain
, TI #= I-DD
, labeling([min(TI)],[TI]).
I know similar questions have been answered before but none of the answers I could find were specific to .NET.
Does the VB.NET compiler optimize expressions like:
x = y / 4
by compiling:
x = y * 0.25
And before anyone says don't worry the difference is small, i already know that but this will be executed a lot and choosing one over the other could make a useful difference in total execution time and will be much easier to do than a more major refactoring exercise.
Perhaps I should have mentioned for the benefit of those who live in an environment of total freedom: I am not at liberty to change to a different language. If I were I would probably have written this code in Fortran.
As suggested here is a simple comparison:
Dim y = 1234.567
Dim x As Double
Dim c = 10000000000.0
Dim ds As Date
Dim df As Date
ds = Now
For i = 1 To c
x = y / 4
Next
df = Now
Console.WriteLine("divide " & (df - ds).ToString)
ds = Now
For i = 1 To c
x = y * 0.25
Next
df = Now
Console.WriteLine("multiply " & (df - ds).ToString)
The output is:
divide 00:00:52.7452740
multiply 00:00:47.2607256
So divide does appear to be slower by about 10%. But this difference is so small that I suspected it to be accidental. Another two runs give:
divide 00:00:45.1280000
multiply 00:00:45.9540000
divide 00:00:45.9895985
multiply 00:00:46.8426838
Suggesting that in fact the optimization is made or that the arithmetic operations are a vanishingly small part of the total time.
In either case it means that I don't need to care which is used.
In fact ildasm shows that the IL uses div in the first loop and mul in the second. So it doesn't make the subsitution after all. Unless the JIT compiler does.
I'm sorry the title is so confusingly worded, but it's hard to condense this problem down to a few words.
I'm trying to find the minimum value of a specific equation. At first I'm looping through the equation, which for our purposes here can be something like y = .245x^3-.67x^2+5x+12. I want to design a loop where the "steps" through the loop get smaller and smaller.
For example, the first time it loops through, it uses a step of 1. I will get about 30 values. What I need help on is how do I Use the three smallest values I receive from this first loop?
Here's an example of the values I might get from the first loop: (I should note this isn't supposed to be actual code at all. It's just a brief description of what's happening)
loop from x = 1 to 8 with step 1
results:
x = 1 -> y = 30
x = 2 -> y = 28
x = 3 -> y = 25
x = 4 -> y = 21
x = 5 -> y = 18
x = 6 -> y = 22
x = 7 -> y = 27
x = 8 -> y = 33
I want something that can detect the lowest three values and create a loop. From theses results, the values of x that get the smallest three results for y are x = 4, 5, and 6.
So my "guess" at this point would be x = 5. To get a better "guess" I'd like a loop that now does:
loop from x = 4 to x = 6 with step .5
I could keep this pattern going until I get an absurdly accurate guess for the minimum value of x.
Does anybody know of a way I can do this? I know the values I'm going to get are going to be able to be modeled by a parabola opening up, so this format will definitely work. I was thinking that the values could be put into a column. It wouldn't be hard to make something that returns the smallest value for y in that column, and the corresponding x-value.
If I'm being too vague, just let me know, and I can answer any questions you might have.
nice question. Here's at least a start for what I think you should do for this:
Sub findMin()
Dim lowest As Integer
Dim middle As Integer
Dim highest As Integer
lowest = 999
middle = 999
hightest = 999
Dim i As Integer
i = 1
Do While i < 9
If (retVal(i) < retVal(lowest)) Then
highest = middle
middle = lowest
lowest = i
Else
If (retVal(i) < retVal(middle)) Then
highest = middle
middle = i
Else
If (retVal(i) < retVal(highest)) Then
highest = i
End If
End If
End If
i = i + 1
Loop
End Sub
Function retVal(num As Integer) As Double
retVal = 0.245 * Math.Sqr(num) * num - 0.67 * Math.Sqr(num) + 5 * num + 12
End Function
What I've done here is set three Integers as your three Min values: lowest, middle, and highest. You loop through the values you're plugging into the formula (here, the retVal function) and comparing the return value of retVal (hence the name) to the values of retVal(lowest), retVal(middle), and retVal(highest), replacing them as necessary. I'm just beginning with VBA so what I've done likely isn't very elegant, but it does at least identify the Integers that result in the lowest values of the function. You may have to play around with the values of lowest, middle, and highest a bit to make it work. I know this isn't EXACTLY what you're looking for, but it's something along the lines of what I think you should do.
There is no trivial way to approach this unless the problem domain is narrowed.
The example polynomial given in fact has no minimum, which is readily determined by observing y'>0 (hence, y is always increasing WRT x).
Given the wide interpretation of
[an] equation, which for our purposes here can be something like y =
.245x^3-.67x^2+5x+12
many conditions need to be checked, even assuming the domain is limited to polynomials.
The polynomial order is significant, and the order determines what conditions are necessary to check for how many solutions are possible, or whether any solution is possible at all.
Without taking this complexity into account, an iterative approach could yield an incorrect solution due to underflow error, or an unfortunate choice of iteration steps or bounds.
I'm not trying to be hard here, I think your idea is neat. In practice it is more complicated than you think.