XOR statement in PuLP python - python-3.x

I am working on PuLP in python and I want to model the following statement :
x is positive XOR y is positive, where x ans y are integer.
How can I convert this in PuLP code ?
I started with
XOR

I agree with #kabdulla. Binary variables would be the way to go here.
Expanding on that idea a little further: You can use binary variables to indicate whether X is positive (TRUE/1) by a constraint such as M*binary_variable_for_x <= X where M is a sufficiently large number for the problem that would not limit X. Then the binary_variable_for_x can be 1 if X > 0.
Do the same for when Y is positive (TRUE/1).
And then you could write another constraint that requires the sum of these booleans to be >= 1.
binary_variable_for_x + binary_variable_for_y >= 1
There's multiple ways to formulate the problem, but this can be one way.

Related

Algorithm: find two positive integers whose difference is minimized and whose product is known

Some background...
I am currently building a macro that will estimate the cost of an injection molding tool. These tools have cavities which are filled with plastic. The number of cavities a tool has is the number of parts that will be formed.
So far my program will determine the minimum number of cavities a tool can have based on customer demand. This number is always even. The tool should have an even number of cavities. Given the bounding length and width of a cavity, and setting a limit to how much space the cavities can occupy within the tool, I need my program to calculate the combination of number of cavities along the length and width whose difference is minimized and whose product is equal to the total number of minimum cavities the tool should have.
I am programming my macro is SolidWorks VBA. I first constructed this problem in Excel and used the solver tool. But, I am unable to find a way to reference the Excel Solver Tool in SolidWorks to automate this optimization problem. I am hoping to find a clever set of equations that can solve this specific problem for me. But if someone else has a better idea of what to use, that would be awesome.
Rephrasing in an optimization format...
Variables
x = number of cavities along width of tool
y = number of cavities along length of tool
z = suggested number of total cavities
Objective Function
Minimize x - y
Such that
x * y = z
x >= 1
y >= 1
x <= y
x is an integer
y is an integer
Example
My macro says that in order to meet demand, our tool needs to have at least 48 cavities. Find the number of cavities along the length and width of the tool such that the difference is minimized and the product is equal to 48. Ideally in this case the macro would return x = 6 and y = 8.
Thanks!
Just to clarify, in the question did you actually mean to Min y-x rather than Min x-y? Otherwise there is a naïve solution taking x = 1 and y = z. Min x - y = 1-z.
I don't program in VBA but here is the idea.
Since x and y are positive integers and there product is z, with x <= y. You can essentially start with x = floor(sqrt(z)) and decrement until x = 1.
For each x, check if there exists an integer y such that x * y = z. If there is, break the loop and that's the pair you are looking for. Otherwise continue until x = 1
If you need any pseudo code so you can translate it into VBA. Here it is
int x, y;
for (x = floor(sqrt(z)); x >= 1; --x)
{
y = z / x;
if (x * y == z)
break;
}
I think you can just test out a few examples. No fancy algorithm is needed.
If you relax the condition to be 2 numbers, x and y, whose product is z and with a minimum difference, then the answer is SQRT(z).
That is not an integer that meets your needs (in general). However, you can then try integers around the square root to see if they divide z. The first one you hit (i.e. minimum difference from SQRT(z)) should have the minimum difference.
If you relax the condition to be |z - x * y| is minimized, then I would recommend testing the numbers around sqrt(z). You need to check two cases -- the floor and ceiling of the square root (and the appropriate other number).
Just in case someone is needs something similar to this in the future, but can't figure out the pseudo-code I went ahead wrote it up. I wasn't sure how to output it as two values so I just threw them together as a string for the user to see.
Option Explicit
Function Factors(ByVal Test As Long) As String
Dim Val As Long
Dim i As Long
Val = Test
i = Int(Sqr(Val))
While Val / i >= 2
If Int(Val / i) * i = Val Then
Factors = i & " & " & Val / i
Exit Function
End If
i = i - 1
Wend
End Function

math.sqrt function python gives same result for two different values [duplicate]

Why does the math module return the wrong result?
First test
A = 12345678917
print 'A =',A
B = sqrt(A**2)
print 'B =',int(B)
Result
A = 12345678917
B = 12345678917
Here, the result is correct.
Second test
A = 123456758365483459347856
print 'A =',A
B = sqrt(A**2)
print 'B =',int(B)
Result
A = 123456758365483459347856
B = 123456758365483467538432
Here the result is incorrect.
Why is that the case?
Because math.sqrt(..) first casts the number to a floating point and floating points have a limited mantissa: it can only represent part of the number correctly. So float(A**2) is not equal to A**2. Next it calculates the math.sqrt which is also approximately correct.
Most functions working with floating points will never be fully correct to their integer counterparts. Floating point calculations are almost inherently approximative.
If one calculates A**2 one gets:
>>> 12345678917**2
152415787921658292889L
Now if one converts it to a float(..), one gets:
>>> float(12345678917**2)
1.5241578792165828e+20
But if you now ask whether the two are equal:
>>> float(12345678917**2) == 12345678917**2
False
So information has been lost while converting it to a float.
You can read more about how floats work and why these are approximative in the Wikipedia article about IEEE-754, the formal definition on how floating points work.
The documentation for the math module states "It provides access to the mathematical functions defined by the C standard." It also states "Except when explicitly noted otherwise, all return values are floats."
Those together mean that the parameter to the square root function is a float value. In most systems that means a floating point value that fits into 8 bytes, which is called "double" in the C language. Your code converts your integer value into such a value before calculating the square root, then returns such a value.
However, the 8-byte floating point value can store at most 15 to 17 significant decimal digits. That is what you are getting in your results.
If you want better precision in your square roots, use a function that is guaranteed to give full precision for an integer argument. Just do a web search and you will find several. Those usually do a variation of the Newton-Raphson method to iterate and eventually end at the correct answer. Be aware that this is significantly slower that the math module's sqrt function.
Here is a routine that I modified from the internet. I can't cite the source right now. This version also works for non-integer arguments but just returns the integer part of the square root.
def isqrt(x):
"""Return the integer part of the square root of x, even for very
large values."""
if x < 0:
raise ValueError('square root not defined for negative numbers')
n = int(x)
if n == 0:
return 0
a, b = divmod(n.bit_length(), 2)
x = (1 << (a+b)) - 1
while True:
y = (x + n//x) // 2
if y >= x:
return x
x = y
If you want to calculate sqrt of really large numbers and you need exact results, you can use sympy:
import sympy
num = sympy.Integer(123456758365483459347856)
print(int(num) == int(sympy.sqrt(num**2)))
The way floating-point numbers are stored in memory makes calculations with them prone to slight errors that can nevertheless be significant when exact results are needed. As mentioned in one of the comments, the decimal library can help you here:
>>> A = Decimal(12345678917)
>>> A
Decimal('123456758365483459347856')
>>> B = A.sqrt()**2
>>> B
Decimal('123456758365483459347856.0000')
>>> A == B
True
>>> int(B)
123456758365483459347856
I use version 3.6, which has no hardcoded limit on the size of integers. I don't know if, in 2.7, casting B as an int would cause overflow, but decimal is incredibly useful regardless.

How do I compare large numbers to small numbers in python 3?

I have to use the math.exp() function to get the following value for x which is converted to scientific notation. However, when trying to compare to see if this number is greater than y, python thinks it is less than.
x = 4.0686596698143466e+186
y = 59425800000000000000000
if x >= y:
print:("x is greater than y")
I realize there are methods to turn very large numbers into smaller int but I feel that route is a little more complicated and above my learning curve than necessary. I just need a way to see if x > y and also curious why python doesn't support the comparison. Converting y to scientific notation using decimal only turns it into a string.
Disclaimer: still a beginner

Mutable variables in Haskell?

I'm starting to wrap my head around Haskell and do some exciting experiments. And there's one thing I just seem to be unable to comprehend (previous "imperativist" experience talks maybe).
Recently, I was yearning to implement integer division function as if there where no multiply/divide operations. An immensely interesting brain-teaser which led to great confusion.
divide x y =
if x < y then 0
else 1 + divide (x - y) y
I compiled it and it.. works(!). That's mind-blowing. However, I was told, I was sure that variables are immutable in Haskell. How comes that with each recursive step variable x keeps it's value from previous step? Or is my glorious compiler lying to me? Why does it work at all?
Your x here doesn't change during one function call (i.e., after creation) - that's exactly what immutable means. What does change is value of x during multiple (recursive) calls. In a single stack frame (function call) the value of x is constant.
An example of execution of your code, for a simple case
call divide 8 3 -- (x = 8, y = 3), stack: divide 8 3
step 1: x < y ? NO
step 2: 1 + divide 5 3
call: divide 5 3 -- (x = 5, y = 3), stack: divide 8 3, divide 5 3
step 1: x < y ? NO
step 2: 1 + divide 2 3
call divide 2 3 -- (x = 2, y = 3), stack: divide 8 3, divide 5 3, divide 2 3
step 1: x < y ? YES
return: 0 -- unwinding bottom call
return 1 + 0 -- stack: divide 8 3, divide 5 3, unwinding middle call
return 1 + 1 + 0 -- stack: divide 8 3
I am aware that the above notation is not anyhow formalized, but I hope it helps to understand what recursion is about and that x might have different values in different calls, because it's simply a different instance of whole call, thus also different instance of x.
x is actually not a variable, but a parameter, and isn't that different from parameters in imperative languages.
Maybe it'd look more obvious with explicit return statements?
-- for illustrative purposes only, doesn't actually work
divide x y =
if x < y
then return 0
else return 1 + divide (x - y) y
You're not mutating x, just stacking up several function calls to calculate your desired result with the values they return.
Here's the same function in Python:
def divide(x, y):
if x < y:
return 0
else:
return 1 + divide(x - y, y)
Looks familiar, right? You can translate this to any language that allows recursion, and none of them would require you to mutate a variable.
Other than that, yes, your compiler is lying to you. Because you're not allowed to directly mutate values, the compiler can make a lot of extra assumptions based on your code, which helps translating it to efficient machine code, and at that level, there's no escaping mutability. The major benefit is that compilers are way less likely to introduce mutability-related bugs than us mortals.

If Then Constraints in non-linear programming

I have several constrains in a No linear problem.
For example:
In m(x+y-n)^2
If x+y-n>=0 Then m=0,
Else m=1.
How can I write this conditional constraint as linear or non-linear constraint?
Well you could write this as [min(x+y-n,0)]^2. Not sure if that will do you any good (this is non-differentiable, and thus difficult for many solvers). We can make the min() expression linear using additional binary variables:
z <= x+y-n
z <= 0
z >= x+y-n - b * M
z >= 0 - (1-b) * M
b in {0,1}
with M a large enough constant. In many cases better reformulations can be applied but that depends on the rest of the model.
If you use a constraint-programming solver, such as Choco Solver, then you can use IfThenElse constraints directly as well as other non linear constraints, such as square.

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