I want to potentiate certain terms in my objective function.
model.addConstr(KW == quicksum(I[t] *(1.05**(-i)) for t in Tst + Z[t]
* (1.05**(-j)) for t in T)
model.setObjective(KW,GRB.MAXIMIZE)
model.optimize()
The variable i should run from 1 to the number of elements in Tst and T
respectively.
So if t in Tst is [2020,2021,2022], I[2020] gets multiplied by 1.05**
(-1)
I[2021] by 1.05**(-2) and I[2022] by 1.05**(-3).
Same with Z[t], only that the list of T is larger than Tst.
for i in range(1,len(Tst)+1):
model.addConstr(KW == quicksum(I[t] * (1.05**(-i)))
However KW is always 0 then, which it shouldnt be. What am i missing?
I just created a 2nd dictionary
Expo = {}
i=1
for t in T:
Expo[t] = i
i = i + 1
If I do:
model.addConstr(KW == quicksum(I[t] *(1.05**(Expo[t])) for t in Tst + Z[t]
* (1.05**(Expo[t])) for t in T)
model.setObjective(KW,GRB.MAXIMIZE)
model.optimize()
it does what i want. But I dont think its a very good solution :P
Related
The fold manual gives an example:
input price = close;
input length = 9;
plot SMA = (fold n = 0 to length with s do s + getValue(price, n, length - 1)) / lenth;
This effectively calls a function iteratively like in a for loop body.
When I use this statement to call my own function as follows, then it breaks because the loop index variable is not recognized as a variable that can be passed to my function:
script getItem{
input index = 0;
plot output = index * index;
}
script test{
def total = fold index = 0 to 10 with accumulator = 0 do
accumulator + getItem(index);########## Error: No such variable: index
}
It is a known bug / limitation. Has been acknowledged without a time line for a fix. No workaround available.
Have you tried adding a small remainder to your defined variable within the fold and then pass that variable? You can strip the integer value and then use the remainder as your counter value. I've been playing around with somethin similar but it isn't working (yet). Here's an example:
script TailOverlap{
input i = 0;
def ii = (Round(i, 1) - i) * 1000;
... more stuff
plot result = result;
};
def _S = (
fold i = displace to period
with c = 0
do if
TailOverlap(i = _S) #send cur val of _S to script
then _S[1] + 1.0001 #increment variable and counter
else _S[1] + 0.0001 #increment the counter only
);
I'm going to continue playing around with this. If I get it to work I'll post the final solution. If you're able to get work this (or have discovered another solution) please do post it here so I know.
Thanks!
I've been experimenting with various prime sieves in Julia with a view to finding the fastest. This is my simplest, if not my fastest, and it runs in around 5-6 ms on my 1.80 GHz processor for n = 1 million. However, when I add a simple 'if' statement to take care of the cases where n <= 1 or s (the start number) > n, the run-time increases by a factor of 15 to around 80-90 ms.
using BenchmarkTools
function get_primes_1(n::Int64, s::Int64=2)::Vector{Int64}
#=if n <= 1 || s > n
return []
end=#
sieve = fill(true, n)
for i = 3:2:isqrt(n) + 1
if sieve[i]
for j = i ^ 2:i:n
sieve[j]= false
end
end
end
pl = [i for i in s - s % 2 + 1:2:n if sieve[i]]
return s == 2 ? unshift!(pl, 2) : pl
end
#btime get_primes_1(1_000_000)
Output with the 'if' statement commented out, as above, is:
5.752 ms (25 allocations: 2.95 MiB)
Output with the 'if' statement included is:
86.496 ms (2121646 allocations: 35.55 MiB)
I'm probably embarrassingly ignorant or being terminally stupid, but if someone could point out what I'm doing wrong it would be very much appreciated.
The problem of this function is with Julia compiler having problems with type inference when closures appear in your function. In this case the closure is a comprehension and the problem is that if statement makes sieve to be only conditionally defined.
You can see this by moving sieve up:
function get_primes_1(n::Int64, s::Int64=2)::Vector{Int64}
sieve = fill(true, n)
if n <= 1 || s > n
return Int[]
end
for i = 3:2:isqrt(n) + 1
if sieve[i]
for j = i ^ 2:i:n
sieve[j]= false
end
end
end
pl = [i for i in s - s % 2 + 1:2:n if sieve[i]]
return s == 2 ? unshift!(pl, 2) : pl
end
However, this makes sieve to be created also when n<1 which you want to avoid I guess :).
You can solve this problem by wrapping sieve in let block like this:
function get_primes_1(n::Int64, s::Int64=2)::Vector{Int64}
if n <= 1 || s > n
return Int[]
end
sieve = fill(true, n)
for i = 3:2:isqrt(n) + 1
if sieve[i]
for j = i ^ 2:i:n
sieve[j]= false
end
end
end
let sieve = sieve
pl = [i for i in s - s % 2 + 1:2:n if sieve[i]]
return s == 2 ? unshift!(pl, 2) : pl
end
end
or avoiding an inner closure for example like this:
function get_primes_1(n::Int64, s::Int64=2)::Vector{Int64}
if n <= 1 || s > n
return Int[]
end
sieve = fill(true, n)
for i = 3:2:isqrt(n) + 1
if sieve[i]
for j = i ^ 2:i:n
sieve[j]= false
end
end
end
pl = Int[]
for i in s - s %2 +1:2:n
sieve[i] && push!(pl, i)
end
s == 2 ? unshift!(pl, 2) : pl
end
Now you might ask how can you detect such problems and make sure that some solution solves them? The answer is to use #code_warntype on a function. In your original function you will notice that sieve is Core.Box which is an indication of the problem.
See https://github.com/JuliaLang/julia/issues/15276 for details. In general this is in my perception the most important issue with performance of Julia code which is easy to miss. Hopefully in the future the compiler will be smarter with this.
Edit: My suggestion actually doesn't seem to help. I missed your output annotation, so the return type appears to be correctly inferred after all. I am stumped, for the moment.
Original answer:
The problem isn't that there is an if statement, but that you introduce a type instability inside that if statement. You can read about type instabilities in the performance section of the Julia manual here.
An empty array defined like this: [], has a different type than a vector of integers:
> typeof([1,2,3])
Array{Int64,1}
> typeof([])
Array{Any,1}
The compiler cannot predict what the output type of the function will be, and therefore produces defensive, slow code.
Try to change
return []
to
return Int[]
I am making a program for nth Fibonacci number. I made the following program using recursion and memoization.
The main problem is that the value of n can go up to 10000 which means that the Fibonacci number of 10000 would be more than 2000 digit long.
With a little bit of googling, I found that i could use arrays and store every digit of the solution in an element of the array but I am still not able to figure out how to implement this approach with my program.
#include<iostream>
using namespace std;
long long int memo[101000];
long long int n;
long long int fib(long long int n)
{
if(n==1 || n==2)
return 1;
if(memo[n]!=0)
return memo[n];
return memo[n] = fib(n-1) + fib(n-2);
}
int main()
{
cin>>n;
long long int ans = fib(n);
cout<<ans;
}
How do I implement that approach or if there is another method that can be used to achieve such large values?
One thing that I think should be pointed out is there's other ways to implement fib that are much easier for something like C++ to compute
consider the following pseudo code
function fib (n) {
let a = 0, b = 1, _;
while (n > 0) {
_ = a;
a = b;
b = b + _;
n = n - 1;
}
return a;
}
This doesn't require memoisation and you don't have to be concerned about blowing up your stack with too many recursive calls. Recursion is a really powerful looping construct but it's one of those fubu things that's best left to langs like Lisp, Scheme, Kotlin, Lua (and a few others) that support it so elegantly.
That's not to say tail call elimination is impossible in C++, but unless you're doing something to optimise/compile for it explicitly, I'm doubtful that whatever compiler you're using would support it by default.
As for computing the exceptionally large numbers, you'll have to either get creative doing adding The Hard Way or rely upon an arbitrary precision arithmetic library like GMP. I'm sure there's other libs for this too.
Adding The Hard Way™
Remember how you used to add big numbers when you were a little tater tot, fresh off the aluminum foil?
5-year-old math
1259601512351095520986368
+ 50695640938240596831104
---------------------------
?
Well you gotta add each column, right to left. And when a column overflows into the double digits, remember to carry that 1 over to the next column.
... <-001
1259601512351095520986368
+ 50695640938240596831104
---------------------------
... <-472
The 10,000th fibonacci number is thousands of digits long, so there's no way that's going to fit in any integer C++ provides out of the box. So without relying upon a library, you could use a string or an array of single-digit numbers. To output the final number, you'll have to convert it to a string tho.
(woflram alpha: fibonacci 10000)
Doing it this way, you'll perform a couple million single-digit additions; it might take a while, but it should be a breeze for any modern computer to handle. Time to get to work !
Here's an example in of a Bignum module in JavaScript
const Bignum =
{ fromInt: (n = 0) =>
n < 10
? [ n ]
: [ n % 10, ...Bignum.fromInt (n / 10 >> 0) ]
, fromString: (s = "0") =>
Array.from (s, Number) .reverse ()
, toString: (b) =>
b .reverse () .join ("")
, add: (b1, b2) =>
{
const len = Math.max (b1.length, b2.length)
let answer = []
let carry = 0
for (let i = 0; i < len; i = i + 1) {
const x = b1[i] || 0
const y = b2[i] || 0
const sum = x + y + carry
answer.push (sum % 10)
carry = sum / 10 >> 0
}
if (carry > 0) answer.push (carry)
return answer
}
}
We can verify that the Wolfram Alpha answer above is correct
const { fromInt, toString, add } =
Bignum
const bigfib = (n = 0) =>
{
let a = fromInt (0)
let b = fromInt (1)
let _
while (n > 0) {
_ = a
a = b
b = add (b, _)
n = n - 1
}
return toString (a)
}
bigfib (10000)
// "336447 ... 366875"
Expand the program below to run it in your browser
const Bignum =
{ fromInt: (n = 0) =>
n < 10
? [ n ]
: [ n % 10, ...Bignum.fromInt (n / 10 >> 0) ]
, fromString: (s = "0") =>
Array.from (s) .reverse ()
, toString: (b) =>
b .reverse () .join ("")
, add: (b1, b2) =>
{
const len = Math.max (b1.length, b2.length)
let answer = []
let carry = 0
for (let i = 0; i < len; i = i + 1) {
const x = b1[i] || 0
const y = b2[i] || 0
const sum = x + y + carry
answer.push (sum % 10)
carry = sum / 10 >> 0
}
if (carry > 0) answer.push (carry)
return answer
}
}
const { fromInt, toString, add } =
Bignum
const bigfib = (n = 0) =>
{
let a = fromInt (0)
let b = fromInt (1)
let _
while (n > 0) {
_ = a
a = b
b = add (b, _)
n = n - 1
}
return toString (a)
}
console.log (bigfib (10000))
Try not to use recursion for a simple problem like fibonacci. And if you'll only use it once, don't use an array to store all results. An array of 2 elements containing the 2 previous fibonacci numbers will be enough. In each step, you then only have to sum up those 2 numbers. How can you save 2 consecutive fibonacci numbers? Well, you know that when you have 2 consecutive integers one is even and one is odd. So you can use that property to know where to get/place a fibonacci number: for fib(i), if i is even (i%2 is 0) place it in the first element of the array (index 0), else (i%2 is then 1) place it in the second element(index 1). Why can you just place it there? Well when you're calculating fib(i), the value that is on the place fib(i) should go is fib(i-2) (because (i-2)%2 is the same as i%2). But you won't need fib(i-2) any more: fib(i+1) only needs fib(i-1)(that's still in the array) and fib(i)(that just got inserted in the array).
So you could replace the recursion calls with a for loop like this:
int fibonacci(int n){
if( n <= 0){
return 0;
}
int previous[] = {0, 1}; // start with fib(0) and fib(1)
for(int i = 2; i <= n; ++i){
// modulo can be implemented with bit operations(much faster): i % 2 = i & 1
previous[i&1] += previous[(i-1)&1]; //shorter way to say: previous[i&1] = previous[i&1] + previous[(i-1)&1]
}
//Result is in previous[n&1]
return previous[n&1];
}
Recursion is actually discommanded while programming because of the time(function calls) and ressources(stack) it consumes. So each time you use recursion, try to replace it with a loop and a stack with simple pop/push operations if needed to save the "current position" (in c++ one can use a vector). In the case of the fibonacci, the stack isn't even needed but if you are iterating over a tree datastructure for example you'll need a stack (depends on the implementation though). As I was looking for my solution, I saw #naomik provided a solution with the while loop. That one is fine too, but I prefer the array with the modulo operation (a bit shorter).
Now concerning the problem of the size long long int has, it can be solved by using external libraries that implement operations for big numbers (like the GMP library or Boost.multiprecision). But you could also create your own version of a BigInteger-like class from Java and implement the basic operations like the one I have. I've only implemented the addition in my example (try to implement the others they are quite similar).
The main idea is simple, a BigInt represents a big decimal number by cutting its little endian representation into pieces (I'll explain why little endian at the end). The length of those pieces depends on the base you choose. If you want to work with decimal representations, it will only work if your base is a power of 10: if you choose 10 as base each piece will represent one digit, if you choose 100 (= 10^2) as base each piece will represent two consecutive digits starting from the end(see little endian), if you choose 1000 as base (10^3) each piece will represent three consecutive digits, ... and so on. Let's say that you have base 100, 12765 will then be [65, 27, 1], 1789 will be [89, 17], 505 will be [5, 5] (= [05,5]), ... with base 1000: 12765 would be [765, 12], 1789 would be [789, 1], 505 would be [505]. It's not the most efficient, but it is the most intuitive (I think ...)
The addition is then a bit like the addition on paper we learned at school:
begin with the lowest piece of the BigInt
add it with the corresponding piece of the other one
the lowest piece of that sum(= the sum modulus the base) becomes the corresponding piece of the final result
the "bigger" pieces of that sum will be added ("carried") to the sum of the following pieces
go to step 2 with next piece
if no piece left, add the carry and the remaining bigger pieces of the other BigInt (if it has pieces left)
For example:
9542 + 1097855 = [42, 95] + [55, 78, 09, 1]
lowest piece = 42 and 55 --> 42 + 55 = 97 = [97]
---> lowest piece of result = 97 (no carry, carry = 0)
2nd piece = 95 and 78 --> (95+78) + 0 = 173 = [73, 1]
---> 2nd piece of final result = 73
---> remaining: [1] = 1 = carry (will be added to sum of following pieces)
no piece left in first `BigInt`!
--> add carry ( [1] ) and remaining pieces from second `BigInt`( [9, 1] ) to final result
--> first additional piece: 9 + 1 = 10 = [10] (no carry)
--> second additional piece: 1 + 0 = 1 = [1] (no carry)
==> 9542 + 1 097 855 = [42, 95] + [55, 78, 09, 1] = [97, 73, 10, 1] = 1 107 397
Here is a demo where I used the class above to calculate the fibonacci of 10000 (result is too big to copy here)
Good luck!
PS: Why little endian? For the ease of the implementation: it allows to use push_back when adding digits and iteration while implementing the operations will start from the first piece instead of the last piece in the array.
I have this code in VBA (looping through the array a() of type double):
bm = 0 'tot
b = 0 'prev
For i = 24 To 0 Step -1
BP = b 'prevprev = prev
b = bm 'prev = tot
bm = T * b - BP + a(i) 'tot = a(i) + T * prev - prevprev
Next
p = Exp(-xa * xa) * (bm - BP) / 4 '* (tot - prevprev)/4
I'm putting this in F#. Clearly I could use an array and mutable variables to recreate the VBA. And maybe this is an example of the right time to use mutable that I've seen hinted at. But why not try to do it the most idiomatic way?
I could write a little recursive function to replicate the loop. But it kind of feels like littering to hang out a little sub-loop that has no meaning on its own as a standalone, named function.
I want to do it with List functions. I have a couple ideas, but I'm not there yet. Anyone get this in a snap??
The two vague ideas I have are: 1. I could make two more lists by chopping off one (and two) elements and adding zero-value element(s). And combine those lists. 2. I'm wondering if a list function like map can take trailing terms in the list as arguments. 3. As a general question, I wonder if this might be a case where an experienced person would say that this problem screams for mutable values (and if so does that dampen my enthusiasm for getting on the functional boat).
To give more intuition for the code: The full function that this is excerpted from is a numerical approximation for the cumulative normal distribution. I haven't looked up the math behind this one. "xa" is the absolute value of the main function argument "x" which is the number of standard deviations from zero. Without working through the proof, I don't think there's much more to say than: it's just a formula. (Oh and maybe I should change the variable names--xa and bm etc are pretty wretched. I did put suggestions as comments.)
It's just standard recursion. You make your exit condition and your recur condition.
let rec calc i prevPrev prev total =
if i = 0 then // exit condition; do your final calc
exp(-xa * xa) * (total - prevPrev) / 4.
else // recur condition, call again
let newPrevPrev = prev
let newPrev = total
let newTotal = (T * newPrev - newPrevPrev + a i)
calc (i-1) newPrevPrev newPrev newTotal
calc 24 initPrevPrev initPrev initTotal
or shorter...
let rec calc i prevPrev prev total =
if i = 0 then
exp(-xa * xa) * (total - prevPrev) / 4.
else
calc (i-1) prev total (T * total - prev + a i)
Here's my try at pulling the loop out as a recursive function. I'm not thrilled about the housekeeping to have this stand alone, but I think the syntax is neat. Aside from an error in the last line, that is, where the asterisk in (c * a.Tail.Head) gets the red squiggly for float list not matching type float (but I thought .Head necessarily returned float not list):
let rec RecurseIt (a: float list) c =
match a with
| []-> 0.0
| head::[]-> a.Head
| head::tail::[]-> a.Head + (c * a.Tail) + (RecurseIt a.Tail c)
| head::tail-> a.Head + (c * a.Tail.Head) - a.Tail.Tail.Head + (RecurseIt a.Tail c)
Now I'll try list functions. It seems like I'm going to have to iterate by element rather than finding a one-fell-swoop slick approach.
Also I note in this recursive function that all my recursive calls are in tail position I think--except for the last one which will come one line earlier. I wonder if this creates a stack overflow risk (ie, prevents the compiler from treating the recursion as a loop (if that's the right description), or if I'm still safe because the algo will run as a loop plus just one level of recursion).
EDIT:
Here's how I tried to return a list instead of the sum of the list (so that I could use the 3rd to last element and also sum the elements), but I'm way off with this syntax and still hacking away at it:
let rec RecurseIt (a: float list) c =
match a with
| []-> []
| head::[]-> [a.Head]
| head::tail::[]-> [a.Head + (c * a.Tail)] :: (RecurseIt a.Tail c)
| head::tail-> [a.Head + (c * a.Tail.Head) - a.Tail.Tail.Head] :: (RecurseIt a.Tail c)
Here's my try at a list function. I think the problem felt more complicated than it was due to confusing myself. I just had some nonsense with List.iteri here. Hopefully this is closer to making sense. I hoped some List. function would be neat. Didn't manage. For loop not so idiomatic I think. :
for i in 0 .. a.Length - 1 do
b::
a.Item(i) +
if i > 0 then
T * b.Item(i-1) -
if i > 1 then
b.Item(i-2)
else
0
else
0
I'm very new to OCaml / functional programming, and I'm confused about the implementation of some things that are relatively simple other languages I know. I could use any and all help.
Chiefly: in a program I'm working on, I either increment or decrement a variable based on a certain parameter. Here's something representative of what I have:
let tot = ref 0 in
for i = 0 to s do
if test_num > 0 then
tot := !tot + other_num
else
tot := !tot - other_num
done;;
This is obviously not the way to go about it, because even if the else statement is never taken, the code acts as if it is, each and every time, presumably because it's closer to the bottom of the program? I know OCaml has pretty sophisticated pattern matching, but within this level of coed I need access to a handful of lists I've already created, and, as far as I understand, I can't access those lists from a top-level function without passing them all as parameters.
I know I'm going about this the wrong way, but I have no idea how to do this idiomatically.
Suggestions? Thanks.
edit
Here's a more concise example:
let ex_list = [1; -2; 3; -4] in
let max_mem = ref 0 in
let mem = ref 0 in
let () =
for i = 0 to 3 do
let transition = List.nth ex_list i in
if transition > 0 then (
mem := (!mem + 10);
) else
mem := (!mem - 1);
if (!mem > !max_mem) then (max_mem := !mem);
done;
print_int !max_mem; print_string "\n";
in !mem;
At the end, when I print max_mem, I get 19, though this value should be (0 + 10 - 1 + 10 - 1 = 18). Am I doing the math wrong, or does the problem come from somewhere else?
Your code looks fine to me. It doesn't make a lot of sense as actual code, but I think you're just trying to show a general layout. It's also written in imperative style, which I usually try to avoid if possible.
The if in OCaml acts just like it does in other languages, there's no special thing about being near the bottom of the program. (More precisely, it acts like the ? : ternary operator from C and related languages; i.e., it's an expression.)
Your code doesn't return a useful value; it always returns () (the quintessentially uninteresting value known as "unit").
If we replace your free variables (ones not defined in this bit of code) by constants, and change the code to return a value, we can run it:
# let s = 8 in
let other_num = 7 in
let test_num = 3 in
let tot = ref 0 in
let () =
for i = 0 to s do
if test_num > 0 then
tot := !tot + other_num
else
tot := !tot - other_num
done
in
!tot;;
- : int = 63
#
If you're trying to learn to write in a functional style (i.e., without mutable variables), you would write this loop as a recursive function and make tot a parameter:
# let s = 8 in
let other_num = 7 in
let test_num = 3 in
let rec loop n tot =
if n > s then
tot
else
let tot' =
if test_num > 0 then tot + other_num else tot - other_num
in
loop (n + 1) tot'
in
loop 0 0;;
- : int = 63
It would probably be easier to help if you gave a (edited to add: small :-) self-contained problem that you're trying to solve.
The other parts of your question aren't clear enough to give any advice on. One thing that I might point out is that it's completely idiomatic to use pattern matching when processing lists.
Also, there's nothing wrong with passing things as parameters. That's why the language is called "functional" -- your code consists of functions, which have parameters.
Update
I like to write let () = expr1 in expr2 instead of expr1; expr2. It's just a habit I got into, sorry if it's confusing. The essence is that you're evaluating the first expression just for its side effects (it has type unit), and then returning the value of the second expression.
If you don't have something after the for, the code will evaluate to (), as I said. Since the purpose of the code seems to be to compute the value of !tot, this is what I returned. At the very least, this lets you see the calculated value in the OCaml top level.
tot' is just another variable. If you calculate a new value straightforwardly from a variable named var, it's conventional to name the new value var'. It reads as "var prime".
Update 2
Your example code works OK, but it has the problem that it uses List.nth to traverse a list, which is a slow (quadratic) operation. In fact your code is naturally considered a fold. Here's how you might write it in a functional style:
# let ex_list = [1; -2; 3; -4] in
let process (tot, maxtot) transition =
let tot' = if transition > 0 then tot + 10 else tot - 1 in
(tot', max maxtot tot')
in
List.fold_left process (0, 0) ex_list;;
- : int * int = (18, 19)
#
In addition to Jeffrey's answer, let me second that this is not how you would usually write such code in Ocaml, since it is a very low-level imperative approach. A more functional version would look like this:
let high ex_list =
let deltas = List.map (fun x -> if x > 0 then 10 else -1) ex_list in
snd (List.fold_left (fun (n, hi) d -> (n+d, max (n+d) hi)) (0, 0) deltas)
let test = high [1; -2; 3; -4]