In this post, I would like to explain how I have used Lambda to create a function to generate a Fibonacci series array. This example can also be used to understand how to create an array where th...
Just to add here some of the research I did around this problem. Due to some of the limitation the recursive Lamba function have, some of the mentioned here. First I was trying to find a way to get the Fibonacci number without using the classical Fibo calculation (formula 1):
formula 1 for 30 Fibonacci numbers takes 1800ms. Around 50 Fibonacci number start crashing
formula 2 for 30 Fibonacci numbers returns the result instantaneously. My Excel reports 0ms. It starts showing some time consuming around 1000 Fibonacci numbers, consuming 20ms.
So the recursive approach is an elegant approach, but not a good one in terms of performance.
Back to the original problem generating a sequence of Fibonacci numbers is straightforward using formula 2 (formula 3):
Note: I just adapted the solution to start from F1=1.
Here are the performance results for n=1000
Formula 3: nfibo_serie(1000): 1,230ms
Formula 4: FIBO_SERIE.v0(1000): 110ms
as you can see it is a huge difference, I would suspect the main reason is MAP call (I also tested it with BYROW a little bit faster, but nothing relevant). and under this approach, I don't see a way to get rid of MAP.
So as you can see we have several situations where the recursive approach has been significantly time-consuming, but not for this case.
To understand how the recursion works for this case (Formula 4) provided by lori_m. We can see how it works for the case of 5. From the formula as you can see the recursion ends when n=2, so for this case what it does is the following process:
In column, A you have the final result, but the recursion goes back up to the n=2 (column D), so you should read it from column D (n=2) to column C (n=3), etc. so the formula does this backward calculation until the entire recursion is expanded up to the end n=2. On each call of the recursion, only the last row changes from the previous one, adding the next Fibonacci number. It is ensured by the second IF condition until it goes to the last one which is the sequence: {1;1}.
I hope this graphical representation helps to understand the formula.
UPDATE
Later I found that we can use an indirect way to obtain the Fibonacci number using the following mathematical formulation based on the Binet formula:
F(n) = (phi^n - (1-phi)^n) / sqrt(5)
where phi is the golden ratio: 1.618, so in Excel it can be formulated as follows using the simplified Binet formula (check the link):
I verified this formula has the fastest performance among all the formulas on this post. Using this formula, you can now generate the sequence as follows:
Here is the performance result for the first 1000 Fibonacci numbers:
FIBO_SERIE.v0(1000): Around 100ms
FIBO_SERIE(SEQUENCE(1000)): less than 20ms
So, the last FIBO_SERIE formula is not just very short, but the fastest solution among others.
NOTE: there is no need to create an additional LAMBDA function, since all the functions involved in FIBO work with arrays, i.e.:
FIBO(SEQUENCE(1000))
which should be even faster than FIBO_SERIE which uses BYROW. I tested it for the first 10K Fibonacci numbers and almost instantaneously, the worst try was 20ms.
I rarely use recursion these days, preferring instead to use Lambda helper functions. Occasionally it might be a built-in SCAN or BYROW but, more often, it is a mocked-up version built from REDUCE/VSTACK since it is unusual to require an array of scalars; an array of arrays is far more useful and more frequently encountered.
For the Fibonacci series, one needs to take an array of 2 values forward so
In this instance, the scanned array does not figure in the calculation so the function could be simplified, but at the cost of generality. For the Fibonacci series only one column of the results is needed, so
returns the first 1,000 Fibonacci numbers in 100 ms. I couldn't extend the test that much without hitting #NUM! overflow errors.
Out of interest, I had lifted the SCANV function directly from a workbook that calculated the motion of a container slung from an overhead crane, by tenths of a second for 20 secs. That required 4 variables at each step, namely the cart position, the pendulum angle subtended by the container and the corresponding velocities. The name of the game was to provide force inputs that led to a final state in which the container was hanging vertically with zero velocity since placing a swinging cargo on a truck is tricky.
The parameters were optimised by using Solver to reduce the terminal energy of the system, though lori_m has written optimisation routines as Lambda functions!
Thank you very much for your help..... I will implement the formulas and see if i can make things work.
Resources
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This example can also be used to understand how to create an array where th...","body@stringLength":"6056","rawBody":"
In this post, I would like to explain how I have used Lambda to create a function to generate a Fibonacci series array.
This example can also be used to understand how to create an array where the value of an element in the array depends on previous elements in the same array.
Generating Fibonacci as a dynamic array by @Viz
This function has been created using three function in two layers. The inner layer functions include the following:
InFib: This function generates the Nth Fibonacci number
InFibSer: This function generates the entire Fibonacci series up to the Nth number
The two functions mentioned above require arguments that are complicated and less intuitive. Therefore, we use an outer layer function (FibSeries) that generates the necessary argument and calls the two functions to generate the series.
=InFIB
'This function returns nth Fibonacci using tail recursion 'It takes three arguments n, a=0 , b=1 as argument and returns the nth Fibonacci value
=LAMBDA(n,a,b,IF(n=1,a,IF(n=2,b,Infib(n-1,b,a+b))))\n/* The value for the arguments a and b should be passed as 0 and 1, respectively. */
Example:
=InFIB(10,0,1) would generate 34 as the value
Our objective, here, is not to obtain a single value but to generate an entire array. However, here is where we have a challenge: While it is easier to manipulate an existing array, it is far more to difficult to create a new array (other than sequence array) or extend an existing array.
Therefore, in order to create a Fibonacci series, we are going to pass a dummy array of n values. This array will have 0 as the first element and 1 as the value for all the other elements.
The following image portrays the recursive addition process that converts that the initial array into a Fibonacci series.
The following code does carries out the process recursively till the maximum value of the array equals the nth Fibonacci value.
=INFIBSER
'This inner function takes three arguments and returns a Fibonacci series 'The three arguments are nth Fibonacci value, the initial array and a sequence array (sequential numbers from 1 to n) 'Note, the sequence array can instead be generated inside the function but it may inefficient
In the first iteration, the above function adds one to all the values from the fourth position onwards. For the subsequent iterations it adds the second largest unique value to all values that all 2 places after the position of such second largest value. The above function uses the match function to identify the position pointer.
Although the above function can generate the Fibonacci series, as mentioned earlier, it requires arguments that are less intuitive and which requires some set up.
Therefore, here we have an outer layer function that requires only one argument, n. It creates the necessary argument to be passed to the inner layer function and calls them.
=FIBSERIES
'This outermost function generates an initial array, a sequence array, and nth fibonnaci value 'It then calls the inner functions with these parameters
This final function can be used by the user to generate the Fibonacci series by merely passing the number of values required as argument.
Hope you found this useful. I would love to know if you think there is a way to improve the algorithm, here. I am especially looking for suggestions that could replace the match / xmatch in the InFibSer function to get the position pointer. Since the positions move sequentially, I believe a mathematical solution may exist.
In this post, I would like to explain how I have used Lambda to create a function to generate a Fibonacci series array.
This example can also be used to understand how to create an array where the value of an element in the array depends on previous elements in the same array.
Generating Fibonacci as a dynamic array by @Viz
This function has been created using three function in two layers. The inner layer functions include the following:
InFib: This function generates the Nth Fibonacci number
InFibSer: This function generates the entire Fibonacci series up to the Nth number
The two functions mentioned above require arguments that are complicated and less intuitive. Therefore, we use an outer layer function (FibSeries) that generates the necessary argument and calls the two functions to generate the series.
=InFIB
'This function returns nth Fibonacci using tail recursion 'It takes three arguments n, a=0 , b=1 as argument and returns the nth Fibonacci value
=LAMBDA(n,a,b,IF(n=1,a,IF(n=2,b,Infib(n-1,b,a+b))))\n/* The value for the arguments a and b should be passed as 0 and 1, respectively. */
Example:
=InFIB(10,0,1) would generate 34 as the value
Our objective, here, is not to obtain a single value but to generate an entire array. However, here is where we have a challenge: While it is easier to manipulate an existing array, it is far more to difficult to create a new array (other than sequence array) or extend an existing array.
Therefore, in order to create a Fibonacci series, we are going to pass a dummy array of n values. This array will have 0 as the first element and 1 as the value for all the other elements.
The following image portrays the recursive addition process that converts that the initial array into a Fibonacci series.
The following code does carries out the process recursively till the maximum value of the array equals the nth Fibonacci value.
=INFIBSER
'This inner function takes three arguments and returns a Fibonacci series 'The three arguments are nth Fibonacci value, the initial array and a sequence array (sequential numbers from 1 to n) 'Note, the sequence array can instead be generated inside the function but it may inefficient
In the first iteration, the above function adds one to all the values from the fourth position onwards. For the subsequent iterations it adds the second largest unique value to all values that all 2 places after the position of such second largest value. The above function uses the match function to identify the position pointer.
Although the above function can generate the Fibonacci series, as mentioned earlier, it requires arguments that are less intuitive and which requires some set up.
Therefore, here we have an outer layer function that requires only one argument, n. It creates the necessary argument to be passed to the inner layer function and calls them.
=FIBSERIES
'This outermost function generates an initial array, a sequence array, and nth fibonnaci value 'It then calls the inner functions with these parameters
This final function can be used by the user to generate the Fibonacci series by merely passing the number of values required as argument.
Hope you found this useful. I would love to know if you think there is a way to improve the algorithm, here. I am especially looking for suggestions that could replace the match / xmatch in the InFibSer function to get the position pointer. Since the positions move sequentially, I believe a mathematical solution may exist.
I rarely use recursion these days, preferring instead to use Lambda helper functions. Occasionally it might be a built-in SCAN or BYROW but, more often, it is a mocked-up version built from REDUCE/VSTACK since it is unusual to require an array of scalars; an array of arrays is far more useful and more frequently encountered.
For the Fibonacci series, one needs to take an array of 2 values forward so
In this instance, the scanned array does not figure in the calculation so the function could be simplified, but at the cost of generality. For the Fibonacci series only one column of the results is needed, so
returns the first 1,000 Fibonacci numbers in 100 ms. I couldn't extend the test that much without hitting #NUM! overflow errors.
Out of interest, I had lifted the SCANV function directly from a workbook that calculated the motion of a container slung from an overhead crane, by tenths of a second for 20 secs. That required 4 variables at each step, namely the cart position, the pendulum angle subtended by the container and the corresponding velocities. The name of the game was to provide force inputs that led to a final state in which the container was hanging vertically with zero velocity since placing a swinging cargo on a truck is tricky.
The parameters were optimised by using Solver to reduce the terminal energy of the system, though lori_m has written optimisation routines as Lambda functions!
= SCANV(X₀, t, RK4Stepλ(Dλ))
","body@stringLength":"3843","rawBody":"
I rarely use recursion these days, preferring instead to use Lambda helper functions. Occasionally it might be a built-in SCAN or BYROW but, more often, it is a mocked-up version built from REDUCE/VSTACK since it is unusual to require an array of scalars; an array of arrays is far more useful and more frequently encountered.
For the Fibonacci series, one needs to take an array of 2 values forward so
In this instance, the scanned array does not figure in the calculation so the function could be simplified, but at the cost of generality. For the Fibonacci series only one column of the results is needed, so
returns the first 1,000 Fibonacci numbers in 100 ms. I couldn't extend the test that much without hitting #NUM! overflow errors.
Out of interest, I had lifted the SCANV function directly from a workbook that calculated the motion of a container slung from an overhead crane, by tenths of a second for 20 secs. That required 4 variables at each step, namely the cart position, the pendulum angle subtended by the container and the corresponding velocities. The name of the game was to provide force inputs that led to a final state in which the container was hanging vertically with zero velocity since placing a swinging cargo on a truck is tricky.
The parameters were optimised by using Solver to reduce the terminal energy of the system, though has written optimisation routines as Lambda functions!
= SCANV(X₀, t, RK4Stepλ(Dλ))
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(check the UPDATE section for the shortest and fastest solution)
Just to add here some of the research I did around this problem. Due to some of the limitation the recursive Lamba function have, some of the mentioned here. First I was trying to find a way to get the Fibonacci number without using the classical Fibo calculation (formula 1):
formula 1 for 30 Fibonacci numbers takes 1800ms. Around 50 Fibonacci number start crashing
formula 2 for 30 Fibonacci numbers returns the result instantaneously. My Excel reports 0ms. It starts showing some time consuming around 1000 Fibonacci numbers, consuming 20ms.
So the recursive approach is an elegant approach, but not a good one in terms of performance.
Back to the original problem generating a sequence of Fibonacci numbers is straightforward using formula 2 (formula 3):
Note: I just adapted the solution to start from F1=1.
Here are the performance results for n=1000
Formula 3: nfibo_serie(1000): 1,230ms
Formula 4: FIBO_SERIE.v0(1000): 110ms
as you can see it is a huge difference, I would suspect the main reason is MAP call (I also tested it with BYROW a little bit faster, but nothing relevant). and under this approach, I don't see a way to get rid of MAP.
So as you can see we have several situations where the recursive approach has been significantly time-consuming, but not for this case.
To understand how the recursion works for this case (Formula 4) provided by lori_m. We can see how it works for the case of 5. From the formula as you can see the recursion ends when n=2, so for this case what it does is the following process:
In column, A you have the final result, but the recursion goes back up to the n=2 (column D), so you should read it from column D (n=2) to column C (n=3), etc. so the formula does this backward calculation until the entire recursion is expanded up to the end n=2. On each call of the recursion, only the last row changes from the previous one, adding the next Fibonacci number. It is ensured by the second IF condition until it goes to the last one which is the sequence: {1;1}.
I hope this graphical representation helps to understand the formula.
UPDATE
Later I found that we can use an indirect way to obtain the Fibonacci number using the following mathematical formulation based on the Binet formula:
F(n) = (phi^n - (1-phi)^n) / sqrt(5)
where phi is the golden ratio: 1.618, so in Excel it can be formulated as follows using the simplified Binet formula (check the link):
I verified this formula has the fastest performance among all the formulas on this post. Using this formula, you can now generate the sequence as follows:
Here is the performance result for the first 1000 Fibonacci numbers:
FIBO_SERIE.v0(1000): Around 100ms
FIBO_SERIE(SEQUENCE(1000)): less than 20ms
So, the last FIBO_SERIE formula is not just very short, but the fastest solution among others.
NOTE: there is no need to create an additional LAMBDA function, since all the functions involved in FIBO work with arrays, i.e.:
FIBO(SEQUENCE(1000))
which should be even faster than FIBO_SERIE which uses BYROW. I tested it for the first 10K Fibonacci numbers and almost instantaneously, the worst try was 20ms.
","body@stringLength":"8317","rawBody":"
(check the UPDATE section for the shortest and fastest solution)
Just to add here some of the research I did around this problem. Due to some of the limitation the recursive Lamba function have, some of the mentioned here. First I was trying to find a way to get the Fibonacci number without using the classical Fibo calculation (formula 1):
formula 1 for 30 Fibonacci numbers takes 1800ms. Around 50 Fibonacci number start crashing
formula 2 for 30 Fibonacci numbers returns the result instantaneously. My Excel reports 0ms. It starts showing some time consuming around 1000 Fibonacci numbers, consuming 20ms.
So the recursive approach is an elegant approach, but not a good one in terms of performance.
Back to the original problem generating a sequence of Fibonacci numbers is straightforward using formula 2 (formula 3):
Note: I just adapted the solution to start from F1=1.
Here are the performance results for n=1000
Formula 3: nfibo_serie(1000): 1,230ms
Formula 4: FIBO_SERIE.v0(1000): 110ms
as you can see it is a huge difference, I would suspect the main reason is MAP call (I also tested it with BYROW a little bit faster, but nothing relevant). and under this approach, I don't see a way to get rid of MAP.
So as you can see we have several situations where the recursive approach has been significantly time-consuming, but not for this case.
To understand how the recursion works for this case (Formula 4) provided by . We can see how it works for the case of 5. From the formula as you can see the recursion ends when n=2, so for this case what it does is the following process:
In column, A you have the final result, but the recursion goes back up to the n=2 (column D), so you should read it from column D (n=2) to column C (n=3), etc. so the formula does this backward calculation until the entire recursion is expanded up to the end n=2. On each call of the recursion, only the last row changes from the previous one, adding the next Fibonacci number. It is ensured by the second IF condition until it goes to the last one which is the sequence: {1;1}.
I hope this graphical representation helps to understand the formula.
UPDATE
Later I found that we can use an indirect way to obtain the Fibonacci number using the following mathematical formulation based on the Binet formula:
F(n) = (phi^n - (1-phi)^n) / sqrt(5)
where phi is the golden ratio: 1.618, so in Excel it can be formulated as follows using the simplified Binet formula (check the link):
I verified this formula has the fastest performance among all the formulas on this post. Using this formula, you can now generate the sequence as follows:
Here is the performance result for the first 1000 Fibonacci numbers:
FIBO_SERIE.v0(1000): Around 100ms
FIBO_SERIE(SEQUENCE(1000)): less than 20ms
So, the last FIBO_SERIE formula is not just very short, but the fastest solution among others.
NOTE: there is no need to create an additional LAMBDA function, since all the functions involved in FIBO work with arrays, i.e.:
FIBO(SEQUENCE(1000))
which should be even faster than FIBO_SERIE which uses BYROW. I tested it for the first 10K Fibonacci numbers and almost instantaneously, the worst try was 20ms.
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PeterBartholomew1 I don't know if at the time you replied you haven't seen the update I did to my post in the UPDATE section. I don't think any other approach can beat this one FIBO using the Binet formula:
I tested it for FIBO(SEQUENCE(10000)) and in the worst-case scenario, it took 20ms, so it serves to get a single Fibonacci number or a series. Short, no recursive, and low computation effort. It reminds me of this heuristic rule that I heard once, :-):
T+S+H=C
Thinking (T) plus Software (S) plus Hardware(H) is equal to Constant(C). This is what the Binet formula does. Simplified the mathematical formulation, then you can minimize the Software and Hardware effort/investment, in our case Software is Excel and Hardware is the computer where it runs.
Thanks for your replay, similar to my approach (formula 2 in my post) using REDUCE to avoid the recursion.
Please accept my apologies. I hadn't noticed your use of the Binet formula. I simply revisited the problem as an exercise, since the original Viz post seemed to predate the Lambda helper functions. Somewhat oddly, I did use the Binet formula to determine why my pseudo-SCAN failed with #NUM! errors long before I reached the 10,000th term you mentioned. I hit overflow errors somewhere before the 1,500th term which in turn meant that my formula returned nothing but the error.
The main takeaway for me from the exercise is that the inbuilt Excel functions SCAN, MAP, BYCOL, BYROW are all inadequately scoped since the nested array should be the basic building block of the functionality, not an error. As it stands, the use Lambda helper functions in formulas can be more restrictive than relying on the underlying array methods they support, since the lifting and broadcasting behaviours of the array formula are not generalised as one might hope.
PeterBartholomew1 not a problem at all, indeed your approach is very interesting too. I realized that the problem of numerical precision is affecting not just the Binet formula, but also the initial approach I provided in my post (formula 2). Check my reply to lori_m. Using the REDUCE approach starts to fail for getting the 74th Fibonacci number, even it involves just additions. Have you tested your formula for this number or greater than this one?, unless the Python algorithm is also wrong, but it involves just additions too.
Additionally the Fibonacci sequence provides a nice testing ground for different conceptual approaches to functional programming, Peter provides a mechanical example of this above. Another approach could be to use SCAN with an accumulator such as LAMBDA(i,IF(i,a,b)) as a means of overcoming array truncation even if this may be not as efficient, I'll try and post an example soon..
lori_m thanks for your feedback, the first link refers to another post not to any specific answer in this post that is why I was not aware of it. About the numerical accuracy, I was aware that for Python the Binet approach provides a wrong result starting from 72 fibonacci number. I checked that for this number Excel provides the correct result using Binet formula, but now looking at your link I tested more numbers and I realized that starting from 74, the result is different. I verified it online using the following python approach that is really fast:
import math\ndef fibo(n):\n a, b = 0, 1\n for i in range(n):\n a, b = b, a + b\n return a\nprint(fibo(74))
But the problem is not the numerical precision of the approximate method. I tested the second formula of my post, which doesn't involve a numerical calculation other than addition:
and for 74 and returns: 1304969544928660, but the Python formula returns 1304969544928657 and we are not close to the maximum integer number 9223372036854780000
Thanks,
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