I'm trying to solve this recursion:
T(n)=log(T(n-1))+1 :n>2
T(n)=O(1) :n=2
I'm getting an answer of O(1), but I feel I'm missing something.
I'd be happy to get some help.
Thanks in advance!
Your recursion relation converges to 1.0 for any value >= 1.0
Your answer of O(1) is quite correct. You may be a little thrown by the recursion relation being expressed in such a straightforward fashion of time, rather than being given the algorithm?
Let me try again. Also, perhaps we're both a little confused. I answered at the single-call level; perhaps you need the overall answer (more likely, now that I think about it).
First, let's take a single call. If n=2, it's constant time. If n>2 ... this is where I'm not quite familiar with the notation. Does this denote time for a single call, or for the entire recursion sequence descending to n=2? I think it has to be for the single call, due to practical considerations. This makes my earlier answer incorrect.
Look at the call for n=3. This expands and solves as
T(3) = log(T(2)) + 1
T(3) = log(1) + 1
T(3) = 0 + 1 = 1
By induction T(n) = 1 for n >= 2. As it turns out, even if the base case -- T(2) -- has a value other than 1, so long as it's finite and more than 1/ (whatever base we're using for logs), the series will converge to 1, and each call will be in constant time.
Thus, to solve T(n), we have n-2 calls, each of which is T(1). This gives an overall complexity of O(n).
Is that more clear?
Related
We all know good old Math.random(). It returns a random floating point number between 0 and 1.
What I can't seem to find any evidence about is if zero or one is exclusive or inclusive.
I know that if they are inclusive, the probability of hitting either one of these values is seriously low.
But I can't help but wonder if I should wasting an if statement looking for it or not.
In my current scenario zero is not a problem, but one is.
var __rand:uint = Math.floor( Math.random() * myArray.length );
var result:String = myArray[__rand];
if the 1 in Math.random() is exclusive, then I will know that will NEVER be 1, and therefore __rand could never equal myArray.length and should always be below it.. But just wasn't sure if I should waste time in some performance critical code if I should account for it.
PS: The code above is NOT the performance critical code, just an example
Basically, just 2 simple questions.
1) Is returning one impossible or possible.
2) If possible, is it worth accounting for it.
As per the docs:
Returns a pseudo-random number n, where 0 <= n < 1. The number
returned is calculated in an undisclosed manner, and is
"pseudo-random" because the calculation inevitably contains some
element of non-randomness.
So it can be 0 but not 1. You don't have to worry about index out of bounds.
By the way, if this was really performance critical code, you are better off casting the value as int or uint rather than using Math.floor (see this performance test).
Math.random will return a number between 0 and (1 exclusive). Never will return a 1.
Problem # 305
Let's call S the (infinite) string
that is made by concatenating the
consecutive positive integers
(starting from 1) written down in base
10.
Thus, S =
1234567891011121314151617181920212223242...
It's easy to see that any number will
show up an infinite number of times in
S.
Let's call f(n) the starting position
of the nth occurrence of n in S. For
example, f(1)=1, f(5)=81, f(12)=271
and f(7780)=111111365.
Find Summation[f(3^k)] for 1 <= k <=
13.
How can I go about solving this?
Calculating S to an arbitrary size is deceivingly easy, but as you have probably already found out, not practical, it simply becomes too big .
As is common for the newer Project Euler Problems, brute force simply does not work.
That said, you can still look at S for small values of k and maybe construct a formula that will solve the problem in parts (the first few values are easy to handle in memory). Also, look at Problem 40
Note: remember the one minute rule. (most problems can be solved in a few milliseconds)
My estimate of the running time is O(n2 log n), so this brute force approach is not feasible.
Note that you are supposed to solve Project Euler problems yourself, which IMHO applies in particular to newer problems.
When people talk about the use of "magic numbers" in computer programming, what do they mean?
Magic numbers are any number in your code that isn't immediately obvious to someone with very little knowledge.
For example, the following piece of code:
sz = sz + 729;
has a magic number in it and would be far better written as:
sz = sz + CAPACITY_INCREMENT;
Some extreme views state that you should never have any numbers in your code except -1, 0 and 1 but I prefer a somewhat less dogmatic view since I would instantly recognise 24, 1440, 86400, 3.1415, 2.71828 and 1.414 - it all depends on your knowledge.
However, even though I know there are 1440 minutes in a day, I would probably still use a MINS_PER_DAY identifier since it makes searching for them that much easier. Whose to say that the capacity increment mentioned above wouldn't also be 1440 and you end up changing the wrong value? This is especially true for the low numbers: the chance of dual use of 37197 is relatively low, the chance of using 5 for multiple things is pretty high.
Use of an identifier means that you wouldn't have to go through all your 700 source files and change 729 to 730 when the capacity increment changed. You could just change the one line:
#define CAPACITY_INCREMENT 729
to:
#define CAPACITY_INCREMENT 730
and recompile the lot.
Contrast this with magic constants which are the result of naive people thinking that just because they remove the actual numbers from their code, they can change:
x = x + 4;
to:
#define FOUR 4
x = x + FOUR;
That adds absolutely zero extra information to your code and is a total waste of time.
"magic numbers" are numbers that appear in statements like
if days == 365
Assuming you didn't know there were 365 days in a year, you'd find this statement meaningless. Thus, it's good practice to assign all "magic" numbers (numbers that have some kind of significance in your program) to a constant,
DAYS_IN_A_YEAR = 365
And from then on, compare to that instead. It's easier to read, and if the earth ever gets knocked out of alignment, and we gain an extra day... you can easily change it (other numbers might be more likely to change).
There's more than one meaning. The one given by most answers already (an arbitrary unnamed number) is a very common one, and the only thing I'll say about that is that some people go to the extreme of defining...
#define ZERO 0
#define ONE 1
If you do this, I will hunt you down and show no mercy.
Another kind of magic number, though, is used in file formats. It's just a value included as typically the first thing in the file which helps identify the file format, the version of the file format and/or the endian-ness of the particular file.
For example, you might have a magic number of 0x12345678. If you see that magic number, it's a fair guess you're seeing a file of the correct format. If you see, on the other hand, 0x78563412, it's a fair guess that you're seeing an endian-swapped version of the same file format.
The term "magic number" gets abused a bit, though, referring to almost anything that identifies a file format - including quite long ASCII strings in the header.
http://en.wikipedia.org/wiki/File_format#Magic_number
Wikipedia is your friend (Magic Number article)
Most of the answers so far have described a magic number as a constant that isn't self describing. Being a little bit of an "old-school" programmer myself, back in the day we described magic numbers as being any constant that is being assigned some special purpose that influences the behaviour of the code. For example, the number 999999 or MAX_INT or something else completely arbitrary.
The big problem with magic numbers is that their purpose can easily be forgotten, or the value used in another perfectly reasonable context.
As a crude and terribly contrived example:
while (int i != 99999)
{
DoSomeCleverCalculationBasedOnTheValueOf(i);
if (escapeConditionReached)
{
i = 99999;
}
}
The fact that a constant is used or not named isn't really the issue. In the case of my awful example, the value influences behaviour, but what if we need to change the value of "i" while looping?
Clearly in the example above, you don't NEED a magic number to exit the loop. You could replace it with a break statement, and that is the real issue with magic numbers, that they are a lazy approach to coding, and without fail can always be replaced by something less prone to either failure, or to losing meaning over time.
Anything that doesn't have a readily apparent meaning to anyone but the application itself.
if (foo == 3) {
// do something
} else if (foo == 4) {
// delete all users
}
Magic numbers are special value of certain variables which causes the program to behave in an special manner.
For example, a communication library might take a Timeout parameter and it can define the magic number "-1" for indicating infinite timeout.
The term magic number is usually used to describe some numeric constant in code. The number appears without any further description and thus its meaning is esoteric.
The use of magic numbers can be avoided by using named constants.
Using numbers in calculations other than 0 or 1 that aren't defined by some identifier or variable (which not only makes the number easy to change in several places by changing it in one place, but also makes it clear to the reader what the number is for).
In simple and true words, a magic number is a three-digit number, whose sum of the squares of the first two digits is equal to the third one.
Ex-202,
as, 2*2 + 0*0 = 2*2.
Now, WAP in java to accept an integer and print whether is a magic number or not.
It may seem a bit banal, but there IS at least one real magic number in every programming language.
0
I argue that it is THE magic wand to rule them all in virtually every programmer's quiver of magic wands.
FALSE is inevitably 0
TRUE is not(FALSE), but not necessarily 1! Could be -1 (0xFFFF)
NULL is inevitably 0 (the pointer)
And most compilers allow it unless their typechecking is utterly rabid.
0 is the base index of array elements, except in languages that are so antiquated that the base index is '1'. One can then conveniently code for(i = 0; i < 32; i++), and expect that 'i' will start at the base (0), and increment to, and stop at 32-1... the 32nd member of an array, or whatever.
0 is the end of many programming language strings. The "stop here" value.
0 is likewise built into the X86 instructions to 'move strings efficiently'. Saves many microseconds.
0 is often used by programmers to indicate that "nothing went wrong" in a routine's execution. It is the "not-an-exception" code value. One can use it to indicate the lack of thrown exceptions.
Zero is the answer most often given by programmers to the amount of work it would take to do something completely trivial, like change the color of the active cell to purple instead of bright pink. "Zero, man, just like zero!"
0 is the count of bugs in a program that we aspire to achieve. 0 exceptions unaccounted for, 0 loops unterminated, 0 recursion pathways that cannot be actually taken. 0 is the asymptote that we're trying to achieve in programming labor, girlfriend (or boyfriend) "issues", lousy restaurant experiences and general idiosyncracies of one's car.
Yes, 0 is a magic number indeed. FAR more magic than any other value. Nothing ... ahem, comes close.
rlynch#datalyser.com
Reading this question got me thinking: For a given function f, how can we know that a loop of this form:
while (x > 2)
x = f(x)
will stop for any value x? Is there some simple criterion?
(The fact that f(x) < x for x > 2 doesn't seem to help since the series may converge).
Specifically, can we prove this for sqrt and for log?
For these functions, a proof that ceil(f(x))<x for x > 2 would suffice. You could do one iteration -- to arrive at an integer number, and then proceed by simple induction.
For the general case, probably the best idea is to use well-founded induction to prove this property. However, as Moron pointed out in the comments, this could be impossible in the general case and the right ordering is, in many cases, quite hard to find.
Edit, in reply to Amnon's comment:
If you wanted to use well-founded induction, you would have to define another strict order, that would be well-founded. In case of the functions you mentioned this is not hard: you can take x << y if and only if ceil(x) < ceil(y), where << is a symbol for this new order. This order is of course well-founded on numbers greater then 2, and both sqrt and log are decreasing with respect to it -- so you can apply well-founded induction.
Of course, in general case such an order is much more difficult to find. This is also related, in some way, to total correctness assertions in Hoare logic, where you need to guarantee similar obligations on each loop construct.
There's a general theorem for when then sequence of iterations will converge. (A convergent sequence may not stop in a finite number of steps, but it is getting closer to a target. You can get as close to the target as you like by going far enough out in the sequence.)
The sequence x, f(x), f(f(x)), ... will converge if f is a contraction mapping. That is, there exists a positive constant k < 1 such that for all x and y, |f(x) - f(y)| <= k |x-y|.
(The fact that f(x) < x for x > 2 doesn't seem to help since the series may converge).
If we're talking about floats here, that's not true. If for all x > n f(x) is strictly less than x, it will reach n at some point (because there's only a limited number of floating point values between any two numbers).
Of course this means you need to prove that f(x) is actually less than x using floating point arithmetic (i.e. proving it is less than x mathematically does not suffice, because then f(x) = x may still be true with floats when the difference is not enough).
There is no general algorithm to determine whether a function f and a variable x will end or not in that loop. The Halting problem is reducible to that problem.
For sqrt and log, we could safely do that because we happen to know the mathematical properties of those functions. Say, sqrt approaches 1, log eventually goes negative. So the condition x < 2 has to be false at some point.
Hope that helps.
In the general case, all that can be said is that the loop will terminate when it encounters xi≤2. That doesn't mean that the sequence will converge, nor does it even mean that it is bounded below 2. It only means that the sequence contains a value that is not greater than 2.
That said, any sequence containing a subsequence that converges to a value strictly less than two will (eventually) halt. That is the case for the sequence xi+1 = sqrt(xi), since x converges to 1. In the case of yi+1 = log(yi), it will contain a value less than 2 before becoming undefined for elements of R (though it is well defined on the extended complex plane, C*, but I don't think it will, in general converge except at any stable points that may exist (i.e. where z = log(z)). Ultimately what this means is that you need to perform some upfront analysis on the sequence to better understand its behavior.
The standard test for convergence of a sequence xi to a point z is that give ε > 0, there is an n such that for all i > n, |xi - z| < ε.
As an aside, consider the Mandelbrot Set, M. The test for a particular point c in C for an element in M is whether the sequence zi+1 = zi2 + c is unbounded, which occurs whenever there is a |zi| > 2. Some elements of M may converge (such as 0), but many do not (such as -1).
Sure. For all positive numbers x, the following inequality holds:
log(x) <= x - 1
(this is a pretty basic result from real analysis; it suffices to observe that the second derivative of log is always negative for all positive x, so the function is concave down, and that x-1 is tangent to the function at x = 1). From this it follows essentially immediately that your while loop must terminate within the first ceil(x) - 2 steps -- though in actuality it terminates much, much faster than that.
A similar argument will establish your result for f(x) = sqrt(x); specifically, you can use the fact that:
sqrt(x) <= x/(2 sqrt(2)) + 1/sqrt(2)
for all positive x.
If you're asking whether this result holds for actual programs, instead of mathematically, the answer is a little bit more nuanced, but not much. Basically, many languages don't actually have hard accuracy requirements for the log function, so if your particular language implementation had an absolutely terrible math library this property might fail to hold. That said, it would need to be a really, really terrible library; this property will hold for any reasonable implementation of log.
I suggest reading this wikipedia entry which provides useful pointers. Without additional knowledge about f, nothing can be said.
So far I've seen many posts dealing with equality of floating point numbers. The standard answer to a question like "how should we decide if x and y are equal?" is
abs(x - y) < epsilon
where epsilon is a fixed, small constant. This is because the "operands" x and y are often the results of some computation where a rounding error is involved, hence the standard equality operator == is not what we mean, and what we should really ask is whether x and y are close, not equal.
Now, I feel that if x is "almost equal" to y, then also x*10^20 should be "almost equal" to y*10^20, in the sense that the relative error should be the same (but "relative" to what?). But with these big numbers, the above test would fail, i.e. that solution does not "scale".
How would you deal with this issue? Should we rescale the numbers or rescale epsilon? How?
(Or is my intuition wrong?)
Here is a related question, but I don't like its accepted answer, for the reinterpret_cast thing seems a bit tricky to me, I don't understand what's going on. Please try to provide a simple test.
It all depends on the specific problem domain. Yes, using relative error will be more correct in the general case, but it can be significantly less efficient since it involves an extra floating-point division. If you know the approximate scale of the numbers in your problem, using an absolute error is acceptable.
This page outlines a number of techniques for comparing floats. It also goes over a number of important issues, such as those with subnormals, infinities, and NaNs. It's a great read, I highly recommend reading it all the way through.
As an alternative solution, why not just round or truncate the numbers and then make a straight comparison? By setting the number of significant digits in advance, you can be certain of the accuracy within that bound.
The problem is that with very big numbers, comparing to epsilon will fail.
Perhaps a better (but slower) solution would be to use division, example:
div(max(a, b), min(a, b)) < eps + 1
Now the 'error' will be relative.
Using relative error is at least not as bad as using absolute errors, but it has subtle problems for values near zero due to rounding issues. A far from perfect, but somewhat robust algorithm combines absolute and relative error approaches:
boolean approxEqual(float a, float b, float absEps, float relEps) {
// Absolute error check needed when comparing numbers near zero.
float diff = abs(a - b);
if (diff <= absEps) {
return true;
}
// Symmetric relative error check without division.
return (diff <= relEps * max(abs(a), abs(b)));
}
I adapted this code from Bruce Dawson's excellent article Comparing Floating Point Numbers, 2012 Edition, a required read for anyone doing floating-point comparisons -- an amazingly complex topic with many pitfalls.
Most of the time when code compares values, it is doing so to answer some sort of question. For example:
If I know what a function returned when given a value of X, can I assume it will return the same thing if given Y?
If I have a method of computing a function which is slow but accurate, I am willing to accept some inaccuracy in exchange for speed, and I want to test a candidate function which seems to fit the bill, are the outputs from that function close enough to the known-accurate one to be considered "correct".
To answer the first question, code should ideally do a bit-wise comparison on the value, though unless a language supports the new operators added to IEEE-754 in 2009 that may be less efficient than ideal. To answer the second question, one should define what degree of accuracy is required and test against that.
I don't think there's much merit in a general-purpose method which regards as equal things which are close, since different applications will have differing requirements for both absolute and relative tolerance, based upon what exact questions the tests are supposed to answer.