An Alogorithm's running time.
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It simply means that the running time is logarithmic to the input size. Just imaging the curves on a 2D graph (X=data size, Y=theorical running time) O(N) is a line; running time is "eqaul" to the data siz. O(N-square) is a curve that will grow faster and faster as the datasize increase. O(log N) is a log curve that will grow but the growth rate will decrease as the datasize increase. i.e. if you increase the data size by "one unit" the running time will only increase by a factor of log of 1 unit. The best example of an O(log N) algorithm is the Binary search algorithm; its worst case scenario will be in O(log N).
Watched code never compiles.
Maximilien wrote:
O(log N) is a log curve that will grow but the growth rate will decrease as the datasize increase. i.e. if you increase the data size by "one unit" the running time will only increase by a factor of log of 1 unit.
I understand this, so what I can infer from this is ... 1) With the increase in the number of inputs, the increase in running time is not significant, 2) Since Log(N) < N always, and if an algorithm takes 1s for 1 input data, and if this is logarithmic in nature... Running itme = Log(10) = 1 1 < 10 , which means, in logarithmic algorithms "running time is always less than number of inputs".. Is my understanding right.. Or am I misinterpreting.. :^)
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Maximilien wrote:
O(log N) is a log curve that will grow but the growth rate will decrease as the datasize increase. i.e. if you increase the data size by "one unit" the running time will only increase by a factor of log of 1 unit.
I understand this, so what I can infer from this is ... 1) With the increase in the number of inputs, the increase in running time is not significant, 2) Since Log(N) < N always, and if an algorithm takes 1s for 1 input data, and if this is logarithmic in nature... Running itme = Log(10) = 1 1 < 10 , which means, in logarithmic algorithms "running time is always less than number of inputs".. Is my understanding right.. Or am I misinterpreting.. :^)
There is an invisible constant multiplier and overhead. So really it's
Runtime = C * Log(Inputs) + D. With a large enough number, the "D" constant becomes insignificant and the "C" constant matters less than if the algorithm were of a worse performing function (e.g., N vs Log(N)). Look into Big O notation.Somebody in an online forum wrote:
INTJs never really joke. They make a point. The joke is just a gift wrapper.
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It simply means that the running time is logarithmic to the input size. Just imaging the curves on a 2D graph (X=data size, Y=theorical running time) O(N) is a line; running time is "eqaul" to the data siz. O(N-square) is a curve that will grow faster and faster as the datasize increase. O(log N) is a log curve that will grow but the growth rate will decrease as the datasize increase. i.e. if you increase the data size by "one unit" the running time will only increase by a factor of log of 1 unit. The best example of an O(log N) algorithm is the Binary search algorithm; its worst case scenario will be in O(log N).
Watched code never compiles.
Also, A common observation with all logarithmic algorithms is , we don't end up processing all input elements. Eg : Binary search, Searching an element in Binary search tree, etc
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Maximilien wrote:
O(log N) is a log curve that will grow but the growth rate will decrease as the datasize increase. i.e. if you increase the data size by "one unit" the running time will only increase by a factor of log of 1 unit.
I understand this, so what I can infer from this is ... 1) With the increase in the number of inputs, the increase in running time is not significant, 2) Since Log(N) < N always, and if an algorithm takes 1s for 1 input data, and if this is logarithmic in nature... Running itme = Log(10) = 1 1 < 10 , which means, in logarithmic algorithms "running time is always less than number of inputs".. Is my understanding right.. Or am I misinterpreting.. :^)
Vijay Sringeri wrote:
1 < 10 , which means, in logarithmic algorithms "running time is always less than number of inputs"..
Sort of right sort of wrong. What it means is you will not need touch each of the inputs (time is really irrelevant).
Computers have been intelligent for a long time now. It just so happens that the program writers are about as effective as a room full of monkeys trying to crank out a copy of Hamlet.
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Hi, Since I couldn't find any of the technology forum on CP to post my question, I felt "Lounge" is the right place to post it. My questions is this... I'm finding it really hard to understand what it literally means " When an algorithms running time is logarithmic" ? How this can be explained in layman terms ? What I can understand is.. When the running time is linear... => Running time increases proportionally to increase in number of inputs. When the running time is quadratic..ie.. n2 => Running time doubles, proportionally to increase in number of inputs. So, Can someone explain me the logarithmic behavior of a algorithms. All, I know about logarithms is the easy way to express huge numbers (exponents).. Any links would be much appreciated. :) Regards, Vijay.
When talking about Big O, we do not actually talk about time but data points (in and out). So O(N) means each input must be touched. Where as O(log N) means we must touch log N inputs to get our output. Time is relative. If it takes 10s to 'touch' an input then you can calculate how long it will take to calculate the output of a dataset of 1 million.
Computers have been intelligent for a long time now. It just so happens that the program writers are about as effective as a room full of monkeys trying to crank out a copy of Hamlet.
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Vijay Sringeri wrote:
1 < 10 , which means, in logarithmic algorithms "running time is always less than number of inputs"..
Sort of right sort of wrong. What it means is you will not need touch each of the inputs (time is really irrelevant).
Computers have been intelligent for a long time now. It just so happens that the program writers are about as effective as a room full of monkeys trying to crank out a copy of Hamlet.
Collin Jasnoch wrote:
What it means is you will not need touch each of the inputs (time is really irrelevant).
Exactly , this is what that I was looking for.. :-), but nobody explains this while doing run time analysis.. Thanks.
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When talking about Big O, we do not actually talk about time but data points (in and out). So O(N) means each input must be touched. Where as O(log N) means we must touch log N inputs to get our output. Time is relative. If it takes 10s to 'touch' an input then you can calculate how long it will take to calculate the output of a dataset of 1 million.
Computers have been intelligent for a long time now. It just so happens that the program writers are about as effective as a room full of monkeys trying to crank out a copy of Hamlet.
Collin Jasnoch wrote:
we do not actually talk about time but data points (in and out).
Thanks Collin, this is actually the core which one should understand... Basically these notations indicates how many input data need to be processed to get output.. Thanks.
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Hi, Since I couldn't find any of the technology forum on CP to post my question, I felt "Lounge" is the right place to post it. My questions is this... I'm finding it really hard to understand what it literally means " When an algorithms running time is logarithmic" ? How this can be explained in layman terms ? What I can understand is.. When the running time is linear... => Running time increases proportionally to increase in number of inputs. When the running time is quadratic..ie.. n2 => Running time doubles, proportionally to increase in number of inputs. So, Can someone explain me the logarithmic behavior of a algorithms. All, I know about logarithms is the easy way to express huge numbers (exponents).. Any links would be much appreciated. :) Regards, Vijay.
It's like trying to eat a donut in a single bite. The more donut you get in your mouth, the longer it takes you to swallow it all. Mmmmmm.... donuts.
".45 ACP - because shooting twice is just silly" - JSOP, 2010
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You can never have too much ammo - unless you're swimming, or on fire. - JSOP, 2010
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"Why don't you tie a kerosene-soaked rag around your ankles so the ants won't climb up and eat your candy ass." - Dale Earnhardt, 1997 -
When talking about Big O, we do not actually talk about time but data points (in and out). So O(N) means each input must be touched. Where as O(log N) means we must touch log N inputs to get our output. Time is relative. If it takes 10s to 'touch' an input then you can calculate how long it will take to calculate the output of a dataset of 1 million.
Computers have been intelligent for a long time now. It just so happens that the program writers are about as effective as a room full of monkeys trying to crank out a copy of Hamlet.
Collin Jasnoch wrote:
Where as O(log N) means we must touch log N inputs to get our output.
Only in the best case scenario; in the worse case, it can be very different and even worse.
Watched code never compiles.
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Collin Jasnoch wrote:
Where as O(log N) means we must touch log N inputs to get our output.
Only in the best case scenario; in the worse case, it can be very different and even worse.
Watched code never compiles.
Yes. You must touch Log N inputs and likely a few more... But not N and certainly not N^2 The notation comes from taking series out infinitely.
F(n) = (n)^-(1/5) + Log(n)then one can say it is of Log(n) complexity, because as n gets larger and larger the 5th root of n is neglagable and the Log(n) outweighs it.Computers have been intelligent for a long time now. It just so happens that the program writers are about as effective as a room full of monkeys trying to crank out a copy of Hamlet.
