Log for YSITD
Assertion: "state->readTill >= tillId" data_histories.cpp:414
最新版 telegram desktop 是不是有 bug，十分鐘內噴掉兩次了
最新版 telegram desktop 是不是有 bug，十分鐘內噴掉兩次了
An algorithm is just a formally specified procedure for solving a computational problem. A computation problem is just a formal description of input and output. For example, the sorting problem can be defined as follows:
Input: A sequence of n numbers <a1, a2, ..., an> Output: A permutation (reordering) <a1', a2', ..., an'> of the input sequence satisfying the ordered property: a1' <= a2' <= ... <= an'
There's nothing special about the usage of the word
algorithm and what you write everydayAs long as you can explicitly define a computational task and the steps to compute, you are working with algorithms. However, I don't think many people can come up with well-known algorithms with their original research. Even if they can, it is a waste of time.
That is, of course, assumming the set {a1, a2... an} is a totally-ordered set
A few more example to ponder with: Given a set of vertices V of a directed acyclic graph, we can define a relation -> such that V is totally ordered.
You sure persons who didn't know what "algorithm" is will know what is "totally-ordered set"?
I'm sure people who did not know what algorithms mean will learn and not come up with their own definition
Just out of curiosity, how do you find this group, and why are you replying message in English instead of Chinese?
Now we are bestowed the ability to define computational problems, let's try to define a few of them.
1.
Input: A list of unique 32 bit integers plus 1 duplicate
Output: The duplicated integer
2.
Input: An imperative program
Output: The list of no longer used variables after executing the n-th machine instructions
3.
Input: A seed integer
Output: A stream of integers that is deterministic (entirely upon the seed), satisfying the uniform distribution.
1.
Input: A list of unique 32 bit integers plus 1 duplicate
Output: The duplicated integer
2.
Input: An imperative program
Output: The list of no longer used variables after executing the n-th machine instructions
3.
Input: A seed integer
Output: A stream of integers that is deterministic (entirely upon the seed), satisfying the uniform distribution.
By now we should've realized that a well-defined problem is a problem half solved. We should define what the problem is before defining an algorithm lol
We can readily supply an algorithm for (1) if we are acquainted with the ubiquitous set data structure.
However, we can do better. There is a O(n) time O(1) memory algorithm hidden in the wild, which is quite nontrivial. However, everyone with a basic understanding of data structure can devise the algorithm outlined above immediately.
let s be an empty set
for i in list:
if i in s:
return i
s.insert(i)However, we can do better. There is a O(n) time O(1) memory algorithm hidden in the wild, which is quite nontrivial. However, everyone with a basic understanding of data structure can devise the algorithm outlined above immediately.
(2) is sadly undecidable, a situation much more horrid than being NP-hard. If you're familiar with languages that have garbage collection facilities, you should be wondering, how do they even do that?
The truth is, you have been lied on... They implement gc using an approximation algorithm. They only clear all the dead nodes (the definition of dead node varies as the underlying algorithm changes), which is a subset of nodes no longer used
You can observe that the definition of dead node in the mark-sweep, reference counting, and Cherny algorithm are all different, as least slightly