1. The algorithm course mainly talks about these three things:
State space
optimal feasible solution problem.
Certainty and non-determination.
You must master the state space way of thinking and be able to use state space to solve new problems.
2. Course arrangement:
You should also read the first few books, but the last one is highly recommended.
3. Speaking from experience:
Read more books! Read more books! If you don’t specify textbooks, for a certain specified problem, you should read more blogs
during the postgraduate stage. Don’t deceive yourself anymore!
For algorithms, read more SIAM, and for other topics, read more ACM, IEEE
4. Amortization analysis
I won’t talk about it anymore, but you can take a look.
5. Non-deterministic algorithms: randomness and approximation
To distinguish between "heuristic algorithms" and "random and approximate" algorithms
6. Relaxation operation
algorithm? The theory talks about relaxation operations, and the shortest path in graph theory uses relaxation operations.
7. Time complexity of Fibonacci sequence
Planned search == dynamic programming (will be discussed later)
How is O(logN) derived?
2^64 = (2^32)^2
2^32 = (2^16)^2
2^16 = (2^8)^2 2
^8= (2^4)^2
2^4 = (2 ^2)^2
2^2 = (2^1)^2
2^64 requires 63 multiplications, but after the above recursion, only 6 multiplications are required, so it is O(logN)
