I am not certainly not in full speed in my algorithmic study. There are two other priorities in my life which are competing.
One is work. Though I don't perfectly happy with the work environment, I still consider a must to do a good job in work.
The other is languages. I spent some of my time in Spanish, some of time in French. My original intention of learning them is to open my mind. In fact, I always find learning a language do some special magic in my mind. I seem to be ...... smarter. I also feel I realize something I don't usually do.
What does language learning teach me? To me, the biggest lesson is that not everyone is learning correctly. They put stress on something unimportant. For example, if you are bad in Spanish subjunctives, what should you do? In concrete, if you do an grammar exercise which you got them wrong all the time for the first time. what should you do?
This is the moment many people would think that they are "not good enough" or "smart enough". Some would even decide to give up. Here is a correct thing to do : treat it like a sport exercises, do it again and again until you come to perfection. The thing is you might not be talented in Spanish, but you might be talented in learning.
You might say if I am not learning this fast enough. Doesn't that mean I am not talented enough? Doesn't that mean I should just work on something else which my talents are better used? Again, I think this is a wrong mindset.
Why? Here is the reason. If you have the tendency to give up learning one thing, so why not give up another one? why not give up the third thing? why not give up the forth, ...... or the N-th thing?
Why not? Once you admit that there can be something beyond you. I think you need to admit many things else to be beyond you. It's true scientifically that you can't be No.1 in everything. But it's also true that your potential is way more than what you have now. So a better Why-not here :
Why not go for it? Why not keep it on? Why not I try harder? Why not changing my strategy to solve this problem?
That's why when I decide to do something or solve a problem, I usually wrestle with it until the problem succumb to me. It's a problem that takes a long time to think about? I wrote it down to a notebook. If it's a problem that has been solved, I wouldn't waste it, I will try to think of another solution. How come one should feel happy with an existing and old solution? Why not come up with someone simpler? Faster? Generalize the solution to something more interesting?
Admittedly, my learning in last week or so has been slowed down. Also, there starts to be some topics which are tougher than what can I understand. But I won't give up. Because I know, there is nothing so difficult that I can't thoroughly understand in algorithm. There are only the wrong method to learn, the wrong explanation, the over-complicated thinking. And those issues can be resolved. (Hell, they can be) . Wrong method to learn can be corrected. A correct explanation can always be looked up and you can always ask an expert. You can always simplify your thought.
There is nothing related to whether one person is smart or not. He/she can just learn. So I am not going to be deterred by the thousands of discouragement I got. I am just going to learn.
This is my self-study page for the book, "Introduction to Algorithm", or commonly known as CLRS. This is also my diary page of how I struggle and grow in the programming world. I hope this blog can help amateurs or professionals, to improve their skills in programming, learning and living. As of Sep 12, 2011, I finished the "exercise read" of Chapter 2 (20110518) and 3 (20110608) and half of Chapter 4.
Shortcut for chapter specific information
Chapter4
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chapter3
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Chapter2
(22)
chapter6
(10)
chapter12
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chapter15
(8)
chapter13
(7)
chapter7
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Chapter10
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chapter5
(5)
Appendix A
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chapter8
(4)
Chapter19
(3)
Chapter22
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Chapter34
(3)
Chapter35
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chapter11
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chapter16
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chapter18
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Appendix C
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Chapter21
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Chapter25
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Chapter26
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Chapter27
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Chapter28
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Chapter29
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Chapter9
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chapter14
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chapter20
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chapter23
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chapter24
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chapter30
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chapter31
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chapter32
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Appendix D
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Chapter1
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Chapter33
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chapter17
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