SB 3.7.10: The living entity is in distress regarding his self-identity. He has no factual background, like a man who dreams that he sees his head cut off.

ving entity is in distress regarding his self-identity. He has no factual background, like a man who dreams that he sees his head cut off.

SB 3.7.11: As the moon reflected on water appears to the seer to tremble due to being associated with the quality of the water, so the self associated with matter appears to be qualified as matter.

SB 3.7.12: But that misconception of self-identity can be diminished gradually by the mercy of the Personality of Godhead, Vāsudeva, through the process of devotional service to the Lord in the mode of detachment.

SB 3.7.13: When the senses are satisfied in the seer-Supersoul, the Personality of Godhead, and merge in Him, all miseries are completely vanquished, as after a sound sleep.

SB 3.7.14: Simply by chanting and hearing of the transcendental name, form, etc., of the Personality of Godhead, Śrī Kṛṣṇa, one can achieve the cessation of unlimited miserable conditions. Therefore what to speak of those who have attained attraction for serving the flavor of the dust of the Lord’s lotus feet?

hope you like the above sayings about devotiom tpwardsLord krishna

]]>The max variables used in the code were not being updated earlier. That has now been changed. So it might work fine now. However, I must add a disclaimer that I have not tested this code, so it’s possible it might not still work.

]]>has anyone implemented the code ?

i did and it shows a wrong answer ]]>

http://hareykrishna.com/ramayana/ ]]>

Your comment was so full of gratitude – thanks. Constructors and copy constructors are interesting topics to write on. There are a couple of books by Scott Myers on C++ which are good.

Ramesh

]]>Came across this article when i was desperately looking for some good explanation on copy constructor and copy assignment operator. Its an excellent article. Keep writing. BTW thanks.

Sid

]]>Just so as to clarify, I did not translate the sloka into English. As mentioned at the end of the blog post, I took the translation from this site – http://www.vijayadhwani.com/2009/03/nama-ramayana-sloka-shuddha-brahma.html. .

I listened to the Ramadaasu movie version on youtube today. That is also nice.

Thanks.

]]>What I wanted to add is this – this was a maximization problem. Our task was to produce a cut of as large a size as possible. Say the maximum-sized cut for instance, i, is of size OPT (i). Then, an algorithm that, for all instances i, returns a cut of size >= m \times OPT (i), is called an m-approximation algorithm. (Note that m is always <= 1.) For our case, m was = 1/2. So, we have a 1/2 – approximation algorithm.

Note that some authors might refer to what we called an algorithm with approximation factor 1/2 as an algorithm with approximation factor 2, an algorithm with approximation factor 1/3 as one with approximation 3, and so on.

]]>What you said is correct – OPT can |E| in some cases, but not in all cases.

What I think I meant by “the cut produced by the algorithm is always within a factor of 1/2 of the optimal” is this : the number of edges crossing the cut our algorithm produces is >= OPT / 2.

To see why the algorithm always produces a cut of size >= OPT / 2, look at these 2 statements:

(Let y denote the size of the cut produced by the algorithm.)

1. OPT = |E| / 2. (we can see this in Observation 3 in the blog post.)

From these 2 statements, we can see that : y >= OPT / 2.

Now, we know that OPT can at most be |E|. Therefore,

(This was a maximization problem. Our task was to produce a cut that has as many edges as possible crossing it. Hence, an

[ In the blog post, Observation 3 was explained cryptically. Here is a more detailed explanation as to why the size of the cut produced by the algorithm (call it ‘y’ ) >= |E| / 2.

Now, at termination of the algorithm, we know that for every vertex, v, the number of incident edges crossing the cut is >= the number of incident edges not crossing the cut.

i.e. for every vertex v, the number of incident edges crossing the cut is >= d(v) / 2. (where d(v) denotes the degree of vertex v). ————— (observation ‘a’)

Now, what we want is the total number of edges crossing the cut. We take every vertex and sum up the number of incident edges crossing the cut. Now, we observe one thing that if we sum up the edges this way, we would be counting twice each of the edges that crosses the cut – once from each of the end vertices of that edge. (Therefore, we wll add a factor 1/2 in front of the summation.)

Therefore, using “observation ‘a’ “, the number of edges crossing the cut, y, is > = 1/2 [summation over all vertices, v] d(v) / 2.

We know that [summation over all vertices, v] d(v) = 2|E|.

Hence, we get that : y > = |E|/2.

]]>And I still have some doubt about the “1/2 factor”. I mean the OPT can be |E| in some cases, but it can’t always be |E|, so I have some doubts about the proof about ” it produces a cut that is always within a factor of 1/2 of the optimal” .

I read this problem in approximation algorithm and i can’t figure out a proof. Hope your response.

]]>Thanks for the kind words. I would, hopefully, post other software engineering interview-related material over the coming days. Thanks also for sharing the link on chemical engineering interview material. Have a nice day.

Yours sincerely,

Ramesh

You can find some Chemical Engineering Interview Question Answers in the below link

http://www.aired.in/2011/06/chemical-engineering-collection-of.html

Thanks

Joya