• Jump search
• Binary search correct answer
• Interpolation search

• O(log(n*n))
• O(log(n)) correct answer
• O(n*log(n))

3. Which search algorithm is faster?

• Binary search correct answer
• Fibonacci search

4. Which is better for natural language encoding?

• Run-length encoding
• Diagram encoding
• Pattern substitution correct answer

5. What is the complexity of searching into a hash table?

• Quadratic
• Logaritmic
• Linear

Note! – here (my fault) there’s no correct answer. The correct answer is that searching into a hash table has constant complexity – O(1). However I’ll assume that all of you that answered with “Linear” are right!

Here’s a short optimization of a small chunk of jQuery code. The experiment was to increment the number of DOM element access. In this case this was a change of the innerHTML property – using the $.html() method.

I’ve measured the result with the console.time() method and thus I expect correct results for both cases. In the first case I directly call the jQuery selectors’ html() method:

for (var i = 0; i < 10000; i++) {
    $('#container').html(i);
}

While in the second case I "cache" it before the loop:

var t = $('#container');
for (var i = 0; i < 10000; i++) {
    t.html(i);
}

and here's the full code in the first case:

The Results

As expected the second "cached" approach gave better results. With the increment of the iterations the second method began to be slightly better than the first one. That's not so much, but imagine you have more than one selector in the loop's body?

Numbers:

Note that in the time is in ms, and, yes, this is not so much, but when you deal with large data and you've more than one selector in the loop's body, this can be critical!

Graphics: