feat(book): show companies asked questions

book/content/part01/big-o-examples.asc

@@ -23,7 +23,7 @@ Before we dive in, here’s a plot with all of them.
 
 .CPU operations vs. Algorithm runtime as the input size grows
 // image::image5.png[CPU time needed vs. Algorithm runtime as the input size increases]
-image::big-o-running-time-complexity.png[CPU time needed vs. Algorithm runtime as the input size increases]
+image::time-complexity-manual.png[{half-size}]
 
 The above chart shows how the algorithm's running time is related to the CPU's work. As you can see, O(1) and O(log n) is very scalable. However, O(n^2^) and worst can convert your CPU into a furnace 🔥 for massive inputs.
 

book/content/part02/array.asc

@@ -522,7 +522,7 @@ maxSubArray([-3, 4,-1, 2, 1, -5]); // 6 (sum [4,-1, 2, 1])
 maxSubArray([-2, 1, -3, 4, -1, 3, 1]); // 7 (sum [4,-1, 3, 1])
 ----
 
-// _Seen in interviews at: Amazon, Apple, Google, Microsoft, Facebook_
+_Common in interviews at: Amazon, Apple, Google, Microsoft, Facebook_
 // end::array-q-max-subarray[]
 
 [source, javascript]
@@ -548,7 +548,7 @@ maxProfit([3, 2, 1]) // 2 (no buys)
 maxProfit([5, 10, 5, 10]) // 5 (buying at 5 and selling at 10)
 ----
 
-// _Seen in interviews at: Amazon, Facebook, Bloomberg_
+_Common in interviews at: Amazon, Facebook, Bloomberg_
 // end::array-q-buy-sell-stock[]
 
 [source, javascript]

book/content/part02/hash-map.asc

@@ -627,7 +627,7 @@ Something that might look unnecessary is the `Math.max` when updating the `lo` p
 
 // end::hashmap-q-two-sum[]
 
-// _Seen in interviews at: Amazon, Google, Apple._
+_Common in interviews at: Amazon, Google, Apple._
 
 Examples:
 
@@ -656,7 +656,7 @@ _Solution: <<hashmap-q-two-sum>>_
 
 // end::hashmap-q-subarray-sum-equals-k[]
 
-// _Seen in interviews at: Facebook, Google, Amazon_
+_Common in interviews at: Facebook, Google, Amazon_
 
 Examples:
 

book/content/part02/hash-set.asc

@@ -129,7 +129,7 @@ Find the most common word that is not on the banned list.
 You might need to sanitize the text and strip out punctuation `?!,'.`_
 // end::set-q-most-common-word[]
 
-// _Seen in interviews at: Amazon._
+_Common in interviews at: Amazon._
 
 Examples:
 
@@ -173,7 +173,7 @@ _Solution: <<set-q-most-common-word>>_
 
 // end::set-q-longest-substring-without-repeating-characters[]
 
-// _Seen in interviews at: Amazon, Facebook, Bloomberg._
+_Common in interviews at: Amazon, Facebook, Bloomberg._
 
 Examples:
 

book/content/part02/linked-list.asc

@@ -584,7 +584,7 @@ mergeTwoLists(2->3->4, 1->2); // 1->2->2->3->4
 mergeTwoLists(2->3->4,null); // 2->3->4
 ----
 
-// _Seen in interviews at: Amazon, Adobe, Microsoft, Google_
+_Common in interviews at: Amazon, Adobe, Microsoft, Google_
 // end::linkedlist-q-merge-lists[]
 
 [source, javascript]
@@ -612,7 +612,7 @@ hasSameData(hello, hel->lo); // true
 hasSameData(he->ll->o, h->i); // false
 ----
 
-// _Seen in interviews at: Facebook_
+_Common in interviews at: Facebook_
 // end::linkedlist-q-linkedlist-same-data[]
 
 [source, javascript]

book/content/part02/queue.asc

@@ -103,7 +103,7 @@ counter.request(3100); // 1 (last requests was 100 ms ago, > 10ms, so doesn't co
 counter.request(3105); // 2 (last requests was 5 ms ago, <= 10ms, so it counts)
 ----
 
-// _Seen in interviews at: Google, Bloomberg, Yandex_
+_Common in interviews at: Google, Bloomberg, Yandex_
 // end::queue-q-recent-counter[]
 
 
@@ -135,7 +135,7 @@ expect(snakeGame.move('L')).toEqual(2); //  2 (ate food2)
 expect(snakeGame.move('U')).toEqual(-1); // -1 (hit wall)
 ----
 
-// _Seen in interviews at: Amazon, Bloomberg, Apple_
+_Common in interviews at: Amazon, Bloomberg, Apple_
 // end::queue-q-design-snake-game[]
 
 [source, javascript]

book/content/part02/stack.asc

@@ -106,7 +106,7 @@ isParenthesesValid('[{]}'); // false (brakets are not closed in the right order)
 isParenthesesValid('([{)}]'); // false (closing is out of order)
 ----
 
-// _Seen in interviews at: Amazon, Bloomberg, Facebook, Citadel_
+_Common in interviews at: Amazon, Bloomberg, Facebook, Citadel_
 // end::stack-q-valid-parentheses[]
 
 [source, javascript]
@@ -135,7 +135,7 @@ dailyTemperatures([30, 28, 50, 40, 30]); // [2 (to 50), 1 (to 28), 0, 0, 0]
 dailyTemperatures([73, 69, 72, 76, 73, 100]); // [3, 1, 1, 0, 1, 100]
 ----
 
-// _Seen in interviews at: Amazon, Adobe, Cisco_
+_Common in interviews at: Amazon, Adobe, Cisco_
 // end::stack-q-daily-temperatures[]
 
 [source, javascript]

book/content/part03/binary-search-tree-traversal.asc

@@ -105,7 +105,7 @@ Post-order traverval will return `3, 4, 5, 15, 40, 30, 10`.
 
 // end::binary-tree-q-diameter-of-binary-tree[]
 
-// _Seen in interviews at: Facebook, Amazon, Google_
+_Common in interviews at: Facebook, Amazon, Google_
 
 // Example 1:
 // [graphviz, tree-diameter-example-1, png]
@@ -203,7 +203,7 @@ _Solution: <<binary-tree-q-diameter-of-binary-tree>>_
 
 // end::binary-tree-q-binary-tree-right-side-view[]
 
-// _Seen in interviews at: Facebook, Amazon, ByteDance (TikTok)._
+_Common in interviews at: Facebook, Amazon, ByteDance (TikTok)._
 
 Examples:
 

book/content/part03/graph-search.asc

@@ -102,7 +102,7 @@ NOTE: Every tree is a graph, but not every graph is a tree. Only acyclic directe
 
 // end::graph-q-course-schedule[]
 
-// _Seen in interviews at: Amazon, Facebook, Bytedance (TikTok)._
+_Common in interviews at: Amazon, Facebook, Bytedance (TikTok)._
 
 
 *Starter code*:
@@ -155,7 +155,7 @@ _Solution: <<graph-q-course-schedule>>_
 
 // end::graph-q-critical-connections-in-a-network[]
 
-// _Seen in interviews at: Amazon, Google._
+_Common in interviews at: Amazon, Google._
 
 Examples:
 

book/content/part04/algorithmic-toolbox.asc

@@ -24,7 +24,7 @@ TIP: TL;DR: Don't start coding right away. First, solve the problem, then write
 . *Test* your algorithm idea with multiple examples
 . *Optimize* the solution –Only optimize when you have something working. Don't try to do both at the same time!
 .. Can you trade-off space for speed? Use a <<hashmap-chap>> to speed up results!
-.. Do you have a bunch of recursive and overlapping problems? Try <<Dynamic Programming>>.
+.. Do you have a bunch of recursive and overlapping problems? Try <<dynamic-programming-chap>>.
 .. Re-read requirements and see if you can take advantage of anything. E.g. is the array sorted?
 . *Write Code*, yes, now you can code.
 .. Modularize your code with functions (don't do it all in one giant function, please 🙏)

book/content/part04/divide-and-conquer.asc

@@ -14,7 +14,7 @@ We have already implemented some algorithms using the divide and conquer techniq
 
 .Examples of divide and conquer algorithms:
 - <<part04-algorithmic-toolbox#merge-sort>>: *divides* the input into pairs, sort them, and them *join* all the pieces in ascending order.
-- <<part04-algorithmic-toolbox#quicksort>>: *splits* the data by a random number called "pivot," then move everything smaller than the pivot to the left and anything more significant to the right. Repeat the process on the left and right sides. Note: since this works in place doesn't need a "join" part.
+- <<quicksort-chap>>: *splits* the data by a random number called "pivot," then move everything smaller than the pivot to the left and anything more significant to the right. Repeat the process on the left and right sides. Note: since this works in place doesn't need a "join" part.
 - <<part01-algorithms-analysis#logarithmic-example, Binary Search>>: find a value in a sorted collection by *splitting* the data in half until it sees the value.
 - <<part01-algorithms-analysis#factorial-example, Permutations>>: *Take out* the first element from the input and solve permutation for the remainder of the data recursively, then *join* results and append the items that were taken out.
 
@@ -117,7 +117,7 @@ For these cases, when subproblems repeat themselves, we can optimize them using
 
 // // end::divide-and-conquer-q-FILENAME[]
 
-// // _Seen in interviews at: X._
+// _Common in interviews at: X._
 
 // *Starter code*:
 
@@ -148,7 +148,7 @@ For these cases, when subproblems repeat themselves, we can optimize them using
 
 // // end::divide-and-conquer-q-FILENAME[]
 
-// // _Seen in interviews at: X._
+// _Common in interviews at: X._
 
 // *Starter code*:
 

book/content/part04/dynamic-programming.asc

@@ -5,6 +5,7 @@ endif::[]
 
 (((Dynamic Programming)))
 (((Algorithmic Techniques, Dynamic Programming)))
+[[dynamic-programming-chap]]
 === Dynamic Programming
 
 Dynamic programming (DP) is a way to solve algorithmic problems with *overlapping subproblems*. Algorithms using DP find the base case and building a solution from the ground-up. Dp _keep track_ of previous results to avoid re-computing the same operations.

book/content/part04/quick-sort.asc

@@ -3,10 +3,11 @@ ifndef::imagesdir[]
 :codedir: ../../../src
 endif::[]
 
-[[Quicksort]]
-==== Quicksort
 (((Sorting, QuickSort)))
 (((QuickSort)))
+[[quicksort-chap]]
+==== Quicksort
+
 Quicksort is an efficient recursive sorting algorithm that uses <<Divide and Conquer, divide and conquer>> paradigm to sort faster. It can be implemented in-place, so it doesn't require additional memory.
 
 indexterm:[Divide and Conquer]

book/content/part04/sorting-algorithms.asc

@@ -15,7 +15,7 @@ We can sort to get the maximum or minimum value, and many algorithmic problems c
 
 .and then discuss efficient sorting algorithms _O(n log n)_ such as:
 - <<part04-algorithmic-toolbox#merge-sort>>
-- <<part04-algorithmic-toolbox#quicksort>>
+- <<quicksort-chap>>
 
 Before we dive into the most well-known sorting algorithms, let's discuss the sorting properties.
 
@@ -124,7 +124,7 @@ We explored the most common sorting algorithms, some of which are simple and oth
 | <<part04-algorithmic-toolbox#insertion-sort>> | Look for biggest number to the left and swap it with current
 | <<part04-algorithmic-toolbox#selection-sort>> | Iterate array looking for smallest value to the right
 | <<part04-algorithmic-toolbox#merge-sort>>     | Split numbers in pairs, sort pairs and join them in ascending order
-| <<part04-algorithmic-toolbox#quicksort>>      | Choose a pivot, set smaller values to the left and bigger to the right.
+| <<quicksort-chap>>      | Choose a pivot, set smaller values to the left and bigger to the right.
 // | Tim sort        | Hybrid of merge sort and insertion sort
 |===
 
@@ -135,7 +135,7 @@ We explored the most common sorting algorithms, some of which are simple and oth
 | <<part04-algorithmic-toolbox#insertion-sort>> | O(n^2^)    | O(n) | O(n^2^) | O(1)      | Yes    | Yes      | Yes    | Yes
 | <<part04-algorithmic-toolbox#selection-sort>> | O(n^2^)    | O(n^2^) | O(n^2^) | O(1)        | No     | Yes      | No    | No
 | <<part04-algorithmic-toolbox#merge-sort>>     | O(n log n) | O(n log n) | O(n log n) | O(n)        | Yes     | No       | No     | No
-| <<part04-algorithmic-toolbox#quicksort>>      | O(n log n) | O(n log n) | O(n^2^) | O(log n) | No     | Yes      | No     | No
+| <<quicksort-chap>>      | O(n log n) | O(n log n) | O(n^2^) | O(log n) | No     | Yes      | No     | No
 // | Tim sort       | O(n log n) | O(log n)    | Yes    | No       | No     | Yes
 |===
 // end::table[]
@@ -162,7 +162,7 @@ We explored the most common sorting algorithms, some of which are simple and oth
 
 // end::sorting-q-merge-intervals[]
 
-// _Seen in interviews at: Facebook, Amazon, Bloomberg._
+_Common in interviews at: Facebook, Amazon, Bloomberg._
 
 *Starter code*:
 
@@ -195,7 +195,7 @@ _Solution: <<sorting-q-merge-intervals>>_
 
 // end::sorting-q-sort-colors[]
 
-// _Seen in interviews at: Amazon, Microsoft, Facebook._
+_Common in interviews at: Amazon, Microsoft, Facebook._
 
 *Starter code*:
 

book/part04-algorithmic-toolbox.asc

@@ -14,7 +14,7 @@ Later, you will learn some algorithmic paradigms that will help you identify com
 
 .We are going to discuss the following techniques for solving algorithms problems:
 - <<Greedy Algorithms>>: makes greedy choices using heuristics to find the best solution without looking back.
-- <<Dynamic Programming>>: a technique for speeding up recursive algorithms when there are many _overlapping subproblems_. It uses _memoization_ to avoid duplicating work.
+- <<dynamic-programming-chap>>: a technique for speeding up recursive algorithms when there are many _overlapping subproblems_. It uses _memoization_ to avoid duplicating work.
 - <<Divide and Conquer>>: _divide_ problems into smaller pieces, _conquer_ each subproblem, and then _join_ the results.
 - <<Backtracking>>: search _all (or some)_ possible paths. However, it stops and _go back_ as soon as notice the current solution is not working.
 - _Brute Force_: generate all possible solutions and tries all of them. (Use it as a last resort or as the starting point and to later optimize it).