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Commit 15f7008

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graph search docs
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‎book/book.adoc‎

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@@ -84,7 +84,6 @@ include::chapters/tree--search.adoc[]
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include::chapters/tree--self-balancing-rotations.adoc[]
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include::chapters/tree--avl.adoc[]

‎book/chapters/algorithms-analysis.adoc‎

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Algorithms (as you might know) are steps of how to do some task. When you cook, you follow a recipe (or an algorithm) to prepare a dish. Let's say you want to make a pizza.
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.Example of an algorithm
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//[source, js]
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//[source, js] // undefined functions
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----
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function bakePizza(dough, toppins = []) {
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function bakePizza(dough, toppins = ['cheese']) {
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const heatedOven = heatOvenTo(550);
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punchDown(dough);
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rollOut(dough);
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applyToppings(dough, toppings);
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const pizza = heatedOven.bakePizza(dough)
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return pizza;
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}
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bakePizza(new Dough, ['ham', 'cheese']);
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----
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If you play a game, you are devising strategies (or algorithms) to help you win. Likewise, algorithms in computers are a set of instructions used to solve a problem.

‎book/chapters/cheatsheet.adoc‎

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{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
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Time complexity for a Graph data structure
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.Time complexity for a Graph data structure
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|===
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.2+.^s| Data Structure 2+^s| Vertices 2+^s| Edges .2+^.^s| Space Complexity
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^|_Add_ ^|_Remove_ ^|_Add_ ^|_Remove_
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== Sorting Algorithms
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.Sorting algorithms comparison
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|===
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| Algorithms | Runtime | Space | Stable | In-place | Online | Adaptive | Comments
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| Insertion sort | O(n^2^) | O(1) | Yes | Yes | Yes | Yes |

‎book/chapters/graph-search.adoc‎

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= Graph Search
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Quis officia proident fugiat elit cupidatat voluptate. Aliqua anim nulla nulla nisi anim cupidatat eu culpa velit cupidatat. Id duis do esse aliquip fugiat non ea. Fugiat dolore aliquip eu commodo. Sint ex et ullamco laboris consequat non sint id.
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Graph search allows you to visit all the elements connected given a starting node. There are two ways to navigate the graph, one is using Depth-First Search (DFS) and the other one is Breadth-First Search (BFS). Let's see the difference.
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== DepthFirst Search
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== Depth-First Search
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Commodo culpa qui officia eiusmod irure culpa elit excepteur culpa. Tempor aute nisi excepteur qui. Sit in irure aliqua Lorem tempor ut minim. Magna amet nisi sint labore tempor in commodo consequat dolor cupidatat cillum non pariatur reprehenderit. Ex esse cupidatat sunt dolore non commodo est aute.
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As we saw in <<Depth-First Search for Binary Tree>>, depth-first search goes as deep as it can and then backs down visiting adjacent nodes.
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== Breadth First Search
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https://www.google.com/search?q=graph+dfs+vs+bfs&rlz=1C5CHFA_enUS818US818&source=lnms&tbm=isch&sa=X&ved=0ahUKEwimx4bs-uHgAhVNeKwKHS-aATMQ_AUIDigB&biw=1384&bih=977#imgrc=oGB49w2DS4afkM:
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== Breadth-First Search
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‎book/chapters/tree--search.adoc‎

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Let's cover Breadth-first search (BFS) and Depth-first search (DFS).
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== Breadth-First Search
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== Breadth-First Search for Binary Tree
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Breadth-first search goeas wide (breadth) before going deep. Hence, the name. In other words, it goes level by level. It visits all the inmediate nodes or children and then move on to the children's children.
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Let's how can we implement it!
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****
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== Depth-First Search
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== Depth-First Search for Binary Tree
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Depth-First search goes deep before going wide. It means, that starting for the root it goes as deep as it can until it found a leaf node (node without children), then it visits all the remaing nodes that were in the path.
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