The CS Detective: An Algorithmic Tale of Crime, Conspiracy, by Jeremy Kubica

By Jeremy Kubica

Meet Frank Runtime. Disgraced ex-detective. Hard-boiled deepest eye. seek expert.

When a theft hits police headquarters, it is as much as Frank Runtime and his huge seek abilities to capture the culprits. during this detective tale, you are going to how you can use algorithmic instruments to resolve the case. Runtime scours smugglers' boats with binary seek, tails spies with a seek tree, escapes a jail with depth-first seek, and selections locks with precedence queues. Joined by means of know-it-all rookie Officer Notation and inept tag-along Socks, he follows a sequence of leads in a best-first seek that unravels a deep conspiracy. every one bankruptcy introduces an exhilarating twist matched with a brand new algorithmic thought, finishing with a technical recap.

Perfect for desktop technology scholars and beginner sleuths alike, The CS Detective provides an pleasing twist to studying algorithms.

Follow Frank's project and learn:

  • The algorithms in the back of best-first and depth-first seek, iterative deepening, parallelizing, binary seek, and more
  • Basic computational ideas like strings, arrays, stacks, and queues
  • How to evolve seek algorithms to strange information structures
  • The most productive algorithms to take advantage of in a given state of affairs, and while to use common sense heuristic methods

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Extra info for The CS Detective: An Algorithmic Tale of Crime, Conspiracy, and Computation

Sample text

Do you pick random doors, running back and forth until you get lucky? No! You search down the hall, kicking in one door at a time. Or consider an algorithm that scans a list of numbers (an array), searching for a target value. The algorithm moves along the list from number to number, checking each value in turn so as not to miss any, and stops when it reaches the target. If we are searching an array for the number 5, then the search would progress as follows: The advantage of linear search algorithms is that they are simple to implement in the field and they work even on unstructured data.

He asked unconvincingly. His eyes darted around the room, but, as always, nobody paid him any attention. Frank laid two gold pieces on the table, ignoring the sour feeling in his gut. He couldn舗t afford to spend this type of money, especially without knowing if he was paying for a lead or idle gossip. But he舗d known this wasn舗t going to be cheap. He leaned in close. 舡 舠Doesn舗t sound like the sort of thing that would be healthy to remember,舡 said Billy. He eyed the gold pieces. 舡 舠That舗s gold,舡 Frank growled.

As the search progresses, the bounds will move closer until there are no unexplored values between them. Since we are always moving one of the bounds past the midpoint index, we can stop the search when IndexHigh < IndexLow. At that point we can guarantee the target value is not in the array. 舒6舒 Binary Search for Clues Food inspectors,舡 Frank called out as he and Officer Notation stepped up the narrow gangplank and onto the ship. At Frank舗s instigation, Notation waved her badge in a blur, too fast for anyone to read.

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