Problem Solving

From

Overview of the Techniques

An overview of some of the common problem solving techniques:

• Divide and Conquer: Many problems, particularly when the input is given in an array, can be solved by cutting the problem into smaller pieces (divide), solving the smaller parts recursively (conquer), and then combining the solutions into a single result. Examples include the merge sort and quicksort algorithms.

• Randomization: Increasingly, randomization techniques are important for many applications. This chapter presents some classical algorithms that make use of random numbers.

• Backtracking: Almost any problem can be cast in some form as a backtracking algorithm. In backtracking, you consider all possible choices to solve a problem and recursively solve subproblems under the assumption that the choice is taken. The set of recursive calls generates a tree in which each set of choices in the tree is considered consecutively. Consequently, if a solution exists, it will eventually be found.

  Backtracking is generally an inefficient, brute-force technique, but there are optimizations that can be performed to reduce both the depth of the tree and the number of branches. The technique is called backtracking because after one leaf of the tree is visited, the algorithm will go back up the call stack (undoing choices that didn't lead to success), and then proceed down some other branch. To be solved with backtracking techniques, a problem needs to have some form of "self-similarity," that is, smaller instances of the problem (after a choice has been made) must resemble the original problem. Usually, problems can be generalized to become self-similar.

• Dynamic Programming: Dynamic programming is an optimization technique for backtracking algorithms. When subproblems need to be solved repeatedly (i.e., when there are many duplicate branches in the backtracking algorithm) time can be saved by solving all of the subproblems first (bottom-up, from smallest to largest) and storing the solution to each subproblem in a table. Thus, each subproblem is only visited and solved once instead of repeatedly. The "programming" in this technique's name comes from programming in the sense of writing things down in a table; for example, television programming is making a table of what shows will be broadcast when.

• Greedy Algorithms: A greedy algorithm can be useful when enough information is known about possible choices that "the best" choice can be determined without considering all possible choices. Typically, greedy algorithms are not challenging to write, but they are difficult to prove correct.

• Hill Climbing: The final technique we explore is hill climbing. The basic idea is to start with a poor solution to a problem, and then repeatedly apply optimizations to that solution until it becomes optimal or meets some other requirement. An important case of hill climbing is network flow. Despite the name, network flow is useful for many problems that describe relationships, so it's not just for computer networks. Many matching problems can be solved using network flow.