This lecture introduces search algorithms. It also reviews what we have learned about time-complexity. These lectures use Python for illustration, but we will present code in C++. The point of the lectures is to understand the background of the various approaches; don't focus on Python specifically. This contrasts with other teaching approaches that start with the syntax of a particular language.
We begin with this particular lecture, since it introduces concepts that are important to understanding search and sort, graphs and hashing, and the scalability of various approaches. Though we will look at some other lectures in this series, the entire lecture series covers more than this course's purview, so we will not directly engage with all of them. However, you may wish to check out the course page for the original course at MIT, if you'd like more information on what is covered here.
"Complexity" refers to ALL resources consumed by an algorithm, although most people speak only of runtime (processing time, time to run successfully to completion). Refer to our earlier discussion on time/memory tradeoffs. You must consider the overall cost of running an algorithm. For instance, memory is expensive in cloud computing, so you have to be careful about saving time by using algorithms that are memory-intensive. You also have to consider other resources such as network utilization as data is transferred from one place to another. As you work, continue to think in this manner.
Searching, sorting, graphs, and hashing, using arrays or other data structures, are typically discussed in the context of recursion. While these are "academically elegant" and arguably easier to explain, recursion is memory inefficient. Generally, it assumes that large amounts of memory are available. However, that is rarely the case in industrial applications, especially with embedded systems. Additionally, the memory cost for cloud-based systems is high. Constant expansion of memory use with recursive approaches can eat up a lot of money very quickly. Thus, recursion will not be emphasized here.