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The name is a reference to a story in the book The Pragmatic Programmer in which a programmer would carry around a rubber duck and debug their code by forcing themselves to explain it, line by line, to the duck. [1] Many other terms exist for this technique, often involving different (usually) inanimate objects, or pets such as a dog or a cat.
CS32 (Computational Thinking and Problem Solving), taught by Michael D. Smith, [29] is an alternative to CS50 but does not have a free online version. [30] The next course in sequence after CS32 or CS50 is CS51: Abstraction and Design in Computation, instructed by Stuart M. Shieber with Brian Yu as co-instructor. [31]
Problems related to constraint programming and artificial intelligence are also popular in certain competitions. Irrespective of the problem category, the process of solving a problem can be divided into two broad steps: constructing an efficient algorithm , and implementing the algorithm in a suitable programming language (the set of ...
David Jay Malan (/ m eɪ l ɛ n /) is an American computer scientist and professor. Malan is Gordon McKay Professor of Computer Science at Harvard University, and is best known for teaching the course CS50, [2] [3] which is the largest open-learning course at Harvard University and Yale University and the largest massive open online course at EdX, with lectures being viewed by over a million ...
Constraint programming (CP) [1] is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. In constraint programming, users declaratively state the constraints on the feasible solutions for a set of decision variables.
The knapsack problem is one of the most studied problems in combinatorial optimization, with many real-life applications. For this reason, many special cases and generalizations have been examined. [1] [2] Common to all versions are a set of n items, with each item having an associated profit p j and weight w j.
Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables , which is solved by constraint satisfaction methods.
In its most general formulation, there is a multiset of integers and a target-sum , and the question is to decide whether any subset of the integers sum to precisely . [1] The problem is known to be NP-complete. Moreover, some restricted variants of it are NP-complete too, for example: [1]