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The duck appeared at the bottom right corner of the browser viewport, and attempted to help visitors by listening to their problems and responding with solutions. However, the duck merely produced a quack sound after apparently thinking and typing. It referenced rubber ducking as a powerful method for solving problems. [9]
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]
Computational thinking (CT) refers to the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms. [1] In education, CT is a set of problem-solving methods that involve expressing problems and their solutions in ways that a computer could also execute. [2]
It is one of Karp's 21 NP-complete problems shown to be NP-complete in 1972. The optimization/search version of set cover is NP-hard . [ 1 ] It is a problem "whose study has led to the development of fundamental techniques for the entire field" of approximation algorithms .
David Jay Malan (/ m eɪ l ɛ n /) is an American computer scientist and professor. Malan is a 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.
In ASP, search problems are reduced to computing stable models, and answer set solvers—programs for generating stable models—are used to perform search. The computational process employed in the design of many answer set solvers is an enhancement of the DPLL algorithm and, in principle, it always terminates (unlike Prolog query evaluation ...
Divide and conquer is a powerful tool for solving conceptually difficult problems: all it requires is a way of breaking the problem into sub-problems, of solving the trivial cases, and of combining sub-problems to the original problem.