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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]
CS50 (Computer Science 50) [a] is an introductory course on computer science taught at Harvard University by David J. Malan. The on-campus version of the course is Harvard's largest class with 800 students, 102 staff, and up to 2,200 participants in their regular hackathons .
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 ...
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 ...
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 ...
Computational problems of this type are called promise problems. The following is an example of a (decision) promise problem: "Given a graph G, determine if every independent set in G has size at most 5, or G has an independent set of size at least 10."
Even though the total number of sub-problems is actually small (only 43 of them), we end up solving the same problems over and over if we adopt a naive recursive solution such as this. Dynamic programming takes account of this fact and solves each sub-problem only once. Figure 2. The subproblem graph for the Fibonacci sequence.
A penalty method replaces a constrained optimization problem by a series of unconstrained problems whose solutions ideally converge to the solution of the original constrained problem. The unconstrained problems are formed by adding a term, called a penalty function , to the objective function that consists of a penalty parameter multiplied by ...