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In computer programming, a scientific programming language can refer to two degrees of the same concept. In a wide sense, a scientific programming language is a programming language that is used widely for computational science and computational mathematics. In this sense, C/C++ and Python can be considered scientific programming languages.
The quadratic programming problem with n variables and m constraints can be formulated as follows. [2] Given: a real-valued, n-dimensional vector c, an n×n-dimensional real symmetric matrix Q, an m×n-dimensional real matrix A, and; an m-dimensional real vector b, the objective of quadratic programming is to find an n-dimensional vector x ...
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 software engineering, rubber duck debugging (or rubberducking) is a method of debugging code by articulating a problem in spoken or written natural language. 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 ...
Solving the general non-convex case is an NP-hard problem. To see this, note that the two constraints x 1 ( x 1 − 1) ≤ 0 and x 1 ( x 1 − 1) ≥ 0 are equivalent to the constraint x 1 ( x 1 − 1) = 0, which is in turn equivalent to the constraint x 1 ∈ {0, 1}.
In a typical backtracking solution to this problem, one could define a partial candidate as a list of integers c = (c[1], c[2], …, c[k]), for any k between 0 and n, that are to be assigned to the first k variables x[1], x[2], …, x[k]. The root candidate would then be the empty list (). The first and next procedures would then be
A declarative programming program describes what the problem is, not how to solve it. The program is structured as a set of properties to find in the expected result, not as a procedure to follow. Given a database or a set of rules, the computer tries to find a solution matching all the desired properties.
Since a D&C algorithm eventually reduces each problem or sub-problem instance to a large number of base instances, these often dominate the overall cost of the algorithm, especially when the splitting/joining overhead is low. Note that these considerations do not depend on whether recursion is implemented by the compiler or by an explicit stack.