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  2. Skeuomorph - Wikipedia

    en.wikipedia.org/wiki/Skeuomorph

    Mimesis is an imitation, coming directly from the Greek word. [9] Archetype is the original idea or model that is emulated, where the emulations can be skeuomorphic. [ 10 ] Skeuomorphism is parallel to, but different from, path dependence in technology, where an element's functional behavior is maintained even when the original reasons for its ...

  3. Circuit design - Wikipedia

    en.wikipedia.org/wiki/Circuit_design

    A practical constraint on the design at this stage is standardization;. At the same time, a certain value of a component may be calculated for use in some location in a circuit; if that value cannot be purchased from a supplier, then the problem has still not been solved.

  4. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    For very simple problems, say a function of two variables subject to a single equality constraint, it is most practical to apply the method of substitution. [4] The idea is to substitute the constraint into the objective function to create a composite function that incorporates the effect of the constraint.

  5. Galactic algorithm - Wikipedia

    en.wikipedia.org/wiki/Galactic_algorithm

    A galactic algorithm is an algorithm with record-breaking theoretical performance, but which is not used due to practical constraints.Typical reasons are that the performance gains only appear for problems that are so large they never occur, or the algorithm's complexity outweighs a relatively small gain in performance.

  6. Constraint satisfaction problem - Wikipedia

    en.wikipedia.org/wiki/Constraint_satisfaction...

    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.

  7. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    The equality constraint functions :, =, …,, are affine transformations, that is, of the form: () =, where is a vector and is a scalar. The feasible set C {\displaystyle C} of the optimization problem consists of all points x ∈ D {\displaystyle \mathbf {x} \in {\mathcal {D}}} satisfying the inequality and the equality constraints.

  8. Constraint (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Constraint_(mathematics)

    In mathematics, a constraint is a condition of an optimization problem that the solution must satisfy. There are several types of constraints—primarily equality constraints, inequality constraints, and integer constraints. The set of candidate solutions that satisfy all constraints is called the feasible set. [1]

  9. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    In this way, all lower bound constraints may be changed to non-negativity restrictions. Second, for each remaining inequality constraint, a new variable, called a slack variable, is introduced to change the constraint to an equality constraint. This variable represents the difference between the two sides of the inequality and is assumed to be ...