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Decomposition method is a generic term for solutions of various problems and design of algorithms in which the basic idea is to decompose the problem into subproblems. The term may specifically refer to: Decomposition method (constraint satisfaction) in constraint satisfaction
Surprisingly, under mild conditions on (using the spark (mathematics), the mutual coherence or the restricted isometry property) and the level of sparsity in the solution, , the sparse representation problem can be shown to have a unique solution, and BP and MP are guaranteed to find it perfectly. [1] [2] [3]
A decomposition with local endomorphism rings [5] (cf. #Azumaya's theorem): a direct sum of modules whose endomorphism rings are local rings (a ring is local if for each element x, either x or 1 − x is a unit). Serial decomposition: a direct sum of uniserial modules (a module is uniserial if the lattice of submodules is a finite chain [6]).
In mathematics, a polynomial decomposition expresses a polynomial f as the functional composition of polynomials g and h, where g and h have degree greater than 1; it is an algebraic functional decomposition. Algorithms are known for decomposing univariate polynomials in polynomial time.
Decomposition: This is a version of Schur decomposition where and only contain real numbers. One can always write A = V S V T {\displaystyle A=VSV^{\mathsf {T}}} where V is a real orthogonal matrix , V T {\displaystyle V^{\mathsf {T}}} is the transpose of V , and S is a block upper triangular matrix called the real Schur form .
For example, if s=2, then 𝜁(s) is the well-known series 1 + 1/4 + 1/9 + 1/16 + …, which strangely adds up to exactly 𝜋²/6. When s is a complex number—one that looks like a+b𝑖, using ...
Decomposition methods create a problem that is easy to solve from an arbitrary one. Each variable of this new problem is associated to a set of original variables; its domain contains tuples of values for the variables in the associated set; in particular, these are the tuples that satisfy a set of constraints over these variables.
The commented Poisson problem does not have a solution for any functional boundary conditions f 1, f 2, g 1, g 2; however, given f 1, f 2 it is always possible to find boundary functions g 1 *, g 2 * so close to g 1, g 2 as desired (in the weak convergence meaning) for which the problem has solution. This property makes it possible to solve ...
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