Polynomial decomposition algorithms
Journal of Symbolic Computation
Functional decomposition ofpolynomials: the tame case
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Functional decomposition of polynomials: The wild case
Journal of Symbolic Computation
Decomposition of algebraic functions
Journal of Symbolic Computation
Modern computer algebra
Polynomial Decomposition Algorithms
Polynomial Decomposition Algorithms
Computer algebra handbook
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Counting reducible and singular bivariate polynomials
Proceedings of the 2007 international symposium on Symbolic and algebraic computation
Functional decomposition of polynomials
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
On Ritt's decomposition theorem in the case of finite fields
Finite Fields and Their Applications
Finite Fields and Their Applications
Composition collisions and projective polynomials: statement of results
Proceedings of the 2010 International Symposium on Symbolic and Algebraic Computation
Decomposition of generic multivariate polynomials
Proceedings of the 2010 International Symposium on Symbolic and Algebraic Computation
On multivariate homogeneous polynomial decomposition
CASC'10 Proceedings of the 12th international conference on Computer algebra in scientific computing
Lower bounds for decomposable univariate wild polynomials
Journal of Symbolic Computation
Compositions and collisions at degree p2
Proceedings of the 37th International Symposium on Symbolic and Algebraic Computation
Compositions and collisions at degree p 2
Journal of Symbolic Computation
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A univariate polynomial f over a field is decomposable if it is the composition f = g ⊕ h of two polynomials g and h whose degree is at least 2. We determine an approximation to the number of decomposable polynomials over a finite field. The tame case, where the field characteristic p does not divide the degree n of f, is reasonably well understood, and we obtain exponentially decreasing error bounds. The wild case, where p divides n, is more challenging and our error bounds are weaker. A centerpiece of our approach is a decomposition algorithm in the wild case, which shows that sufficiently many polynomials are decomposable.