IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity theory of real functions
Complexity theory of real functions
Computational Complexity of Two-Dimensional Regions
SIAM Journal on Computing
Computable analysis: an introduction
Computable analysis: an introduction
Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
The computational complexity of some julia sets
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Geometric Tools for Computer Graphics
Geometric Tools for Computer Graphics
Hyperbolic Julia Sets are Poly-Time Computable
Electronic Notes in Theoretical Computer Science (ENTCS)
On the Complexity of Finding Paths in a Two-Dimensional Domain II: Piecewise Straight-Line Paths
Electronic Notes in Theoretical Computer Science (ENTCS)
A Fast Algorithm for Julia Sets of Hyperbolic Rational Functions
Electronic Notes in Theoretical Computer Science (ENTCS)
On the complexity of computing the logarithm and square root functions on a complex domain
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
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We investigate the computational complexity of finding the minimum-area circumscribed rectangle of a given two-dimensional domain. We study this problem in the polynomial-time complexity theory of real functions based on the oracle Turing machine model. We show that a bounded domain S with a polynomial-time computable Jordan curve @C as the boundary may not have a computable minimum-area circumscribed rectangle. We also show that the problem of finding the minimum area of a circumscribed rectangle of a polynomial-time computable Jordan curve @C is equivalent to a discrete @S"2^P-complete problem. The related problem of finding the circumscribed squares of a Jordan curve @C is also studied. We show that for any polynomial-time computable Jordan curve @C, there must exist at least one computable circumscribed square (not necessarily of the minimum area), but this square may have arbitrarily high complexity.