Showing papers by "Ming-Yang Kao published in 2014"

A manually-checkable proof for the NP-hardness of 11-color pattern self-assembly tile set synthesis

Abstract: Patterned self-assembly tile set synthesis (PATS) aims at finding a minimum tile set to uniquely self-assemble a given rectangular (color) pattern. For k >= 1, k-PATS is a variant of PATS that restricts input patterns to those with at most $k$ colors. A computer-assisted proof has been recently proposed for 2-PATS by Kari et al. [arXiv:1404.0967 (2014)]. In contrast, the best known manually-checkable proof is for the NP-hardness of 29-PATS by Johnsen, Kao, and Seki [ISAAC 2013, LNCS 8283, pp.~699-710]. We propose a manually-checkable proof for the NP-hardness of 11-PATS.

Computing Minimum Tile Sets to Self-Assemble Colors Patterns

Abstract: Patterned self-assembly tile set synthesis (PATS) aims at finding a minimum tile set to uniquely self-assemble a given rectangular color pattern. For $k \ge 1$, $k$-PATS is a variant of PATS that restricts input patterns to those with at most $k$ colors. We prove the {\bf NP}-hardness of 29-PATS, where the best known is that of 60-PATS.

Linear-time accurate lattice algorithms for tail conditional expectation

Abstract: This paper proposes novel lattice algorithms to compute tail conditional expectation of European calls and puts in linear time. We incorporate the technique of prefix-sum into tilting, trinomial, and extrapolation algorithms as well as some syntheses of these algorithms. Furthermore, we introduce fractional-step lattices to help reduce interpolation error in the extrapolation algorithms. We demonstrate the efficiency and accuracy of these algorithms with numerical results. A key finding is that combining the techniques of tilting lattice, extrapolation, and fractional steps substantially increases speed and accuracy.

Optimal search for parameters in Monte Carlo simulation for derivative pricing

Abstract: This paper provides a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values in Monte Carlo simulation for derivative pricing which are needed to achieve desired precisions. This paper also gives the competitive ratios of the two algorithms and proves the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.