arXiv subjects

Ming-Yang Kao

Publications and source records attributed to Ming-Yang Kao.

At least 19 recordsLinked to original sources

arXiv2014

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

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.

cs.DM↗

arXiv2014

Computing Minimum Tile Sets to Self-Assemble Colors Patterns

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.

cs.CC↗

arXiv2012

Optimizing Tile Concentrations to Minimize Errors and Time for DNA Tile Self-Assembly Systems

DNA tile self-assembly has emerged as a rich and promising primitive for nano-technology. This paper studies the problems of minimizing assembly time and error rate by changing the tile concentrations because changing the tile concentrations is easy to implement in actual lab experiments. We prove that setting the concentration of tile $T_i$ proportional to the square root of $N_i$ where $N_i$ is the number of times $T_i$ appears outside the seed structure in the final assembled shape minimizes the rate of growth errors for rectilinear tile systems. We also show that the same concentrations minimize the expected assembly time for a feasible class of tile systems. Moreover, for general tile systems, given tile concentrations, we can approximate the expected assembly time with high accuracy and probability by running only a polynomial number of simulations in the size of the target shape.

cs.DS↗

arXiv2012

Deterministic Polynomial-Time Algorithms for Designing Short DNA Words

Designing short DNA words is a problem of constructing a set (i.e., code) of n DNA strings (i.e., words) with the minimum length such that the Hamming distance between each pair of words is at least k and the n words satisfy a set of additional constraints. This problem has applications in, e.g., DNA self-assembly and DNA arrays. Previous works include those that extended results from coding theory to obtain bounds on code and word sizes for biologically motivated constraints and those that applied heuristic local searches, genetic algorithms, and randomized algorithms. In particular, Kao, Sanghi, and Schweller (2009) developed polynomial-time randomized algorithms to construct n DNA words of length within a multiplicative constant of the smallest possible word length (e.g., 9 max{log n, k}) that satisfy various sets of constraints with high probability. In this paper, we give deterministic polynomial-time algorithms to construct DNA words based on derandomization techniques. Our algorithms can construct n DNA words of shorter length (e.g., 2.1 log n + 6.28 k) and satisfy the same sets of constraints as the words constructed by the algorithms of Kao et al. Furthermore, we extend these new algorithms to construct words that satisfy a larger set of constraints for which the algorithms of Kao et al. do not work.

cs.DS↗

arXiv2011

On Approximating Four Covering and Packing Problems

In this paper, we consider approximability issues of the following four problems: triangle packing, full sibling reconstruction, maximum profit coverage and 2-coverage. All of them are generalized or specialized versions of set-cover and have applications in biology ranging from full-sibling reconstructions in wild populations to biomolecular clusterings; however, as this paper shows, their approximability properties differ considerably. Our inapproximability constant for the triangle packing problem improves upon the previous results; this is done by directly transforming the inapproximability gap of Haastad for the problem of maximizing the number of satisfied equations for a set of equations over GF(2) and is interesting in its own right. Our approximability results on the full siblings reconstruction problems answers questions originally posed by Berger-Wolf et al. and our results on the maximum profit coverage problem provides almost matching upper and lower bounds on the approximation ratio, answering a question posed by Hassin and Or.

cs.CC↗

arXiv2006

Reducing Tile Complexity for Self-Assembly Through Temperature Programming

We consider the tile self-assembly model and how tile complexity can be eliminated by permitting the temperature of the self-assembly system to be adjusted throughout the assembly process. To do this, we propose novel techniques for designing tile sets that permit an arbitrary length $m$ binary number to be encoded into a sequence of $O(m)$ temperature changes such that the tile set uniquely assembles a supertile that precisely encodes the corresponding binary number. As an application, we show how this provides a general tile set of size O(1) that is capable of uniquely assembling essentially any $n\times n$ square, where the assembled square is determined by a temperature sequence of length $O(\log n)$ that encodes a binary description of $n$. This yields an important decrease in tile complexity from the required $Ω(\frac{\log n}{\log\log n})$ for almost all $n$ when the temperature of the system is fixed. We further show that for almost all $n$, no tile system can simultaneously achieve both $o(\log n)$ temperature complexity and $o(\frac{\log n}{\log\log n})$ tile complexity, showing that both versions of an optimal square building scheme have been discovered. This work suggests that temperature change can constitute a natural, dynamic method for providing input to self-assembly systems that is potentially superior to the current technique of designing large tile sets with specific inputs hardwired into the tileset.

cs.CC↗

arXiv2006

Randomized Fast Design of Short DNA Words

We consider the problem of efficiently designing sets (codes) of equal-length DNA strings (words) that satisfy certain combinatorial constraints. This problem has numerous motivations including DNA computing and DNA self-assembly. Previous work has extended results from coding theory to obtain bounds on code size for new biologically motivated constraints and has applied heuristic local search and genetic algorithm techniques for code design. This paper proposes a natural optimization formulation of the DNA code design problem in which the goal is to design n strings that satisfy a given set of constraints while minimizing the length of the strings. For multiple sets of constraints, we provide high-probability algorithms that run in time polynomial in n and any given constraint parameters, and output strings of length within a constant factor of the optimal. To the best of our knowledge, this work is the first to consider this type of optimization problem in the context of DNA code design.

cs.DS↗

arXiv2002

Improved Phylogeny Comparisons: Non-Shared Edges Nearest Neighbor Interchanges, and Subtree Transfers

The number of the non-shared edges of two phylogenies is a basic measure of the dissimilarity between the phylogenies. The non-shared edges are also the building block for approximating a more sophisticated metric called the nearest neighbor interchange (NNI) distance. In this paper, we give the first subquadratic-time algorithm for finding the non-shared edges, which are then used to speed up the existing approximating algorithm for the NNI distance from $O(n^2)$ time to $O(n \log n)$ time. Another popular distance metric for phylogenies is the subtree transfer (STT) distance. Previous work on computing the STT distance considered degree-3 trees only. We give an approximation algorithm for the STT distance for degree-$d$ trees with arbitrary $d$ and with generalized STT operations.

cs.DS↗

arXiv2002

Linear-Time Algorithms for Computing Maximum-Density Sequence Segments with Bioinformatics Applications

We study an abstract optimization problem arising from biomolecular sequence analysis. For a sequence A of pairs (a_i,w_i) for i = 1,..,n and w_i>0, a segment A(i,j) is a consecutive subsequence of A starting with index i and ending with index j. The width of A(i,j) is w(i,j) = sum_{i <= k <= j} w_k, and the density is (sum_{i<= k <= j} a_k)/ w(i,j). The maximum-density segment problem takes A and two values L and U as input and asks for a segment of A with the largest possible density among those of width at least L and at most U. When U is unbounded, we provide a relatively simple, O(n)-time algorithm, improving upon the O(n \log L)-time algorithm by Lin, Jiang and Chao. When both L and U are specified, there are no previous nontrivial results. We solve the problem in O(n) time if w_i=1 for all i, and more generally in O(n+n\log(U-L+1)) time when w_i>=1 for all i.

cs.DS↗

arXiv2002

Fast Universalization of Investment Strategies with Provably Good Relative Returns

A universalization of a parameterized investment strategy is an online algorithm whose average daily performance approaches that of the strategy operating with the optimal parameters determined offline in hindsight. We present a general framework for universalizing investment strategies and discuss conditions under which investment strategies are universalizable. We present examples of common investment strategies that fit into our framework. The examples include both trading strategies that decide positions in individual stocks, and portfolio strategies that allocate wealth among multiple stocks. This work extends Cover's universal portfolio work. We also discuss the runtime efficiency of universalization algorithms. While a straightforward implementation of our algorithms runs in time exponential in the number of parameters, we show that the efficient universal portfolio computation technique of Kalai and Vempala involving the sampling of log-concave functions can be generalized to other classes of investment strategies.

cs.CE↗

arXiv2001

DNA Self-Assembly For Constructing 3D Boxes

We propose a mathematical model of DNA self-assembly using 2D tiles to form 3D nanostructures. This is the first work to combine studies in self-assembly and nanotechnology in 3D, just as Rothemund and Winfree did in the 2D case. Our model is a more precise superset of their Tile Assembly Model that facilitates building scalable 3D molecules. Under our model, we present algorithms to build a hollow cube, which is intuitively one of the simplest 3D structures to construct. We also introduce five basic measures of complexity to analyze these algorithms. Our model and algorithmic techniques are applicable to more complex 2D and 3D nanostructures.

cs.CC↗

arXiv2001

Predicting RNA Secondary Structures with Arbitrary Pseudoknots by Maximizing the Number of Stacking Pairs

The paper investigates the computational problem of predicting RNA secondary structures. The general belief is that allowing pseudoknots makes the problem hard. Existing polynomial-time algorithms are heuristic algorithms with no performance guarantee and can only handle limited types of pseudoknots. In this paper we initiate the study of predicting RNA secondary structures with a maximum number of stacking pairs while allowing arbitrary pseudoknots. We obtain two approximation algorithms with worst-case approximation ratios of 1/2 and 1/3 for planar and general secondary structures,respectively. For an RNA sequence of $n$ bases, the approximation algorithm for planar secondary structures runs in $O(n^3)$ time while that for the general case runs in linear time. Furthermore, we prove that allowing pseudoknots makes it NP-hard to maximize the number of stacking pairs in a planar secondary structure. This result is in contrast with the recent NP-hard results on psuedoknots which are based on optimizing some general and complicated energy functions.

cs.CE↗

arXiv2001

The Risk Profile Problem for Stock Portfolio Optimization

This work initiates research into the problem of determining an optimal investment strategy for investors with different attitudes towards the trade-offs of risk and profit. The probability distribution of the return values of the stocks that are considered by the investor are assumed to be known, while the joint distribution is unknown. The problem is to find the best investment strategy in order to minimize the probability of losing a certain percentage of the invested capital based on different attitudes of the investors towards future outcomes of the stock market. For portfolios made up of two stocks, this work shows how to exactly and quickly solve the problem of finding an optimal portfolio for aggressive or risk-averse investors, using an algorithm based on a fast greedy solution to a maximum flow problem. However, an investor looking for an average-case guarantee (so is neither aggressive or risk-averse) must deal with a more difficult problem. In particular, it is #P-complete to compute the distribution function associated with the average-case bound. On the positive side, approximate answers can be computed by using random sampling techniques similar to those for high-dimensional volume estimation. When k>2 stocks are considered, it is proved that a simple solution based on the same flow concepts as the 2-stock algorithm would imply that P = NP, so is highly unlikely. This work gives approximation algorithms for this case as well as exact algorithms for some important special cases.

cs.CE↗

arXiv2001

Common-Face Embeddings of Planar Graphs

Given a planar graph G and a sequence C_1,...,C_q, where each C_i is a family of vertex subsets of G, we wish to find a plane embedding of G, if any exists, such that for each i in {1,...,q}, there is a face F_i in the embedding whose boundary contains at least one vertex from each set in C_i. This problem has applications to the recovery of topological information from geographical data and the design of constrained layouts in VLSI. Let I be the input size, i.e., the total number of vertices and edges in G and the families C_i, counting multiplicity. We show that this problem is NP-complete in general. We also show that it is solvable in O(I log I) time for the special case where for each input family C_i, each set in C_i induces a connected subgraph of the input graph G. Note that the classical problem of simply finding a planar embedding is a further special case of this case with q=0. Therefore, the processing of the additional constraints C_1,...,C_q only incurs a logarithmic factor of overhead.

cs.DS↗

arXiv2001

Optimal Bid Sequences for Multiple-Object Auctions with Unequal Budgets

In a multiple-object auction, every bidder tries to win as many objects as possible with a bidding algorithm. This paper studies position-randomized auctions, which form a special class of multiple-object auctions where a bidding algorithm consists of an initial bid sequence and an algorithm for randomly permuting the sequence. We are especially concerned with situations where some bidders know the bidding algorithms of others. For the case of only two bidders, we give an optimal bidding algorithm for the disadvantaged bidder. Our result generalizes previous work by allowing the bidders to have unequal budgets. One might naturally anticipate that the optimal expected numbers of objects won by the bidders would be proportional to their budgets. Surprisingly, this is not true. Our new algorithm runs in optimal O(n) time in a straightforward manner. The case with more than two bidders is open.

cs.CE↗

arXiv2001

Optimal Augmentation for Bipartite Componentwise Biconnectivity in Linear Time

A graph is componentwise biconnected if every connected component either is an isolated vertex or is biconnected. We present a linear-time algorithm for the problem of adding the smallest number of edges to make a bipartite graph componentwise biconnected while preserving its bipartiteness. This algorithm has immediate applications for protecting sensitive information in statistical tables.

cs.DS↗

arXiv2001

The Enhanced Double Digest Problem for DNA Physical Mapping

The double digest problem is a common NP-hard approach to constructing physical maps of DNA sequences. This paper presents a new approach called the enhanced double digest problem. Although this new problem is also NP-hard, it can be solved in linear time in certain theoretically interesting cases.

cs.CE↗

arXiv2001

Compact Encodings of Planar Graphs via Canonical Orderings and Multiple Parentheses

Let G be a plane graph of n nodes, m edges, f faces, and no self-loop. G need not be connected or simple (i.e., free of multiple edges). We give three sets of coding schemes for G which all take O(m+n) time for encoding and decoding. Our schemes employ new properties of canonical orderings for planar graphs and new techniques of processing strings of multiple types of parentheses. For applications that need to determine in O(1) time the adjacency of two nodes and the degree of a node, we use 2m+(5+1/k)n + o(m+n) bits for any constant k > 0 while the best previous bound by Munro and Raman is 2m+8n + o(m+n). If G is triconnected or triangulated, our bit count decreases to 2m+3n + o(m+n) or 2m+2n + o(m+n), respectively. If G is simple, our bit count is (5/3)m+(5+1/k)n + o(n) for any constant k > 0. Thus, if a simple G is also triconnected or triangulated, then 2m+2n + o(n) or 2m+n + o(n) bits suffice, respectively. If only adjacency queries are supported, the bit counts for a general G and a simple G become 2m+(14/3)n + o(m+n) and (4/3)m+5n + o(n), respectively. If we only need to reconstruct G from its code, a simple and triconnected G uses roughly 2.38m + O(1) bits while the best previous bound by He, Kao, and Lu is 2.84m.

cs.DS↗