Approximating integer programs with positive right-hand sides

We study minimisation of integer linear programs with positive right-hand sides. We show that such programs can be approximated within the maximum absolute row sum of the constraint matrix A whenever the variables are allowed to take values in N. This result is optimal under the unique games conjecture. When the variables are restricted to bounded domains, we show that finding a feasible solution is NP-hard in almost all cases.     

A survey of partial-observation stochastic parity games

We consider two-player zero-sum stochastic games on graphs with ω-regular winning conditions specified as parity objectives. These games have applications in the design and control of reactive systems. We survey the complexity results for the problem of deciding the winner in such games, and in classes of interest obtained as special cases, based on the information and the power of randomization available to the players, on the class of objectives and on the winning mode. On the basis of information, these games can be classified as follows: (a) partial-observation (both players have partial view of the game); (b) one-sided partial-observation (one player has partial-observation and the other player has complete-observation); and (c) complete-observation (both players have complete view of the game). The one-sided partial-observation games have two important subclasses: the one-player games, known as partial-observation Markov decision processes (POMDPs), and the blind one-player games, known as probabilistic automata. On the basis of randomization, (a) the players may not be allowed to use randomization (pure strategies), or (b) they may choose a probability distribution over actions but the actual random choice is external and not visible to the player (actions invisible), or (c) they may use full randomization. Finally, various classes of games are obtained by restricting the parity objective to a reachability, safety, Büchi, or coBüchi condition. We also consider several winning modes, such as sure-winning (i.e., all outcomes of a strategy have to satisfy the winning condition), almost-sure winning (i.e., winning with probability 1), limit-sure winning (i.e., winning with probability arbitrarily close to 1), and value-threshold winning (i.e., winning with probability at least ν, where ν is a given rational).     

The complexity of constraint satisfaction problems for small relation algebras

Andréka and Maddux [Notre Dame J. Formal Logic 35 (4) 1994] classified the small relation algebras—those with at most 8 elements, or in other terms, at most 3 atomic relations. They showed that there are eighteen isomorphism types of small relation algebras, all representable. For each simple, small relation algebra they computed the spectrum of the algebra, namely the set of cardinalities of square representations of that relation algebra.In this paper we analyze the computational complexity of the problem of deciding the satisfiability of a finite set of constraints built on any small relation algebra. We give a complete classification of the complexities of the general constraint satisfaction problem for small relation algebras. For three of the small relation algebras the constraint satisfaction problem is NP-complete, for the other fifteen small relation algebras the constraint satisfaction problem has cubic (or lower) complexity.We also classify the complexity of the constraint satisfaction problem over fixed finite representations of any relation algebra. If the representation has size two or less then the complexity is cubic (or lower), but if the representation is square, finite and bigger than two then the complexity is NP-complete.     

Concurrent reachability games

We consider concurrent two-player games with reachability objectives. In such games, at each round, player 1 and player 2 independently and simultaneously choose moves, and the two choices determine the next state of the game. The objective of player 1 is to reach a set of target states; the objective of player 2 is to prevent this. These are zero-sum games, and the reachability objective is one of the most basic objectives: determining the set of states from which player 1 can win the game is a fundamental problem in control theory and system verification. There are three types of winning states, according to the degree of certainty with which player 1 can reach the target. From type-1 states, player 1 has a deterministic strategy to always reach the target. From type-2 states, player 1 has a randomized strategy to reach the target with probability 1. From type-3 states, player 1 has for every real ε>0$\epsilon >0$ a randomized strategy to reach the target with probability greater than 1−ε$1-\epsilon$. We show that for finite state spaces, all three sets of winning states can be computed in polynomial time: type-1 states in linear time, and type-2 and type-3 states in quadratic time. The algorithms to compute the three sets of winning states also enable the construction of the winning and spoiling strategies.     

The values of college students in business simulation game: A means-end chain approach

Business simulation games (BSGs) enable students to practice making decisions in a virtual environment, accumulate experience in application of strategies, and train themselves in modes of decision-making. This study examines the value sought by players of BSG. In this study, a means-end chain (MEC) model was adopted as the basis, and ladder method soft laddering was used to conduct in-depth interviews with students who had experience in using BSGs. The chain concept of “attribute-consequence-value” was used to understand students’ value cognition structures. Content analysis was used to analyze the attributes-consequences-values for BSGs players, then converted into a Hierarchical Value Map (HVM). The results showed that students consider teamwork and market diversity as the most important attributes, and the consequences of a cooperative approach and market diversity are emotional exchange and multi-thinking, with the ultimate value brought to users by exchanges between teams and constant thinking being interpersonal relationships and a sense of accomplishment.     

Concise: Compressed ‘n’ Composable Integer Set

Bit arrays, or bitmaps, are used to significantly speed up set operations in several areas, such as data warehousing, information retrieval, and data mining, to cite a few. However, bitmaps usually use a large storage space, thus requiring compression. Consequently, there is a space-time tradeoff among compression schemes. The Word Aligned Hybrid (WAH) bitmap compression trades some space to allow for bitwise operations without first decompressing bitmaps. WAH has been recognized as the most efficient scheme in terms of computation time. In this paper we present Concise (Compressed ‘n’ Composable Integer Set), a new scheme that enjoys significantly better performances than WAH. In particular, when compared to WAH our algorithm is able to reduce the required memory up to 50%, while having comparable computation time. Further, we show that Concise can be efficiently used to represent sets of integral numbers in lieu of well-known data structures such as arrays, lists, hashtables, and self-balancing binary search trees. Extensive experiments over synthetic data show the effectiveness of our proposal.     

The complexity of stochastic games

We consider the complexity of stochastic games—simple games of chance played by two players. We show that the problem of deciding which player has the greatest chance of winning the game is in the class NP co-NP.     

Note on winning positions on pushdown games with ω-regular conditions

We consider infinite two-player games on pushdown graphs. For parity winning conditions, we show that the set of winning positions of each player is regular and we give an effective construction of an alternating automaton recognizing it. This provides a DEXPTIME procedure to decide whether a position is winning for a given player. Finally, using the same methods, we show, for any ω-regular winning condition, that the set of winning positions for a given player is regular and effective.     

Synchronizing automata with finitely many minimal synchronizing words

A synchronizing word for a given synchronizing DFA is called minimal if none of its proper factors is synchronizing. We characterize the class of synchronizing automata having only finitely many minimal synchronizing words (the class of such automata is denoted by FG). Using this characterization we prove that any such automaton possesses a synchronizing word of length at most 3n-5. We also prove that checking whether a given DFA A is in FG is co-NP-hard and provide an algorithm for this problem which is exponential in the number of states A.     

Flipping the winner of a poset game

Partially-ordered set games, also called poset games, are a class of two-player combinatorial games. The playing field consists of a set of elements, some of which are greater than other elements. Two players take turns removing an element and all elements greater than it, and whoever takes the last element wins. Examples of poset games include Nim and Chomp. We investigate the complexity of computing which player of a poset game has a winning strategy. We give an inductive procedure that modifies poset games to change the nim-value which informally captures the winning strategies in the game. For a generic poset game G, we describe an efficient method for constructing a game ¬G such that the first player has a winning strategy if and only if the second player has a winning strategy on G. This solves the long-standing problem of whether this construction can be done efficiently. This construction also allows us to reduce the class of Boolean formulas to poset games, establishing a lower bound on the complexity of poset games.     

The complexity of achievement and maintenance problems in agent-based systems

We completely classify the computational complexity of the basic achievement and maintenance agent design problems in bounded environments when these problems are parameterized by the number of environment states and the number of agent actions. The different problems are P-complete, NP-complete, co-NP-complete or PSPACE-complete (when they are not trivial). We also consider alternative achievement and maintenance agent design problems by allowing longer runs in environments (that is, our environments are bounded but the bounds are more liberal than was the case previously). Again, we obtain a complete classification but so that the different problems are DEXPTIME-complete, NEXPTIME-complete, co-NEXPTIME-complete or NEXPSPACE-complete (when they are not trivial).     

Performance evaluation for a transportation system in stochastic case

In a single-commodity multistate flow network, the arc capacity is stochastic and thus the system capacity (i.e. the maximum flow from the source to the sink) is not a fixed number. This paper constructs a multicommodity multistate flow network with weighted capacity allocation to model a transportation system. Each arc with cost attribute has several possible capacities. The capacity weight, the consumed amount of arc capacity by per commodity, varies with the arcs and types of commodity. We define the system capacity as a demand vector d if the system fulfills at most d. The addressed problem in this work is to measure the service quality of a transportation system. We propose a performance index, the probability that the upper bound of the system capacity equals a demand vector d subject to the budget constraint. A simple algorithm based on minimal cuts is presented to generate all (d,B)-MC that are the maximal capacity vectors meeting exactly the demand d under the budget B. The proposed performance index can be subsequently evaluated in terms of such (d,B)-MC.     

Determinization and Memoryless Winning Strategies

We show that Safra's determinization ofω-automata with Streett (strong fairness) acceptance condition also gives memoryless winning strategies in infinite games, for the player whose acceptance condition is the complement of the Streett condition. Both determinization and memorylessness are essential parts of known proofs of Rabin's tree automata complementation lemma. Also, from Safra's determinization construction, along with its memoryless winning strategy extension, a single exponential complementation of Streett tree automata follows. A different single exponential construction and proof first appeared in [N. Klarlund (1992), Progress measures, immediate determinacy, and a subset construction for tree automata,in“Proceedings, 7th IEEE Symposium on Logics in Computer Science”].     

A differential evolution algorithm with self-adapting strategy and control parameters

This paper presents a Differential Evolution algorithm with self-adaptive trial vector generation strategy and control parameters (SspDE) for global numerical optimization over continuous space. In the SspDE algorithm, each target individual has an associated strategy list (SL), a mutation scaling factor F list (FL), and a crossover rate CR list (CRL). During the evolution, a trial individual is generated by using a strategy, F, and CR taken from the lists associated with the target vector. If the obtained trial individual is better than the target vector, the used strategy, F, and CR will enter a winning strategy list (wSL), a winning F list (wFL), and a winning CR list (wCRL), respectively. After a given number of iterations, the FL, CRL or SL will be refilled at a high probability by selecting elements from wFL, wCRL and wSL or randomly generated values. In this way, both the trial vector generation strategy and its associated parameters can be gradually self-adapted to match different phases of evolution by learning from their previous successful experience. Extensive computational simulations and comparisons are carried out by employing a set of 19 benchmark problems from the literature. The computational results show that overall the SspDE algorithm performs better than the state-of-the-art differential evolution variants.     

Connected sum of digital closed surfaces

During the past 20 years the research of digital surfaces has proceeded to find their properties in the digital space Zⁿ, such as a topological number, a simple k-point, the 3D-Jordan theorem, a k-separating set, a boundary detecting algorithm and so on. Actually, unlike surfaces in a continuous space, the features of digital surfaces have different characteristics. The aim of this paper is to introduce the notion of a digital closed k-surface in Zⁿ, n ? 3, with the general k-adjacency relations as a generalization of Malgouyres’ and Morgenthaler’s k-surfaces in Z³, to establish some minimal simple closed k-surfaces in Z³ and to find their digital topological properties in relation with the k-fundamental group and k-contractibility. Moreover, a connected sum of two digital closed surfaces is introduced and its digital topological properties are investigated.     

Hard constraint satisfaction problems have hard gaps at location 1

An instance of the maximum constraint satisfaction problem (Max CSP) is a finite collection of constraints on a set of variables, and the goal is to assign values to the variables that maximises the number of satisfied constraints. Max CSP captures many well-known problems (such as Maxk-SAT and Max Cut) and is consequently NP-hard. Thus, it is natural to study how restrictions on the allowed constraint types (or constraint language) affect the complexity and approximability of Max CSP. The PCP theorem is equivalent to the existence of a constraint language for which Max CSP has a hard gap at location 1; i.e. it is NP-hard to distinguish between satisfiable instances and instances where at most some constant fraction of the constraints are satisfiable. All constraint languages, for which the CSP problem (i.e., the problem of deciding whether all constraints can be satisfied) is currently known to be NP-hard, have a certain algebraic property. We prove that any constraint language with this algebraic property makes Max CSP have a hard gap at location 1 which, in particular, implies that such problems cannot have a PTAS unless P=NP. We then apply this result to Max CSP restricted to a single constraint type; this class of problems contains, for instance, Max Cut and Max DiCut. Assuming P≠NP, we show that such problems do not admit PTAS except in some trivial cases. Our results hold even if the number of occurrences of each variable is bounded by a constant. Finally, we give some applications of our results.     

Four-arc approximation to ellipses: The best in general

So far, the four-arc approximations to an ellipse E are made under the condition that the major and minor axes keep strictly unchanged. In general, however, this condition is unnecessary. Then the fitting can be further improved. Considering a representative quadrant of E, we first draw two auxiliary circular arcs tangent to E at the axes but having a gap ε at their boundary, such that the small arc S lies outside the large arc L. Meanwhile the extreme errors of S and L w.r.t. E are ε and −ε respectively. Giving the radii of S and L a decrement −ε/2 and an increment ε/2 brings them to join smoothly. Thus, reducing the overall error to minimum, an analytic solution in implicit form is derived.     

Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories

New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (k_nn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar ‘Single Index Model’ transformations of X. Variance functions generated estimates of the variance of Y. Three case studies, with data from the Forest Inventory and Analysis program of the U.S. Forest Service, the Finnish National Forest Inventory, and Landsat ETM+ ancillary data, demonstrate applications of the proposed estimators. Nearly unbiased k_nn predictions of three forest attributes were obtained. Estimates of mean square error indicate that k_nn is an attractive technique for integrating remotely-sensed and ground data for the provision of forest attribute maps and areal predictions.     

Hybrid dynamics of stochastic programs

We provide Stochastic Concurrent Constraint Programming (sCCP), a stochastic process algebra based on CCP, with a semantics in terms of hybrid automata. We associate with each sCCP program both a stochastic and a non-deterministic hybrid automaton. Then, we compare such automata with the standard stochastic semantics (given by a Continuous Time Markov Chain) and the one based on ordinary differential equations, obtained by a fluid-flow approximation technique. We discuss in detail two case studies: Repressilator and the Circadian Clock, with particular regard to the robustness exhibited by the different semantic models and to the effect of discreteness in dynamical evolution of such systems.