Heuristic Techniques for Solving Large Combinatorial Problems on a Computer

The technique of real-time simulation for studying air traffic control problems has been employed for many years. This technique has enabled investigators to simulate live air traffic situations for the purpose of evaluating new concepts and their relationship to the air traffic controller. The modern high-speed digital computer has increased the scope of these real-time simulation studies to include large scale air traffic control systems employing such computers in the control process.

Computer simulation and analysis techniques for reliable circuit design

A confidence interval is an interval estimate of a parameter of a population calculated from a sample drawn from the population. Bootstrapping method, which involves producing several new data sets that are resampled from the original data in order to estimate parameter for each newly created data set, allowing an empirical distribution for the parameter to be estimated. Since certain statistics are harder to estimate, confidence intervals are rarely employed. Several statistics might necessitate multi-step formulas assuming that are impractical for calculating confidence intervals. This paper reviews research on the concept of bootstrapping and bootstrap confidence interval. The current narrative analysis was developed to answer the main research question: (1) What is the concept of the bootstrap method and bootstrap confidence interval? (2) What are the methods of bootstrapping to obtain confidence interval? This study has found general bootstrap method idea, various techniques of bootstrap methods, its advantages and disadvantages, and its limitations. There are normal interval method, percentile bootstrap method, basic method, first-order normal approximation method, bias-corrected bootstrap, accelerated bias-corrected bootstrap and bootstrap-t method. This study concludes that the advantages of using bootstrap CI is that it does not require any assumptions about the shape of distribution and universality of the approach. Bootstrapping is a computer-intensive statistical technique that relies significantly on modern high-speed digital computers to do massive computations.

The problem of space-current calculation is reviewed briefly to point out the inadequacy of the classic solution for modern close-spaced electron tubes. A more accurate solution for the space current is obtained by calculating the current distribution from the potential field in the grid-cathode space, corrected for space charge and initial electron velocities. A machine program set up on a large high-speed computer permits the calculation of complete voltage-current characteristics and their derivatives from basic input data. A brief account of the sequence of machine operations illustrates the large number of detailed steps and the flexibility of the program. The accuracy of the calculations is demonstrated by a comparison of the computed and measured characteristics of a nuvistor triode.

There are several public key establishment protocols as well as complete public key cryptosystems based on allegedly hard problems from combinatorial (semi)group theory known by now. Most of these problems are search problems, i.e., they are of the following nature: given a property P and the information that there are objects with the property P, find at least one particular object with the property P. So far, no cryptographic protocol based on a search problem in a non-commutative (semi)group has been recognized as secure enough to be a viable alternative to established protocols (such as RSA) based on commutative (semi)groups, although most of these protocols are more efficient than RSA is. In this paper, we suggest to use decision problems from combinatorial group theory as the core of a public key establishment protocol or a public key cryptosystem. Decision problems are problems of the following nature: given a property P and an object O, find out whether or not the object O has the property P. By using a popular decision problem, the word problem, we design a cryptosystem with the following features: (1) Bob transmits to Alice an encrypted binary sequence which Alice decrypts correctly with probability “very close” to 1; (2) the adversary, Eve, who is granted arbitrarily high (but fixed) computational speed, cannot positively identify (at least, in theory), by using a “brute force attack”, the “1” or “0” bits in Bob’s binary sequence. In other words: no matter what computational speed we grant Eve at the outset, there is no guarantee that her “brute force attack” program will give a conclusive answer (or an answer which is correct with overwhelming probability) about any bit in Bob’s sequence.

Application of Markov decision processes to search problems

This paper presents a novel way to determine new reaction rate parameters (A’s and Ea’s in the Arrhenius expression) in a semi-detailed reaction mechanism for the thermal degradation of aviation fuel and surface fouling. The technique employed is a specialised optimisation procedure, namely a genetic algorithm (GA), which utilises an abstraction of the Darwinian principle of survival of the fittest in order to “breed” good solutions over a predefined number of “generations”. Deposition rates for a given fuel which have been measured experimentally over a range of conditions are reproduced by solving the conservation equations of mass, momentum, energy and species using a CFD code and the optimised set of rate constants obtained using a genetic algorithm inversion process. The new set of rate constants lie within predefined boundaries based upon previous values found in the literature for the mechanism being used. In addition, this powerful technique promises the ability to develop reaction mechanisms whose newly optimised rate constants reproduce closely all the experimental data available, enabling a greater confidence in their predictive capabilities. The process is also shown to be an effective tool to facilitate the elucidation of shortcomings in current global chemistry models of fuel degradation. Therefore, the results of this study demonstrate that the genetic algorithm inversion process may be used to develop and calibrate more detailed models for the thermal stability behaviour of aviation fuels than have been seen previously and, subsequently, accurately predict the locations and rates of deposit build-up in fuel handling systems. Furthermore, it has been demonstrated that, modern high speed computers allow for evaluation of increasingly complex and expensive GA objective functions.

An approach to solving the short-term unit commitment problem (UCP) using a neural-based tabu search (NBTS) is presented. The solution of the unit commitment problem is a complex optimisation problem. The exact solution of the UCP can be obtained by a complete enumeration of all feasible combinations of generating units, which could be a huge number. The unit commitment has commonly been formulated as a nonlinear, large-scale, mixed-integer combinational optimisation problem. The objective is to find the generation scheduling such that the total operating cost can be minimised, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Tabu search is a powerful optimisation procedure that has been successfully applied to a number of combinatorial optimisation problems. It has the ability to avoid entrapment in local minima by employing a flexible memory system. The neural network combines good solution quality for tabu search with rapid convergence for an artificial neural network. The neural based tabu search method is used to find the unit commitment. By doing so, it gives the optimum solution rapidly and efficiently. The Neyveli Thermal Power Station (NTPS) Unit – II in India has been considered as a case study and extensive studies have also been performed for different power systems consisting of 10, 26, and 34 generating units. The data collected has been used for implementation in the above methods. Numerical results are shown, comparing the cost solutions and computation time obtained by using the intelligent techniques with the conventional methods like dynamic programming and Lagrangian relaxation to reach proper unit commitment.

Recently, there has been much interest in the use of Bayesian statistical methods for performing genetic analyses. Many of the computational difficulties previously associated with Bayesian analysis, such as multidimensional integration, can now be easily overcome using modern high-speed computers and Markov chain Monte Carlo (MCMC) methods. Much of this new technology has been used to perform gene mapping, especially through the use of multi-locus linkage disequilibrium techniques. This review attempts to summarise some of the currently available methods and the software available to implement these methods.

The increased water demand and the increased capital construction costs of water resource development projects force engineers and water planners to utilize efficiently the existing and proposed water resources systems. Hence, it is considered necessary to find the optimum real time operating rules for systems of multiple reservoirs.Stochastic dynamic programming (SDP) has been used as the tool for deriving the real time operating rules for a system of multiple reservoirs, in preference to simulation analysis and linear programming. This is because the operation of the storages is considered as a sequential decision problem and dynamic programming is well suited to handle sequential, multi-period type decision problems. Further, the stochastic nature of streamflow is considered explicitly through a multivariate distribution for streamflow. The method theoretically produces the global optimum solution.The computational difficulties in terms of computer time and memory space have prohibited the realistic application of this method to systems of multiple reservoirs in the past. In this study, two methods have been introduced to reduce the computational difficulties of the method, thus making SDP more attractive to use in the derivation of the real time operating rules for a system of multiple reservoirs.The first method, the assumption of cross correlation of streamflow, reduces the number of states in the SDP method which reduces computer time and memory space. Further, it reduces the number of conditional probability estimates of streamflow which are used in recursive relation of the SDP algorithm. The second method, the “corridor” approach, reduces computer time involved in the SDP method by eliminating the computation of infeasible and/or inferior (suboptimal) solutions. These two methods have resulted in significant savings in the computer effort of the SDP method.The conditional probabilities of streamflow are computed by considering the hypervolume under the conditional probability density function between the integration limits using the computationally efficient Gaussian Legendre quadrature method. The conditional probability density function is derived from the historical streamflow data.Real time operating rules are expressed as a function of the storage volume(s) at the beginning of the current time interval and the forecast inflow into the storage(s) during the current time interval.The Melbourne water supply system is used as the example. The derived operating rules have been tested by operating a simulation model and the conclusion is made that the SDP method is a satisfactory technique for objectively deriving real time operating rules for a system of multiple reservoirs.Finally, a combined stochastic dynamic programming – statistical disaggregation approach is introduced to determine the real time operating rules for a system of multiple reservoirs where operational data are available. Releases from individual storages are a function of the total system storage volume and inflow. The method uses SDP to determine the release rules from the equivalent single reservoir of the multiple reservoir system; these releases are then disaggregated to individual releases using the statistical release disaggregation model whose parameters are derived from the historical operational data. The approach was used to derive real time operating rules for the Melbourne water supply system and the operating rules were validated using a simulation model of the system.

We introduce a formal quantitative notion of “bit security” for a general type of cryptographic games (capturing both decision and search problems), aimed at capturing the intuition that a cryptographic primitive with k-bit security is as hard to break as an ideal cryptographic function requiring a brute force attack on a k-bit key space. Our new definition matches the notion of bit security commonly used by cryptographers and cryptanalysts when studying search (e.g., key recovery) problems, where the use of the traditional definition is well established. However, it produces a quantitatively different metric in the case of decision (indistinguishability) problems, where the use of (a straightforward generalization of) the traditional definition is more problematic and leads to a number of paradoxical situations or mismatches between theoretical/provable security and practical/common sense intuition. Key to our new definition is to consider adversaries that may explicitly declare failure of the attack. We support and justify the new definition by proving a number of technical results, including tight reductions between several standard cryptographic problems, a new hybrid theorem that preserves bit security, and an application to the security analysis of indistinguishability primitives making use of (approximate) floating point numbers. This is the first result showing that (standard precision) 53-bit floating point numbers can be used to achieve 100-bit security in the context of cryptographic primitives with general indistinguishability-based security definitions. Previous results of this type applied only to search problems, or special types of decision problems.

A noticeable fraction of Algorithms papers in the last few decades improve the running time of well-known algorithms for fundamental problems by logarithmic factors. For example, the {O}(n^2) dynamic programming solution to the Longest Common Subsequence problem (LCS) was improved to O(n^2/log^{2}n) in several ways and using a variety of ingenious tricks. This line of research, also known as the art of shaving log factors, lacks a tool for proving negative results. Specifically, how can we show that it is unlikely that LCS can be solved in time O(n^2/log^3n)?\nPerhaps the only approach for such results was suggested in a recent paper of Abboud, Hansen, Vassilevska W. and Williams (STOC'16). The authors blame the hardness of shaving logs on the hardness of solving satisfiability on boolean formulas (Formula-SAT) faster than exhaustive search. They show that an O(n^2/log^{1000} n) algorithm for LCS would imply a major advance in circuit lower bounds. Whether this approach can lead to tighter barriers was unclear.\nIn this paper, we push this approach to its limit and, in particular, prove that a well-known barrier from complexity theory stands in the way for shaving five additional log factors for fundamental combinatorial problems. For LCS, regular expression pattern matching, as well as the Fréchet distance problem from Computational Geometry, we show that an O(n^2/log^{7+epsilon}{n}) runtime would imply new Formula-SAT algorithms.\nOur main result is a reduction from SAT on formulas of size s over n variables to LCS on sequences of length N=2^{n/2} * s^{1+o(1)}. Our reduction is essentially as efficient as possible, and it greatly improves the previously known reduction for LCS with N=2^{n/2} * s^c, for some c >= 100.

The goal of feature selection is to search the optimal feature subset with respect to the evaluation function. Exhaustively searching all possible feature subsets requires high computational cost. The alternative suboptimal methods are more efficient and practical but they cannot promise globally optimal results. We propose a new feature selection algorithm based on distance discriminant and distribution overlapping (HFSDD) for continuous features, which overcomes the drawbacks of the exhaustive search approaches and those of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. Since the distribution of overlapping degrees between every two classes can provide useful information for feature selection, HFSDD also takes them into account by using a new approach to estimate the overlapping degrees. In this sense, HFSDD is a distance discriminant and distribution overlapping based solution. HFSDD was compared with ReliefF and mrmrMID on ten data sets. The experimental results show that HFSDD outperforms the other methods.

Knapsack problem is a combinatorial optimisation problem which is concerned with finding a solution where an exhaustive search is difficult or even impractical to do. Approximate algorithms, also called heuristics are therefore applied in solving Knapsack problems. The goal of this study was to solve the Knapsack model using three heuristics viz: greedy, dynamic programming, and branch-and-bound strategies. The ultimate objective of our study was to evaluate the time complexities of these heuristics as input sizes become larger. In the bid to get that done, a Knapsack problem was solved using the approximate algorithms, employing the same programming environment, with varying input sizes. The problem was simulated using different input data sizes. A synthesis of the results obtained, that is, the various time complexities, was then carried out and a comparative study of the three algorithms was done. The coefficients of the variables used in the Knapsack model for simulation were generated using random numbers generating algorithm. Our results showed that the greedy algorithm was the most efficient computationally. The second best efficient was the dynamic progranmming algorithm while the least in terms of time complexity is branch-and-bound strategy. It is considered a worthwhile effort to study more metrics of complexity measurement. This is recommended as good enough for further research attention. However, our present study has shown in pragmatic form the computational complexity of the greedy, dynamic programming, and branch-and-bound algorithms.

Inspired by the widespread and increasing usage of natural gas, we study the power consumption minimization problem associated with natural gas pipeline transmission in gunbarrel networks with nonidentical compressors. To accurately and flexibly model both gas flow dynamics and compressor working domains, we formulate the problem as a dynamic programming problem. Then we propose an approximate solution approach based on state dimension reduction. We analyze the problem properties and characterize conditions under which optimality is not compromised by the proposed solution approach. Next, we conduct numerical experiments using two data sets based on real networks in China and a data set from the public library GasLib. Numerical results demonstrate that the proposed solution approach significantly reduces computation time without compromising optimality in most cases. Specifically, the proposed solution approach obtains optimal solutions more than a 100 times faster than the exhaustive search when gas pressures are discretized at 0.01 MPa. Further, the optimality gaps do not exceed 0.4%.