Structure and operation algorithm of the data collection module of the Super c-τ factory electromagnetic calorimeter

The study is devoted to the development of a data collection module of the electromagnetic calorimeter of the Super c-τ factory collider. The data collection module is one of the main parts of the data acquisition system of the electromagnetic calorimeter. It is designed for analog and digital signal processing of scintillation counters, calculating its main characteristics (amplitude, time of occurrence and quality of fitting), and participates in the launch of the detector data acquisition system. A prototype of the module on which the module will be debugged and the algorithm of the device will be checked is being developed. The algorithm of the module includes the calculation of signal characteristics and the formation of packets for data transmission. Signal characteristics are calculated by approximating a function defined either by the least squares method or by the method of χ2 function minimization. In the course of a mathematical experiment, it was found that the method of χ2 function minimization gave more accurate calculation values than the least squares method. However, it requires an experiment with scintillation counters to determine the necessary coefficients. Therefore, the coefficients of the approximating curve are determined by the least squares method in the proto-type and the method of χ2 function minimization is used in the module. Based on the results obtained, an algorithm of the module operation was compiled, which was then implemented in the field-programmable gate array Altera Cyclone 10GX.

Geometric Rescaling Algorithms for Submodular Function Minimization

We present a new class of polynomial-time algorithms for submodular function minimization (SFM) as well as a unified framework to obtain strongly polynomial SFM algorithms. Our algorithms are based on simple iterative methods for the minimum-norm problem, such as the conditional gradient and Fujishige–Wolfe algorithms. We exhibit two techniques to turn simple iterative methods into polynomial-time algorithms. First, we adapt the geometric rescaling technique, which has recently gained attention in linear programming, to SFM and obtain a weakly polynomial bound [Formula: see text]. Second, we exhibit a general combinatorial black box approach to turn [Formula: see text]-approximate SFM oracles into strongly polynomial exact SFM algorithms. This framework can be applied to a wide range of combinatorial and continuous algorithms, including pseudo-polynomial ones. In particular, we can obtain strongly polynomial algorithms by a repeated application of the conditional gradient or of the Fujishige–Wolfe algorithm. Combined with the geometric rescaling technique, the black box approach provides an [Formula: see text] algorithm. Finally, we show that one of the techniques we develop in the paper can also be combined with the cutting-plane method of Lee et al., yielding a simplified variant of their [Formula: see text] algorithm.

Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization

Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.

Developing an algorithm to minimize boolean functions for the visual-matrix form of the analytical method

This research has established the possibility of improving the effectiveness of the visual-matrix form of the analytical Boolean function minimization method by identifying reserves in a more complex algorithm for the operations of logical absorption and super-gluing the variables in terms of logical functions. An improvement in the efficiency of the Boolean function minimization procedure was also established, due to selecting, according to the predefined criteria, the optimal stack of logical operations for the first and second binary matrices of Boolean functions. When combining a sequence of logical operations using different techniques for gluing variables such as simple gluing and super-gluing, there are a small number of cases when function minimization is more effective if an operation of simply gluing the variables is first applied to the first matrix. Thus, a short analysis is required for the primary application of operations in the first binary matrix. That ensures the proper minimization efficiency regarding the earlier unaccounted-for variants for simplifying the Boolean functions by the visual-matrix form of the analytical method. For a series of cases, the choice of the optimal stack is also necessary for the second binary matrix. The experimental study has confirmed that the visual-matrix form of the analytical method, whose special feature is the use of 2-(n, b)-design and 2-(n, x/b)-design systems in the first matrix, improves the process efficiency, as well as the reliability of the result of Boolean function minimization. This simplifies the procedure of searching for a minimal function. Compared to analogs, that makes it possible to improve the productivity of the Boolean function minimization process by 100‒200 %. There is reason to assert the possibility of improving the efficiency of the Boolean function minimization process by the visual-matrix form of the analytical method, through the use of more complex logical operations of absorbing and super-gluing the variables. Also, by optimally combining the sequence of logical operations of super-gluing the variables and simply gluing the variables, based on the selection, according to the established criteria, of the stack of logical operations in the first binary matrix of the assigned function