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The document is a preface to the Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2022 (ICNAAM-2022). The conference aims to bring together leading scientists in the field of Numerical and Applied Mathematics and attract high-quality research papers. The selection of papers included in the volume was based on an international peer review process. The preface expresses gratitude to the Scientific Committee, organizers, invited speakers, and others involved in the conference. It also mentions the inclusion of the Proceedings in the AIP Conference Proceedings series. The document provides information about the conference details, the European Society of Computational Methods in Sciences and Engineering (ESCMSE), and the categories of membership in the society. [Extracted from the article]

Vandermonde matrices are fundamental in applied mathematics, natural sciences, and engineering. The inverse form of the Vandermonde matrix (V) is one of its most important characteristics. The purpose of this paper is to develop an algorithm for the inverse of Vandermonde matrix, V-1 in the Python programming language using the Synthetic Division method. To compute the elements of V-1, the Synthetic Division uses arithmetic operations, multiplications, and additions. Consequently, inverse matrix computations require less time compared to a method that involved the multiplication of two matrices. In addition, the efficiency and time required to compute V-1 using Synthetic Division versus the numpy.linalg.inv() function in the Python NumPy module are compared. The study found that the NumPy inverse function demonstrated that the time computation of inverse matrices is consistent, except for certain sizes. In contrast, the Synthetic Division algorithm appears consistent regardless of matrix size. Therefore, Synthetic Division is an effective and efficient method for computing V-1. [ABSTRACT FROM AUTHOR]

Linear programming is one of the basic concepts to further study in applied mathematics and optimization. If the constraints of the linear programming problem form a convex region, then the problem must have an optimal solution. Cube is convex and, in terms of geometry, cube is very special. Cube has some special properties that all edges are congruent and also the all sides are congruent. Other special properties of the cube are related to orthogonality and parallelism. This paper discus linear programming problems with cube constraints. This research is study literature research to describe linear programming problems with cube constraints on geometrical angle. Considering the peculiarities of a cube, this linear programming problem must have an optimal solution. The results show the following points: 1) A cube is a convex polyhedron; 2) The steps for solving linear programming with cube constraints are analogous to the steps for solving linear programming in two dimensions using the graphical method, finding all the vertices of the cube and calculating the value of the objective function at all of the vertices, and then determining the vertex point that produces the optimal value; 3) The problem can have a unique solution (vertex) or have infinitely many solutions (the points along the edges or on the side planes of the cube). [ABSTRACT FROM AUTHOR]

This document is a preface to the Proceedings of the International Conference on Frontiers in Industrial and Applied Mathematics (FIAM) 2022. The conference was organized and hosted by the Department of Mathematics at the Central University of Haryana, India. The objective of the conference was to present and discuss new research findings in the field of industrial and applied mathematics. The conference included 18 technical sessions and 30 invited talks, with 95 selected papers presented by researchers from India and foreign countries. The organizers express gratitude to the sponsors and participants for their contributions to the success of the event. [Extracted from the article]