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## Solving Linear Programming Problems by Reducing to the Form with an Obvious Answer

The article considers a method for solving a linear programming problem (LPP), which requires finding the minimum or maximum of a linear functional on a set of non-negative solutions of a system of linear algebraic equations with the same unknowns. The method is obtained by improving the classical simplex method, which when involving geometric considerations, in fact, generalizes the Gauss complete exclusion method for solving systems of equations. The proposed method, as well as the method of complete exceptions, proceeds from purely algebraic considerations. It consists of converting the entire LPP, including the objective function, into an equivalent problem with an obvious answer. For the convenience of converting the target functional, the equations are written as linear functionals on the left side and zeros on the right one. From the coefficients of the mentioned functionals, a matrix is formed, which is called the LPP matrix. The zero row of the matrix is the coefficients of the target functional, \$a_{00}\$ is its free member. The algorithms are described and justified in terms of the transformation of this matrix. In calculations the matrix is a calculation table. The method under consideration by analogy with the simplex method consists of three stages. At the first stage the LPP matrix is reduced to a special 1-canonical form. With such matrices one of the basic solutions of the system is obvious, and the target functional on it is \$ a_{00}\$, which is very convenient. At the second stage the resulting matrix is transformed into a similar matrix with non-positive elements of the zero column (except \$a_{00}\$), which entails the non-negativity of the basic solution. At the third stage the matrix is transformed into a matrix that provides non-negativity and optimality of the basic solution. For the second stage the analog of which in the simplex method uses an artificial basis and is the most time-consuming, two variants without artificial variables are given. When describing the first of them, along the way, a very easy-to-understand and remember analogue of the famous Farkas lemma is obtained. The other option is quite simple to use, but its full justification is difficult and will be separately published.

## Development of a heuristic to solve the general transportation problem

The transportation problem is well known and has very important applications. For this well-researched model, there are very efficient approaches for solving it that are available. These approaches include formulating the transportation problem as a linear program and then using the efficient methods such as the simplex method or interior point algorithms. The Hungarian method is another efficient method for solving both the assignment model and the general transportation model. An assignment problem is a special case of the transportation model in which all supply and demand points are 1. Every transportation problem can be converted into an assignment problem since rows and columns can be split so that each supply and each demand point is 1. The transportation simplex method is another method that is also used to solve the general transportation problem. This method is also called the modified distribution method (MODI). To use this approach, a starting solution is required and the closer the starting solution to the optimal solution, the fewer the iterations that are required to reach optimality. The fourth method for transportation models is the network simplex method, which is the fastest so far. Unfortunately, all these approaches for transportation models are serial in nature and are very difficult to parallelize, which makes it difficult to efficiently use the available massively parallel technology. There is a need for an efficient approach for the transportation problem, which is easily parallelizable. This paper presents a See-Saw approach for solving the general transportation problem. This is an extension of the See-Saw approach for solving the assignment problem. The See-Saw moves can be done independently, which makes the approach proposed in this paper more promising than the available methods for transportation models

## Improved convergence results for the Nelder-Mead simplex method in low dimensional spaces

Abstract We prove two results on the convergence of the Nelder-Mead simplex method. Both theorems prove the convergence of the simplex vertices to a common limit point. The first result is an improved variant of the convergence theorems of [7] and [8], while the second one proves the convergence with probability 1.

## Study of the soil stability theory problems by the simplex method

Abstract The questions of linear programming methods application to the main problems of stability theory - problems on slope stability, problems on ultimate pressure of soil on enclosures (case of landslide pressure), and problems on bearing capacity of horizontal base of a die are considered. The problems of stability theory are formulated as linear programming tasks. It is shown that the given systems of equations are linear with respect to the unknowns and may be solved by the Simplex method. The results of soil stability problems calculation by Simplex method are compared with the results of calculations according to the most known classical schemes. It is shown that a great scatter of final results is observed in calculating the stability of slopes by classical methods, and in this case, the results obtained by the Simplex method are the most trustworthy ones. The situation with landslide pressure definition is especially complicated in this sense where classical methods give a scatter of landslide pressure values by several times. It is established that with increasing discretization of the computational domain, the results tend to exact solutions of the limit equilibrium theory, obtained, for example, by the method of characteristics. The latter point is illustrated using the example of the problem of a die pushing into a ground massif with a Hill scheme bulge.

## Simplex Method

AbstractWe develop a matrix form of the Nelder-Mead simplex method and show that its convergence is related to the convergence of infinite matrix products. We then characterize the spectra of the involved matrices necessary for the study of convergence. Using these results, we discuss several examples of possible convergence or failure modes. Then, we prove a general convergence theorem for the simplex sequences generated by the method. The key assumption of the convergence theorem is proved in low-dimensional spaces up to 8 dimensions.

## A Simple Algorithm for Finding a Non-negative Basic Solution of a System of Linear Algebraic Equations

This article describes an algorithm for obtaining a non-negative basic solution of a system of linear algebraic equations. This problem, which undoubtedly has an independent interest, in particular, is the most time-consuming part of the famous simplex method for solving linear programming problems.Unlike the artificial basis Orden’s method used in the classical simplex method, the proposed algorithm does not attract artificial variables and economically consumes computational resources.The algorithm consists of two stages, each of which is based on Gaussian exceptions. The first stage coincides with the main part of the Gaussian complete exclusion method, in which the matrix of the system is reduced to the form with an identity submatrix. The second stage is an iterative cycle, at each of the iterations of which, according to some rules, a resolving element is selected, and then a Gaussian elimination step is performed, preserving the matrix structure obtained at the first stage. The cycle ends either when the absence of non-negative solutions is established, or when one of them is found.Two rules for choosing a resolving element are given. The more primitive of them allows for ambiguity of choice and does not exclude looping (but in very rare cases). Use of the second rule ensures that there is no looping.

## Linear Programming the Simplex Method

Implementing the simplex method with grover’s search, dispersion analysis and material property identification of a circular piezoelectric ridge waveguide.

Abstract This study investigates the dispersive properties of ridge waves that travel circumferentially around piezoelectric circular ridge waveguides and investigates their resonant modes. Based on the variable separation method and Hamilton's principle, the displacement of ridge waveguides is represented as the product of a cross-sectional coordinate-dependent function and the propagator along the circumference of a circular ridge waveguide. The dispersion curves of the flexural waves and resonant frequencies corresponding to ridge waveguides are solved numerically by applying the bi-dimensional finite element method (Bi-d FEM) and using the 3D ANSYS package. The estimated impedance curves are compared with the predicted dispersion curves of waves from ridge waveguides to validate the proposed numerical approach. The geometric parameters and elastic constants of the piezoelectric circular ridge waveguide are determined through an inverse scheme that is based on the measured resonant frequencies and uses the modified simplex method. The numerical and experimental results show that, using the modified simplex method to inverse calculate the elastic constants and geometric parameters of the piezoelectric circular ridge waveguides, a good degree of accuracy and sensitivity can be achieved.

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It is shown that the given systems of equations are linear with respect to the unknowns and may be solved by the Simplex method. The results of soil stability

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13. Explanation of Simplex Method

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