Numerical Analysis and Optimization
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 10312
Special Issue Editors
Interests: numerical analysis; optimization; line search; convergence rate; operations research; iterative methods
Interests: artificial neural networks; nonlinear systems; computational mathematics; machine learning
Special Issues, Collections and Topics in MDPI journals
2. Faculty of Sciences and Mathematics, University of Niš, 18000 Niš, Serbia
Interests: orthogonal polynomials, orthogonal systems and special functions; interpolation, quadrature processes and integral equations; approximations by polynomials, splines and linear operators; numerical and optimization methods; polynomials (extremal problems, inequalities, zeros); iterative processes and inequalities
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We prepare a collection of papers in applied mathematics regarding its two essential subject matters: numerical analysis and optimization. These two areas are the forerunners of the mathematical modeling and the numerical simulations. Mathematical modeling tools allow us to transform a physical reality into adequate abstract models on which we can further apply relevant calculations. Correspondingly, numerical simulation presents the process through which we calculate the solutions of the mathematical models on a computer, thus allowing us to simulate physical reality.
Numerical analysis, as an area of mathematics and computer science, analyzes and inspects convergence properties, and implements algorithms for solving various problems numerically. Such problems could originate from real-world applications of algebra, geometry, calculus and other mathematical disciplines. These problems generally appear to the natural sciences, social sciences, engineering, medicine, business and any other real-life areas.
On the numerical analysis and the optimization theory basis, many efficient iterative processes can be established. These models can be applied to solve different types of problems which often occur throughout matrix equations. Solving different types of matrix equations appears as a contemporary problem in almost any computational procedure, such as training algorithms in machine learning, numerical simulations in many scientific and engineering areas and advanced data analysis in economics and social sciences.
Dr. Milena J. Petrović
Prof. Dr. Predrag S. Stanimirovic
Prof. Dr. Gradimir V. Milovanović
Guest Editors
Manuscript Submission Information
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Keywords
- unconstrained optimization
- constrained optimization
- optimization methods
- iterative processes
- gradient-descent methods
- projection methods
- line search
- convergence rate
- hybrid methods
- linear matrix equations
- nonlinear system of equations
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