Advances in Optimization and Linear Programming

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A01=Ivan Stanimirovic
Admissible Solution
advanced simplex method techniques
Author_Ivan Stanimirovic
Basic Solution
Category=PBKJ
Category=UMB
constraint properties
Dependent Constraints
duality theory
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
game theory applications
Gaussian Elimination
General Inverses
Goal Functions
heuristic algorithms
Linear Programming
Linear Programming Problems
Minimum Angle
Moo
Moore Penrose Inverse
Multi-objective Optimization
multiobjective optimization
Objective Function Decreases
Pareto Optimal Solution
Pareto optimality
Pareto Optimum
Procedural Programming Languages
Simplex Method
Single Criteria Problem
Target Function
Weak Pareto Optimum
Weight Coefficients

Product details

  • ISBN 9781774637418
  • Weight: 380g
  • Dimensions: 152 x 229mm
  • Publication Date: 08 Jul 2024
  • Publisher: Apple Academic Press Inc.
  • Publication City/Country: CA
  • Product Form: Paperback
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This new volume provides the information needed to understand the simplex method, the revised simplex method, dual simplex method, and more for solving linear programming problems.

Following a logical order, the book first gives a mathematical model of the linear problem programming and describes the usual assumptions under which the problem is solved. It gives a brief description of classic algorithms for solving linear programming problems as well as some theoretical results. It goes on to explain the definitions and solutions of linear programming problems, outlining the simplest geometric methods and showing how they can be implemented. Practical examples are included along the way. The book concludes with a discussion of multi-criteria decision-making methods.

Advances in Optimization and Linear Programming is a highly useful guide to linear programming for professors and students in optimization and linear programming.

Ivan Stanimirović, PhD, is currently Associate Professor at the Department of Computer Science, Faculty of Sciences and Mathematics at the University of Niš, Serbia. He was formerly with the Faculty of Management at Megatrend University, Belgrade, as a lecturer. His work spans from multi-objective optimization methods to applications of generalized matrix inverses in areas such as image processing and restoration and computer graphics. His current research interests include computing generalized matrix inverses and their applications, applied multi-objective optimization and decision-making, as well as deep learning neural networks. Dr. Stanimirović was the chairman of a workshop held at the 13th Serbian Mathematical Congress, Vrnjaèka Banja, Serbia, in 2014.

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