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Τι (ποιος) είναι optimization$55406$ - ορισμός

WAY OF EXPLORING REACTION PATHS IN COMPUTATIONAL CHEMISTRY
Geometry optimization; Energy minimization (energy optimization)

Price optimization         
USE OF PRICING METHODS TO MAXIMIZE THE PURCHASING BEHAVIOR OF CONSUMERS
Price optimization software
Price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services through different channels. It is also used to determine the prices that the company determines will best meet its objectives such as maximizing operating profit.
Mathematical optimization         
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STUDY OF MATHEMATICAL ALGORITHMS FOR OPTIMIZATION PROBLEMS
Mathematical programming; Optimisation; Optimization theory; Cost functional; Optimal; Optimum; Searching the search space; Optimisation (mathematics); Optimization glossary; Numerical optimization; Mathematical optimisation; Optimizer; Optimation; Ordinal optimization; Energy function; Optimizing; Function optimization; Optimization algorithm; Optimal allocation; Optimization; Optimization (mathematics); Numerical optimisation; Optimally; Make the most out of; Make the most of; Numerical optimization problem; Computational optimization techniques; Mathematical optimization algorithms; Applications of mathematical optimization; Applications of optimization; Algorithms for optimization; Algorithms for solving optimization problems; Automated optimization; Interior solution (optimization); History of mathematical optimization; Algorithm's optimality; Optimization problems in economics; Optimization problems in electrical engineering; Optimization of electrical circuits; Optimization of electronic circuits; Optimization heuristic; Optimization (mathematical); Optimization in electrical engineering
Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives."The Nature of Mathematical Programming ," Mathematical Programming Glossary, INFORMS Computing Society.
Multi-objective optimization         
  • Example of a [[Pareto frontier]] (in red), the set of Pareto optimal solutions (those that are not dominated by any other feasible solutions). The boxed points represent feasible choices, and smaller values are preferred to larger ones. Point ''C'' is not on the Pareto frontier because it is dominated by both point ''A'' and point ''B''. Points ''A'' and ''B'' are not strictly dominated by any other, and hence do lie on the frontier.
AREA OF MULTIPLE CRITERIA DECISION MAKING, THAT IS CONCERNED WITH MATHEMATICAL OPTIMIZATION PROBLEMS INVOLVING MORE THAN ONE OBJECTIVE FUNCTION TO BE OPTIMIZED SIMULTANEOUSLY
Multiobjective problem; Multiobjective programming; Multiple objective optimization; Multiobjective optimisation; Multiobjective optimization; NSGA-II; Non-dominated Sorting Genetic Algorithm-II; Solutions of multi-objective optimization problems; Multivariate optimization; Multicriteria optimization; Bicriteria optimization; Pareto optimization
Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Βικιπαίδεια

Energy minimization

In the field of computational chemistry, energy minimization (also called energy optimization, geometry minimization, or geometry optimization) is the process of finding an arrangement in space of a collection of atoms where, according to some computational model of chemical bonding, the net inter-atomic force on each atom is acceptably close to zero and the position on the potential energy surface (PES) is a stationary point (described later). The collection of atoms might be a single molecule, an ion, a condensed phase, a transition state or even a collection of any of these. The computational model of chemical bonding might, for example, be quantum mechanics.

As an example, when optimizing the geometry of a water molecule, one aims to obtain the hydrogen-oxygen bond lengths and the hydrogen-oxygen-hydrogen bond angle which minimize the forces that would otherwise be pulling atoms together or pushing them apart.

The motivation for performing a geometry optimization is the physical significance of the obtained structure: optimized structures often correspond to a substance as it is found in nature and the geometry of such a structure can be used in a variety of experimental and theoretical investigations in the fields of chemical structure, thermodynamics, chemical kinetics, spectroscopy and others.

Typically, but not always, the process seeks to find the geometry of a particular arrangement of the atoms that represents a local or global energy minimum. Instead of searching for global energy minimum, it might be desirable to optimize to a transition state, that is, a saddle point on the potential energy surface. Additionally, certain coordinates (such as a chemical bond length) might be fixed during the optimization.