Optimization is a problem associated with the best decision that is effective and efficient decisions whether it is worth maximum or minimum by way of determining a satisfactory solution.
Optimization is not a new science. It has grown even since Newton in the 17th century discovered how to count roots. Currently the science of optimization is still evolving in terms of techniques and applications. Many cases or problems in everyday life that involve optimization to solve them. Lately much developed especially in the emergence of new techniques to solve the problem of optimization. To mention some, among others, conic programming, semi definite programming, semi infinite programming and some meta heuristic techniques.
Optimization plays an important role in the process of designing a system. With optimization, the design of a system can result in cheaper or higher cost, lower processing time and so on. For now, much software help is needed to solve the wrong problem found to get the optimal solution with computation time not too long. Applications of optimization techniques have mushroomed in various fields quickly.
Successful application of optimization techniques requires at least three conditions. These requirements are the ability to make mathematical models of problems encountered, knowledge of optimization techniques and knowledge of computer programs. The notion of optimization can be explained as a set of mathematical formulas and numerical methods for finding and identifying the best candidates from a set of alternatives without having to explicitly compute and evaluate all possible alternatives.
Optimization is the process of maximizing or minimizing a function of purpose by keeping in mind the existing constraints. A function is defined as a rule that assigns each choice of value x with a unique value y = f (x). In this case x is the independent variable and y is the dependent variable. Mathematically, suppose we have set S ⊂ R, where R is the set of all real numbers. We can define a transformation assigning a numerical value for each x ⊂ S. This relationship is often called the scalar function f defined in the set S.
Optimization problems can be divided according to several categories:
In addition, optimization problems can also be grouped by the number of variables:
Optimization problems can also be seen from the value of the variable. Grouping optimization problems based on variable values are:
Specifically the problem of optimization can be categorized by the level of decision variables, objective and constraint functions, among others as follows:
Related to this you can read at: https://en.wikipedia.org/wiki/Multi-objective_optimization
Hopefully my writing can be useful for all of us
Thanks (^_^)
Comment
Thank you for the reply, Jeefri.
Thank you for reading my article.
for discussion of the chart above, I apologize because of course I can not discuss it through this media.
for further understanding maybe you can read my book.
here is the link: https://www.amazon.com/Optimization-Performance-Analysis-Interest-I...
and my website : https://exa.000webhostapp.com/
Nice introduction to optimization. However, you used an image of charts that are not discussed in your blog. It would have been nice to see your interpretation of these charts. Maybe you will consider doing so in a future posting.
© 2020 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Upcoming DSC Webinar
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Upcoming DSC Webinar
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central