Optimization techniques

Optimization:

Optimization means to increase the effectiveness of something by choosing the best component or element or set of elements from different sets of other available possibilities.
In computer language, optimization means to write some program as concise as possible for rapid and more efficient retrieval, later on.

Optimizer or Defragger:

Optimization program is usually referred to as defragger or optimizer in computer world.

Optimization techniques:

Optimization techniques are basically collected mathematical rules and methods to solve quantitative problems in many of the fields of life like physics, engineering, biology, business and economics.

The growth of optimization techniques is collateral to not only to the computer science but also to numerical analysis, operations research, game theory control theory, mathematical economics and combinatorics.

Classes of optimization:

Linear programming:

In computer language, linear programming shows the procedure for making programs that help to find most optimum solutions for linear functions in sets of equations in which sufficient terms are not there to produce straightforward solution.

And in mathematical terms, it represents the way of searching the maximum and minimum numerical quantity of linear transformation with the use of variables that are likely to be affected by constraints.

Linear programming is applied in the following researches:

1. Papoutsakis ET: (1984). Equations and calculations for fermentations of butyric acid bacteria. Biotechnology and Bioengineering , 174-187.

2. Varma A, Palsson BO: (1994). Metabolic flux balancing – basic concepts, scientific and practical use. Biotechnology, 994-998.

3. Price ND, Reed JL, Palsson BO: (2004). Genome scale models of microbial cells: Evaluating the consequences constraints. Nature Reviews microbiology, 886-897

Nonlinear programming:

Some of the constraints in this type of programming may involve nonlinear functions i.e. may involve square roots or squares.

Nonlinear programming is applied in the following researches:

1. Mendes P, Kell DB: (1998). Nonlinear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics, 869-883

2. Vo TD, Pallsson BO: (2006). Isotopomer analysis of myocardial substrate metabolism. A systems biology approach. Biotechnology and Bioengineering, 972-983

3. Vo TD, Lee WNP, Pallsson BO: (2007). Systems analysis of energy metabolism elucidates the affected respiratory chain complex in Leigh’s syndrome. Molecular Genetics and Metabolism, 15-22.

Stochastic programming:

In this class, constraints are dependent on random occurrences or variables, so that the optimum values can be found in some expected meanings as for example a stochastic model shows that system in which both chance events and planned events are taken into account.

Network optimization:
Here we consider, for optimization, some of the property related to flow of matter or variable through a network.

Combinatorial optimization:

Here in this solution is to be found among limited system of values which are also huge in number.