Optimization w.r.t. objective function.

Introduction:

An objective optimization problem seeks the best solution to the current benchmark or criterion, such as processing time or effectiveness, or a mixture of this measurement system with power consumption or power handling metrics.

 Mathematical programming selects the appropriate component from several alternatives depending on some criterion. constrained optimization problems of various kinds emerge in all quantifiable subject areas, ranging from computer engineering and programming to industrial engineering and economy. The advancement of methodologies has long been a subject of investigation in mathematics.  

Constrained optimizations in economics:

The examples of unconstrained optimizations in economics are obtaining the optimal profit, highest revenue, lowest price, etc. constrained optimization is a vital instrument in economics and everyday life. Consumers pool in methodology to improve while still being constructed by a wide range of constraints: their budget. Even Bill Gates is limited in consuming everything he desires in this evolving world. Similarly, while improving profitability or reducing cost, production companies in everyday life encounter a range of financial challenges, such as constrained resources, costs of production, and so on.

 The widely used mathematical methodology for constrained optimizations involves utilizing the Lagrange multiplier and Lagrange function to address this problem. The Bordered Hessian follows this to verify the higher-order circumstances. Given that the optimum point is an interior optimum and the objective function is of two parameters. The constrained optimization problem can also be settled by using a geometric approach.

 Jaya algorithm is a recently approved algorithm capable and has a high potential for solving constrained and unconstrained optimization problems.

Constrained optimization problem:

Lets now understand the constrained optimization problem:

Increase utility u=f(x,y)=xy subject to the requirement g(x,y)=x+4y=250 In this case, the price per unit x is 15, the price per unit y is 55, and the cost estimate available to buy x and y is 250. Use the geometric approach to solve the problem.

Solution:

The optimization problem in this case is:

Objective function is: maximize u(x,y)=xyThis is subject to the constraint: g(x,y)=x+4y=250

Step 1: –fxfy = – yx   (This the indifference curve slope)

Step 2: –gxgy = – 15       (This is the budget line slope)

Step 3: –fxfy = – gxgy     

In order to maximise utility, the indifference curve slope should equate to the budget line slope.)

–yx = – 15  

x = 5 y

Step 4: To obtain the critical values, begin with step 3 and use the relation between x and y in the constraint function.

x+5y=250

5y+5y=250

10y=250

y=25

Using y=25 in relation to the base equation, x=5y we get x=5×25=125

The utility may be maximized at (125,25).

Convex optimization: 

A convex optimization problem is one in which you want to reach a position that maximizes/minimizes the objective function utilizing incremental calculations generally, incremental regularisation & linear programming with convex functions.

Some day-to-day examples of convex optimization problems are as the following:

Convex optimization problem example:

• The flight schedule is a good example of a convex optimization problem. It entails determining flight times that reduce costs such as fuel pilot/crew expenses and expanding the number of passengers.
• Facility location problems are designed to improve the use of resource bases within a facility. These types of issues can be found in a variety of industries, including supply chain management, telecommunication services, and industrial production.
• The optimization technique in inventory management reduces the operating costs of purchasing, carrying, and scarcity while keeping stocks inside the desired value.
• Convex programming has been used to solve routing phone calls’ standard network design flaw, which is an example of a convex optimization problem. The difficulty in routing phone calls is determining the shortest distance that connects a group of phone callers to receivers.
• Convex optimization can be used to reduce a fleet’s total operating costs, subject to certain transportation and work schedules constraints.
• Convex optimization problems in logistical issues are concerned with determining the amount and variety of vehicles that should be used to move cargo from a set of intermediaries (e.g., storage facilities) to a group of customers (e.g., Super Market).  

Conclusion 

We discussed constrained optimization, constrained optimization problems & convex optimization problems example, in India, and other related topics through the study material notes on Optimization w.r.t. objective function. We also discussed how to achieve constrained and unconstrained optimization to give you proper knowledge. 

Constrained optimization is an arrangement of techniques for detecting the perfect option or the optimized solution to a problem with several possible alternatives in the existence of identified constituents.