# Optimization Techniques in MATLAB

### Prerequisites

MATLAB Fundamentals. Knowledge of linear algebra and multivariate calculus is helpful.

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Day 1 of 1
Running an Optimization

Objective: Understand the basic structure and process of solving optimization problems effectively. Use interactive tools to define and solve optimization problems.

• Identifying the problem components
• Running an optimization using Optimization Tool
• Applying the optimization process
• Using optimization functions
Specifying the Objective Function

Objective: Implement an objective function as a function file. Use function handles to specify objective functions and extra data.

• Using an objective function file
• Specifying objective functions with function handles
• Passing extra data to objective functions
Specifying Constraints

Objective: Add different kinds of constraints to an optimization problem in MATLAB.

• Identifying different types of constraints
• Defining bounds
• Defining linear constraints
• Defining nonlinear constraints
Choosing a Solver

Objective: Select an appropriate solver and algorithm by considering the type of optimization problem to be solved.

• Classifying the objective
• Choosing a solver
• Choosing the algorithm
Evaluating Results and Improving Performance

Objective: Interpret the output from the solver and diagnose the progress of an optimization. Increase accuracy and efficiency of an optimization by changing settings.

• Examining the optimization
• Interpreting the result
• Setting convergence options
• Providing derivative information
Global Optimization

Objective: Use Global Optimization Toolbox functionality to solve problems where classical algorithms fail or work inefficiently.

• Finding the global minimum
• Using genetic algorithms to solve discrete problems