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