Solve problems that have multiple objectives by the goal attainment
method. For this method, you choose a goal for each objective, and the
solver attempts to find a point that satisfies all goals simultaneously,
or has relatively equal dissatisfaction. One important special case of
this problem is to minimize the maximum objective, and this problem has
a special solver, fminimax.
fgoalattain | Solve multiobjective goal attainment problems |
fminimax | Solve minimax constraint problem |
| Optimize | Optimize or solve equations in the Live Editor |
Generate and Plot Pareto Front
Example showing how to plot a Pareto front in a two-objective problem.
Shows how minimax problems are solved better by the dedicated
fminimax function than by solvers for smooth
problems.
Multi-Objective Goal Attainment Optimization
This example shows how to solve a pole-placement problem using multiobjective goal attainment.
Using fminimax with a Simulink® Model
Example showing how to minimize the maximum discrepancy in a simulation.
Signal Processing Using fgoalattain
Example showing filter design using multiobjective goal attainment.
This example shows how to solve a nonlinear filter design problem.
What Is Parallel Computing in Optimization Toolbox?
Use multiple processors for optimization.
Using Parallel Computing in Optimization Toolbox
Perform gradient estimation in parallel.
Improving Performance with Parallel Computing
Investigate factors for speeding optimizations.
Multiobjective Optimization Algorithms
Minimizing multiple objective functions in n dimensions.
Optimization Options Reference
Explore optimization options.