Solve multiobjective optimization problems in serial or parallel
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,
|Solve multiobjective goal attainment problems|
|Solve minimax constraint problem|
Live Editor Tasks
|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.
- Compare fminimax and fminunc
Shows how minimax problems are solved better by the dedicated
fminimaxfunction 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.
- Minimax Optimization
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.
Algorithms and Other Theory
- Multiobjective Optimization Algorithms
Minimizing multiple objective functions in n dimensions.
- Smooth Formulations of Nonsmooth Functions
Reformulate some nonsmooth functions as smooth functions by using auxiliary variables.
- Optimization Options Reference
Explore optimization options.