fcn2optimexpr 将其转换为优化表达式。请参阅Convert Nonlinear Function to Optimization Expression和Supported Operations for Optimization Variables and Expressions。
Learn the problem-based steps for solving optimization problems.
Learn the problem-based steps for solving equations.
Define expressions for both the objective and constraints.
Pass extra parameters, data, or fixed variables in the problem-based approach.
Syntax rules for problem-based least squares.
solve to use
for problem solution.
Create and work with named indices for variables.
Review or modify problem elements such as variables and constraints.
Evaluate the solution and its quality.
Obtain a faster or more accurate solution when the problem has integer constraints, and avoid loops when creating a problem.
Create reusable, scalable problems by separating the model from the data.
Create initial points for
solve when the problem has named
index variables by using the
Optimization expressions containing
NaN cannot be displayed, and can cause unexpected
Save time when the objective and nonlinear constraint functions share common computations in the problem-based approach.
Automatic differentiation lowers the number of function evaluations for solving a problem.
How to include derivative information in problem-based optimization when automatic derivatives do not apply.
Find the values of extra parameters in nonlinear functions created by
Use an output function in the problem-based approach to record iteration history and to make a custom plot.
Use multiple processors for optimization.
Perform gradient estimation in parallel.
Example showing the effectiveness of parallel computing
in two solvers:
Investigate factors for speeding optimizations.
Learn how automatic differentiation works.
Explore the supported mathematical and indexing operations for optimization variables and expressions.