Before you begin to solve an optimization problem, you must choose the appropriate approach: problem-based or solver-based. For details, seeFirst Choose Problem-Based or Solver-Based Approach.
有关问题设置,请参阅Solver-Based Optimization Problem Setup.
fminbnd |
Find minimum of single-variable function on fixed interval |
fmincon |
Find minimum of constrained nonlinear multivariable function |
fminsearch |
查找最少的无约束多变量功能using derivative-free method |
fminunc |
查找最少的无约束多变量功能 |
fseminf |
查找最少的半无限受限的多变量非线性功能 |
优化 | 优化or solve equations in the Live Editor |
显示如何使用不同的求解器,有或没有梯度来解决Rosenbrock的函数的最小值。
Unconstrained Minimization Using fminunc
无约束非线性规划的例子。
Minimization with Gradient and Hessian
Example of unconstrained nonlinear programming including derivatives.
Example of nonlinear programming using some derivative information.
Tutorial example showing how to solve nonlinear problems and pass extra parameters.
优化Live Editor Task with fmincon Solver
Example of nonlinear programming with constraints using the Optimize Live Editor Task.
Nonlinear Inequality Constraints
Example of nonlinear programming with nonlinear inequality constraints.
具有衍生信息的非线性编程示例。
Example of nonlinear programming with all derivative information.
Linear or Quadratic Objective with Quadratic Constraints
This example shows how to solve an optimization problem that has a linear or quadratic objective and quadratic inequality constraints.
Nonlinear Equality and Inequality Constraints
具有两种类型非线性约束的非线性编程。
How to Use All Types of Constraints
Example showing all constraints.
找到最好的可行点output
structure.
Example showing efficiency gains possible with structured nonlinear problems.
Minimization with Linear Equality Constraints, Trust-Region Reflective Algorithm
Example showing nonlinear programming with only linear equality constraints.
Minimization with Dense Structured Hessian, Linear Equalities
示例显示如何使用结构化Hessian和仅线性平等约束或仅限界限将内存保存在非线性编程中。
Calculate Gradients and Hessians Using Symbolic Math Toolbox™
示例展示如何计算符号为优化求解器的衍生物。
Use Symbolic Math Toolbox™ to generate gradients and Hessians.
Code Generation in fmincon Background
Prerequisites to generate C code for nonlinear optimization.
Learn the basics of code generation for thefmincon
优化求解器。
在问题更改时使用代码生成中的静态内存分配。
Optimization Code Generation for Real-Time Applications
探索在生成的代码中处理实时要求的技术。
示例显示如何在非线性编程中使用一维半无限约束。
Two-Dimensional Semi-Infinite Constraint
示例显示如何在非线性编程中使用二维半无限约束。
Analyzing the Effect of Uncertainty Using Semi-Infinite Programming
This example shows how to use semi-infinite programming to investigate the effect of uncertainty in the model parameters of an optimization problem.
Use multiple processors for optimization.
Using Parallel Computing in Optimization Toolbox
并行执行梯度估计。
Improving Performance with Parallel Computing
Investigate factors for speeding optimizations.
Minimizing an Expensive Optimization Problem Using Parallel Computing Toolbox™
Example showing how to use parallel computing in bothGlobal Optimization Toolboxand Optimization Toolbox™ solvers.
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
Minimizing a single objective function inndimensions without constraints.
Constrained Nonlinear Optimization Algorithms
Minimizing a single objective function inn尺寸具有各种类型的约束。
Steps thatfminsearch
需要最小化函数。
Optimization Options Reference
Explore optimization options.
Explains why solvers might not find the smallest minimum.
Smooth Formulations of Nonsmooth Functions
Reformulate some nonsmooth functions as smooth functions by using auxiliary variables.
Lists published materials that support concepts implemented in the solver algorithms.