Get Started withOptimization Toolbox

Optimization Toolbox™ provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming (QP), second-order cone programming (SOCP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations.

You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use automatic differentiation of objective and constraint functions for faster and more accurate solutions.

您可以使用工具箱求解器来查找连续和离散问题的最佳解决方案，执行权衡分析，并将优化方法纳入算法和应金宝搏官方网站用程序。工具箱允许您执行设计优化任务，包括参数估计，组件选择和参数调整。它使您可以在产品组合优化，能源管理和交易等应用中找到最佳解决方金宝搏官方网站案，以及生产计划。

Tutorials

• 首先选择基于问题的或基于求解的方法
  

  There are two approaches to using Optimization Toolbox solvers: problem-based and solver-based. Before you start, choose the approach.

• Solve a Constrained Nonlinear Problem, Problem-Based
  

  A basic example of solving a nonlinear optimization problem with a nonlinear constraint using the problem-based approach.

• Solve a Constrained Nonlinear Problem, Solver-Based
  

  Presents an example that minimizes a nonlinear function with a nonlinear constraint.

• Get Started with Problem-Based Optimize Live Editor Task
  

  Basic example using the problem-basedOptimizeLive Editor task.

• Use Problem-Based Optimize Live Editor Task Effectively
  

  How to use and understand the problem-basedOptimizeLive Editor task.

• Get Started with Solver-Based Optimize Live Editor Task
  

  An example script to modify for using the solver-based Optimize Live Editor task.

• Use Solver-Based Optimize Live Editor Task Effectively
  

  How to use the solver-basedOptimizeLive Editor task effectively.

• 建立了一个线性规划,具体问题具体分析
  

  Linear problem formulation using the problem-based approach.

• Set Up a Linear Program, Solver-Based
  

  使用基于求解器的方法的问题制定。

About Optimization

• Optimization Theory Overview
  

  Introduces optimization as a way of finding a set of parameters that can be defined as optimal. These parameters are obtained by minimizing or maximizing an objective function, subject to equality or inequality constraints and/or parameter bounds.

• Optimization Toolbox Solvers
  

  Descriptions of optimization solvers.

• Local vs. Global Optima
  

  Explains why solvers might not find the smallest minimum.