AMPL具体在什么时间被提出

AMPL（A Mathematical Programming Language）是由Robert Fourer、David M. Gay和Brian W. Kernighan于1987年提出的。它是一种高级编程语言，用于建模和求解数学规划问题，如线性规划、非线性规划、整数规划等。在数学优化领域，AMPL是一个广泛使用的工具，并且在企业、学术界和政府机构中得到了广泛应用。

相关问题

在AMPL建立双层模型文件.mod，以时间最短和曲率连续为代价函数,具体代码什么样的

在AMPL建立一个包含两层的模型（通常用于动态规划或多期决策问题）通常涉及两个部分：模型的数学描述和数据输入。以下是一个基本的示例，演示如何构建一个时间最短和曲率连续为目标的双层线性规划模型。假设我们有一个序列决策问题，每一步都有两个决策变量x和y，并且需要最小化总时间（T）和曲率的变化（K）：

model "my_model" {
  sets:
    Periods / 1..MaxPeriods;        # 定义时间段
    Actions / A1..AMaxActions;       # 可供选择的动作

  parameters:
    Durations(Periods, Actions);     # 每个动作在每个时期的持续时间
    CurvatureChanges(Actions);      # 动作导致曲率变化的系数

  variables:
    x[Periods, Actions];             # 第一层决策：动作选择
    T[Periods] / Total time;         # 时间总和
    K[Periods] / Total curvature change;  # 曲率变化总和

  equations:
    time_obj:                         # 时间目标函数
      for p in Periods do
        T[p] = sum{a in Actions} Durations[p,a]*x[p,a];
      end;

    curvature_obj:                    # 曲率连续目标函数（这里假设曲率变化是非负的）
      for p in 2..MaxPeriods do
        K[p] = K[p-1] + sum{a in Actions} CurvatureChanges[a]*x[p-1,a] * (x[p,a] - x[p-1,a]);
      end;

    feasibility:                      # 强制曲率为连续的约束
      K[p] >= K[p-1] for p in 2..MaxPeriods;
}

data;
# ...在这里提供具体的Durations, CurvatureChanges等参数值...

AMPL python

AMPL (Algebraic Modeling Language) is a mathematical modeling language used for formulating and solving optimization problems. It provides a high-level, algebraic syntax for expressing mathematical models and connects to various solvers for solving these models.

To use AMPL in Python, you can use the amplpy library, which is a Python interface to AMPL. Here's an example of how you can use AMPL with Python:

1. First, make sure you have AMPL and amplpy installed. You can install amplpy using pip:

pip install amplpy

2. Import the necessary libraries in your Python code:

import amplpy as amp

3. Create an AMPL instance and load your AMPL model file:

ampl = amp.AMPL()
ampl.read("your_model.mod")

4. Set any data or parameters required by your model:

ampl.setOption('solver', 'cplex')  # Set the solver (in this case, CPLEX)
ampl.setParameter('n', 10)  # Set a parameter 'n' to a value of 10

5. Solve the optimization problem:

ampl.solve()

6. Retrieve the solution and print the results:

objective_value = ampl.getObjective('objective_name').value()
variable_value = ampl.getVariable('variable_name').getValues().toPandas()
print("Objective value: ", objective_value)
print("Variable values: ", variable_value)

This is just a basic example to give you an idea of how to use AMPL with Python. There are many more features and functionalities available in AMPL and amplpy that you can explore for more complex modeling and optimization tasks.