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约束优化

约束优化
几何画板及绘图内容加载中，请稍等……
2015-10-15 03:19
函数微积分最值优化
该范例演示了约束优化的概念。通过使用函数和几何对象，用户可以探索在给定约束条件下优化目标函数的方法。构造过程中使用了CurveCartesian和Vector等GeoGebra命令。适合高中数学教学和学习，特别是微积分和优化理论的学习。

GeoGebra 命令及参数

  1. CurveCartesian(cos(t), sin(t), t, 0, 6.28) → S
  2. CurveCartesian(cos(t), sin(t), f(cos(t), sin(t)), t, 0, 6.28) → P
  3. CurveCartesian((sqrt(0.25) * cos(s)), (sqrt(0.25 / 2) * sin(s)), s, 0, 6.28) → d
  4. CurveCartesian((sqrt(0.5) * cos(s)), (sqrt(0.5 / 2) * sin(s)), s, 0, 6.28) → e
  5. CurveCartesian((sqrt(0.75) * cos(s)), (sqrt(0.75 / 2) * sin(s)), s, 0, 6.28) → g
  6. CurveCartesian(cos(s), (sqrt(1 / 2) * sin(s)), s, 0, 6.28) → a
  7. CurveCartesian((sqrt(1.25) * cos(s)), (sqrt(1.25 / 2) * sin(s)), s, 0, 6.28) → b
  8. CurveCartesian((sqrt(1.5) * cos(s)), (sqrt(1.5 / 2) * sin(s)), s, 0, 6.28) → h
  9. CurveCartesian((sqrt(1.75) * cos(s)), (sqrt(1.75 / 2) * sin(s)), s, 0, 6.28) → i
  10. CurveCartesian((sqrt(2) * cos(s)), sin(s), s, 0, 6.28) → j
  11. CurveCartesian((sqrt(2.25) * cos(s)), (sqrt(2.25 / 2) * sin(s)), s, 0, 6.28) → k
  12. CurveCartesian((sqrt(2.5) * cos(s)), (sqrt(2.5 / 2) * sin(s)), s, 0, 6.28) → l
  13. Vector(B, B + ((2 * cos(c)), (2 * sin(c)))) → u
  14. Vector(B, B + u / 4) → v
  15. Vector(((2 * cos(c)), (4 * sin(c)))) → w
  16. Vector(B, B + w / 4) → m