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楼主: mathematica

[提问] 由“陈计的一道代数不等式”所发出的疑问

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发表于 2010-7-12 10:13:55 | 显示全部楼层
$s=1+{2.7771904733328640131}/{\sqrt{x}}-{3.1087580197427311379}/{x+2 \ln (x)}$ s.png

点评

我得到的公式比你的更精确,你的坐标轴的两个单位长度不一样,同时原点也有问题  发表于 2016-7-6 12:28
奇怪,这个s的值有一个精确的下界吗?  发表于 2014-6-28 16:47

评分

参与人数 1贡献 +4 收起 理由
hujunhua + 4 漂亮的弧度,力的感觉

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毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
发表于 2010-7-12 20:26:22 | 显示全部楼层
相关系数能达到几个9?拟合公式貌似与mathe的预测有出入。这个可能只管较小的n吧?
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
发表于 2010-7-13 08:21:16 | 显示全部楼层
我的预测也只是大概,估计出来的$log(x)$项不是很合理,看来主项还是应该$c/{sqrt(n)}$,
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
发表于 2010-7-13 14:30:50 | 显示全部楼层
52# hujunhua 相关系数是0.99997。 这是两个待定参数的公式,如果用三个参数来拟合,相关系数可以达到六个9
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
发表于 2014-6-28 16:48:21 | 显示全部楼层
wayne 发表于 2010-7-13 14:30
52# hujunhua

相关系数是0.99997。

这么好的相关系数,倒使我很想知道这个精确的公式是啥了
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
发表于 2014-6-30 11:39:37 | 显示全部楼层
考虑`\D f(x)=\ln(x+\frac{1}{x})`,或许下面的定理有用。

根据http://www.artofproblemsolving.c ... hp?f=52&t=89035中8#,Vasc说他在某本代数不等式的书上看到有下列定理:

LCRCF-定理(左凹右凸函数定理) `a,b,c`是满足`a < c < b`的实数,`f`是区间`I=[a,b)`上的连续函数,且在`[a,c]`上是凹的,在`[c,b)`上是凸的。
若`x_1,x_2,x_3,\ldots,x_n \in I`满足`x_1+x_2+\cdots+x_n=S`(常数),其中`S<(n-1)c+b`,那么
`f(x_1)+f(x_2)+\cdots+f(x_n)`在`x_1=x_2=\cdots=x_{n-1} \leqslant x_n`时取最大值。

SIP-定理(单拐点定理) `f`是`R`上只有一个拐点的二次可微函数,`S`是某个固定的实数,令`\D g(x)=f(x)+(n-1)f(\frac{S-x}{n-1})`.
若`x_1,x_2,...,x_n`是满足`x_1+x_2+\cdots+x_n=S`的实数,那么
$$\inf_{x \in R}g(x) \leqslant f(x_1)+f(x_2)+\cdots+f(x_n)\leqslant \sup_{x \in R}g(x)$$.
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
发表于 2014-6-30 12:53:14 来自手机 | 显示全部楼层
lcrcf显然是错误的,比如我们选择函数f在x<c时为x(c-x)在x>c时为(x-c)(x-2c).于是我们选择任意大的b对应的函数都符合题目要求,但是这样的函数显然不符合条件,我们可以选S使得S/n落在函数最小值点

点评

在手机上看不清楚,我看错了。  发表于 2014-6-30 21:41
可能是国外的凸函数定义跟国内相反,很多国外教科书或者科普书籍中关于凸函数的说法和国内的一些教材中的相反。  发表于 2014-6-30 14:40
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
 楼主| 发表于 2016-7-5 12:54:37 | 显示全部楼层
@hujunhua
"漂亮的弧度,力的感觉"
一点也不漂亮!

  1. (*非线性方程的拟合*)
  2. Clear["Global`*"];(*Clear all variables*)
  3. data={{3,2.00286740806350017941198515943545931361},
  4. {4,1.9283589164749804283600849366519560639},
  5. {5,1.86400765000052485077735500471126683613},
  6. {6,1.81051438122816316692944707545183728148},
  7. {7,1.76578235001873982989444491931146614181},
  8. {8,1.72786431204913392367633970225491638819},
  9. {9,1.69526942444040933881970369205445408976},
  10. {10,1.66689198313550214392865903283543517526},
  11. {11,1.64190904616094851366101664075402220374},
  12. {12,1.6196998599244209974512574541584287912},
  13. {13,1.59978869842201869056004669676183358389},
  14. {14,1.58180517574496658723209695065387815411},
  15. {15,1.56545660481407284533740801524947779306},
  16. {16,1.55050851817644197976392356354789658309},
  17. {17,1.5367707372478112927676021766159055014},
  18. {18,1.52408725542126641935994748503824288176},
  19. {19,1.51232877977090028689582251305994783841},
  20. {20,1.50138715374182425550739674900868882105},
  21. {21,1.49117113037198696816232030789158595774},
  22. {22,1.481603128981051478805513257481434365},
  23. {23,1.47261671766567601608562219697005149641},
  24. {24,1.46415463821023838316030988522039252819},
  25. {25,1.45616724114422507854121331602445094798},
  26. {26,1.4486112343465122080296824494356282472},
  27. {27,1.44144867381000076000046233040898435983},
  28. {28,1.43464614322487864607990982178408997221},
  29. {29,1.42817408210585004403922210801221054461},
  30. {30,1.4220062317567129945412344765141001355},
  31. {31,1.41611917544533369254102429082160513084},
  32. {32,1.41049195445244880077344760779527345639},
  33. {33,1.4051057456479795593700060551553949446},
  34. {34,1.39994358928469090337863094895696596845},
  35. {35,1.39499015802841523529377394252379049117},
  36. {36,1.39023156004523883530353774388849366078},
  37. {37,1.38565517036913390218387987164360902442},
  38. {38,1.38124948587417599494888974270272683602},
  39. {39,1.37700400004464983512682767443775686515},
  40. {40,1.37290909442703835047823373266554024708},
  41. {41,1.36895594420004818247311699643331377899},
  42. {42,1.36513643574275156839766072988517148305},
  43. {43,1.36144309443977055373412981089021928638},
  44. {44,1.35786902125399984336720056851594591129},
  45. {45,1.35440783683543930473427573602498607559},
  46. {46,1.3510536321300155887091520853891945445},
  47. {47,1.34780092461321684380654451302931688756},
  48. {48,1.34464461940656131097555379617152740538},
  49. {49,1.34157997464560704966451256923364150444},
  50. {50,1.33860257056055295892954001842319703868}};
  51. out=NonlinearModelFit[data,a+b/x^(1/2)+c/x^(3/2), {a,b,c}, x]
  52. Normal[out]
  53. Show[Plot[out[x],{x,0,52},PlotRange->{{0,54},{0,2.5}},AspectRatio->Automatic],ListPlot[data],ImageSize->1200]
复制代码

QQ截图20160705125218.png
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
 楼主| 发表于 2016-7-5 13:12:06 | 显示全部楼层

  1. (*直线方程的拟合*)
  2. Clear["Global`*"];(*Clear all variables*)
  3. data={{3,2.00286740806350017941198515943545931361},
  4. {4,1.9283589164749804283600849366519560639},
  5. {5,1.86400765000052485077735500471126683613},
  6. {6,1.81051438122816316692944707545183728148},
  7. {7,1.76578235001873982989444491931146614181},
  8. {8,1.72786431204913392367633970225491638819},
  9. {9,1.69526942444040933881970369205445408976},
  10. {10,1.66689198313550214392865903283543517526},
  11. {11,1.64190904616094851366101664075402220374},
  12. {12,1.6196998599244209974512574541584287912},
  13. {13,1.59978869842201869056004669676183358389},
  14. {14,1.58180517574496658723209695065387815411},
  15. {15,1.56545660481407284533740801524947779306},
  16. {16,1.55050851817644197976392356354789658309},
  17. {17,1.5367707372478112927676021766159055014},
  18. {18,1.52408725542126641935994748503824288176},
  19. {19,1.51232877977090028689582251305994783841},
  20. {20,1.50138715374182425550739674900868882105},
  21. {21,1.49117113037198696816232030789158595774},
  22. {22,1.481603128981051478805513257481434365},
  23. {23,1.47261671766567601608562219697005149641},
  24. {24,1.46415463821023838316030988522039252819},
  25. {25,1.45616724114422507854121331602445094798},
  26. {26,1.4486112343465122080296824494356282472},
  27. {27,1.44144867381000076000046233040898435983},
  28. {28,1.43464614322487864607990982178408997221},
  29. {29,1.42817408210585004403922210801221054461},
  30. {30,1.4220062317567129945412344765141001355},
  31. {31,1.41611917544533369254102429082160513084},
  32. {32,1.41049195445244880077344760779527345639},
  33. {33,1.4051057456479795593700060551553949446},
  34. {34,1.39994358928469090337863094895696596845},
  35. {35,1.39499015802841523529377394252379049117},
  36. {36,1.39023156004523883530353774388849366078},
  37. {37,1.38565517036913390218387987164360902442},
  38. {38,1.38124948587417599494888974270272683602},
  39. {39,1.37700400004464983512682767443775686515},
  40. {40,1.37290909442703835047823373266554024708},
  41. {41,1.36895594420004818247311699643331377899},
  42. {42,1.36513643574275156839766072988517148305},
  43. {43,1.36144309443977055373412981089021928638},
  44. {44,1.35786902125399984336720056851594591129},
  45. {45,1.35440783683543930473427573602498607559},
  46. {46,1.3510536321300155887091520853891945445},
  47. {47,1.34780092461321684380654451302931688756},
  48. {48,1.34464461940656131097555379617152740538},
  49. {49,1.34157997464560704966451256923364150444},
  50. {50,1.33860257056055295892954001842319703868}};
  51. out=NonlinearModelFit[data,a+b*x, {a,b}, x];
  52. Normal[out]
  53. Show[Plot[out[x],{x,0,52},PlotRange->{{0,54},{0,2.5}},AspectRatio->Automatic],ListPlot[data],ImageSize->1200]
复制代码
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
 楼主| 发表于 2016-7-6 12:23:34 | 显示全部楼层
  1. (*第一列乘以第二列,然后取代第二列的值*)
  2. Clear["Global`*"];(*Clear all variables*)
  3. data={{3,2.00286740806350017941198515943545931361},
  4. {4,1.9283589164749804283600849366519560639},
  5. {5,1.86400765000052485077735500471126683613},
  6. {6,1.81051438122816316692944707545183728148},
  7. {7,1.76578235001873982989444491931146614181},
  8. {8,1.72786431204913392367633970225491638819},
  9. {9,1.69526942444040933881970369205445408976},
  10. {10,1.66689198313550214392865903283543517526},
  11. {11,1.64190904616094851366101664075402220374},
  12. {12,1.6196998599244209974512574541584287912},
  13. {13,1.59978869842201869056004669676183358389},
  14. {14,1.58180517574496658723209695065387815411},
  15. {15,1.56545660481407284533740801524947779306},
  16. {16,1.55050851817644197976392356354789658309},
  17. {17,1.5367707372478112927676021766159055014},
  18. {18,1.52408725542126641935994748503824288176},
  19. {19,1.51232877977090028689582251305994783841},
  20. {20,1.50138715374182425550739674900868882105},
  21. {21,1.49117113037198696816232030789158595774},
  22. {22,1.481603128981051478805513257481434365},
  23. {23,1.47261671766567601608562219697005149641},
  24. {24,1.46415463821023838316030988522039252819},
  25. {25,1.45616724114422507854121331602445094798},
  26. {26,1.4486112343465122080296824494356282472},
  27. {27,1.44144867381000076000046233040898435983},
  28. {28,1.43464614322487864607990982178408997221},
  29. {29,1.42817408210585004403922210801221054461},
  30. {30,1.4220062317567129945412344765141001355},
  31. {31,1.41611917544533369254102429082160513084},
  32. {32,1.41049195445244880077344760779527345639},
  33. {33,1.4051057456479795593700060551553949446},
  34. {34,1.39994358928469090337863094895696596845},
  35. {35,1.39499015802841523529377394252379049117},
  36. {36,1.39023156004523883530353774388849366078},
  37. {37,1.38565517036913390218387987164360902442},
  38. {38,1.38124948587417599494888974270272683602},
  39. {39,1.37700400004464983512682767443775686515},
  40. {40,1.37290909442703835047823373266554024708},
  41. {41,1.36895594420004818247311699643331377899},
  42. {42,1.36513643574275156839766072988517148305},
  43. {43,1.36144309443977055373412981089021928638},
  44. {44,1.35786902125399984336720056851594591129},
  45. {45,1.35440783683543930473427573602498607559},
  46. {46,1.3510536321300155887091520853891945445},
  47. {47,1.34780092461321684380654451302931688756},
  48. {48,1.34464461940656131097555379617152740538},
  49. {49,1.34157997464560704966451256923364150444},
  50. {50,1.33860257056055295892954001842319703868}};
  51. (*第二列换成第一列与第二列的乘积*)
  52. data[[All, 2]]*=data[[All,1]];
  53. out=NonlinearModelFit[data,a+b*x^(1/2)+c*x, {a,b,c}, x];
  54. Normal[out]
  55. Show[Plot[out[x],{x,0,52},PlotRange->{{0,60},{0,70}},AspectRatio->Automatic],ListPlot[data],ImageSize->800]
  56. Print["拟合优度值是:"];
  57. out["RSquared"]
复制代码

拟合方程是:
-1.208100914943779516587560800750535150 + 2.42523844131677074670895367755557525 Sqrt[x] +  1.019655051803581738323392673297013864 x
拟合优度值是:
0.99999995799099676515334782556813032
QQ截图20160706122302.png
毋因群疑而阻独见  毋任己意而废人言
毋私小惠而伤大体  毋借公论以快私情
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