AsymptoticIntegrate[f,x,xx0]
计算不定积分
的渐近逼近,x 以 x0 为中心.
AsymptoticIntegrate[f,{x,a,b},αα0]
计算不定积分
的渐近逼近,α 以 α0 为中心.
AsymptoticIntegrate[f,…,{ξ,ξ0,n}]
计算 n 阶渐近逼近.
AsymptoticIntegrate
AsymptoticIntegrate[f,x,xx0]
计算不定积分
的渐近逼近,x 以 x0 为中心.
AsymptoticIntegrate[f,{x,a,b},αα0]
计算不定积分
的渐近逼近,α 以 α0 为中心.
AsymptoticIntegrate[f,…,{ξ,ξ0,n}]
计算 n 阶渐近逼近.
更多信息和选项
- 积分的渐近逼近亦被称为渐近展开式和摄动展开式. 其中有些展开式也可以用特定的方法来计算,如拉普拉斯方法、稳相法和最速下降法等.
- 渐近逼近通常用于求解无法找到精确解的问题,或者为计算、比较和解释寻求更简单的答案.
- AsymptoticIntegrate[f,…,xx0] 计算 f 的积分的渐近展开式中的首项. 使用 SeriesTermGoal 指定更多项.
- 如果精确结果为 g[x],在 x0 处的 n 阶渐近逼近为 gn[x],那么当 xx0 时,结果为 AsymptoticLess[g[x]-gn[x],gn[x]-gn-1[x],xx0] 或 g[x]-gn[x]∈o[gn[x]-gn-1[x]].
- 渐近逼近 gn[x] 常以和 gn[x]
αkϕk[x] 的形式给出,其中 {ϕ1[x],…,ϕn[x]} 是当 xx0 时的渐近尺度 ϕ1[x]≻ϕ2[x]≻⋯>ϕn[x]. 当 xx0 时,结果为 AsymptoticLess[g[x]-gn[x],ϕn[x],xx0] 或 g[x]-gn[x]∈o[ϕn[x]]. - 常见的渐近尺度包括:
-

Taylor 尺度,当 xx0 时 
Laurent 尺度,当 xx0 时 
Laurent 尺度,当 x±∞ 时 
Puiseux 尺度,当 xx0 时 - 用于表示渐近逼近的尺度是从问题中自动推断出来的,通常可以包含更多的奇异尺度.
- 中心点 x0 可以为任意有限或无限大实数及复数.
- 阶数 n 必须为一个正整数,指定渐近解的近似阶数. 与多项式的次数无关.
- 可以给出以下选项:
-
AccuracyGoal Automatic 所求绝对精度的位数 Assumptions $Assumptions 对参数的设定 GenerateConditions Automatic 是否给出与参数的条件有关的答案 GeneratedParameters None 怎样命名生成的参数 Method Automatic 所用的方法 PerformanceGoal $PerformanceGoal 优化的目标 PrecisionGoal Automatic 所求精度的位数 SeriesTermGoal Automatic 近似的项数 WorkingPrecision Automatic 内部计算使用的精度 - PerformanceGoal 的可能设置包括 $PerformanceGoal、"Quality" 和 "Speed". 当设置为 "Quality" 时,AsymptoticIntegrate 通常可以解出更多的问题或者产生更简单的结果,但是可能会耗费更多的时间和内存.
范例
打开所有单元 关闭所有单元基本范例 (3)
AsymptoticIntegrate[Sin[t x], {t, 0, 1}, x -> 0]AsymptoticIntegrate[E ^ (t x), {t, 0, 1}, {x, 0, 7}]Integrate[E ^ (x t), {t, 0, 1}]Series[%, {x, 0, 7}]//Normalf[x_] := E^-ω (x^2 - 2 x)Plot[f[x] /. {ω -> 3}, {x, 0, 7}, PlotRange -> All, Filling -> Axis]AsymptoticIntegrate[f[x], {x, 0, ∞}, {ω, ∞, 1}]{% /. {ω -> 300.}, NIntegrate[Exp[-300 (x^2 - 2 x)], {x, 0, ∞}]}范围 (21)
不定积分 (3)
AsymptoticIntegrate[Sin[x], x, {x, 0, 8}]与 Integrate 给出的结果相比较:
Integrate[Sin[x], x]Series[%, {x, 0, 8}]//NormalAsymptoticIntegrate[x^x^x, x, {x, 0, 4}]% /. {x -> 0.0005}与 NIntegrate 给出的结果相比较:
NIntegrate[x ^ x ^ x, {x, 0, 0.0005}]AsymptoticIntegrate[E ^ (-m Sin[x]), x, {m, 0, 3}]指数积分 (6)
AsymptoticIntegrate[Exp[ -x t] / (1 + t), {t, 0, Infinity}, {x, Infinity, 7}]与 LaplaceTransform 给出的结果相比较:
LaplaceTransform[1 / (1 + t), t, x]Series[%, {x, Infinity, 7}]//Normal//Simplify//Expandf[t_] := E^-x (t^2 + t) (1 + t)^5 / 2Plot[f[t] /. {x -> 3.}, {t, -3, 2}, PlotRange -> All, Filling -> Axis]AsymptoticIntegrate[f[t], {t, 0, 2}, {x, Infinity, 1}]% /. {x -> 3000.}NIntegrate[Exp[-3000(t ^ 2 + t )] (1 + t) ^ (5 / 2), {t, 0, 2}]f[t_] := Exp[-x t ^ 2] / (t + 2)Plot[f[t] /. {x -> 30.}, {t, -2, 2}, PlotRange -> All, Filling -> Axis]AsymptoticIntegrate[f[t], {t, 0, 2}, {x, Infinity, 7}]% /. {x -> 300.}NIntegrate[Exp[ -300 t ^ 2] / (t + 2), {t, 0, 2}]f[t_] := Exp[-x(t + (1 / t))] / (t + 1)Plot[f[t] /. {x -> 3.}, {t, 1, 2}, Filling -> Axis]AsymptoticIntegrate[f[t], {t, 1, 2}, {x, Infinity, 1}]% /. {x -> 3.}NIntegrate[Exp[-3(t + (1 / t))] / (t + 1), {t, 1, 2}]f[t_] := Exp[-x (t ^ 2 - 2t)] / (t + 1)Plot[f[t] /. {x -> 3.}, {t, 0, 3}, Filling -> Axis]AsymptoticIntegrate[f[t], {t, 0, 3}, {x, Infinity, 1}]% /. {x -> 3000.}NIntegrate[Exp[-3000(t ^ 2 - 2t)] / (t ^ 2 + 1), {t, 0, 3}]f[t_] := Exp[x Sin[t]]Plot[f[t] /. {x -> 3.}, {t, 0, Pi}, Filling -> Axis]AsymptoticIntegrate[f[t], {t, 0, Pi}, {x, Infinity, 1}]% /. {x -> 3000.}NIntegrate[Exp[3000Sin[t]], {t, 0, Pi}]振荡积分 (4)
AsymptoticIntegrate[Exp[ I x t] / (1 + t ^ 2), {t, 0, Infinity}, {x, Infinity, 7}]f[x_] := E ^ (I ω Sin[t])Plot[Re[f[x] /. {ω -> 50}], {t, 0, π}, Filling -> Axis]int = AsymptoticIntegrate[f[t], {t, 0, π}, {ω, ∞, 1}]NIntegrate[E ^ (I 500 Sin[t]), {t, 0, π}]//Quietint /. {ω -> 500.}求含有 Sin 的实振荡积分的首项近似:
f[t_] := Sin[ x t ^ 3] / Sqrt[t + 1]Plot[f[t] /. {x -> 30.}, {t, 0, π / 2}, Filling -> Axis, PlotRange -> All]int = AsymptoticIntegrate[f[t], {t, 0, π / 2}, {x, ∞, 1}]NIntegrate[Sin[ 300 t ^ 3] / Sqrt[t + 1], {t, 0, π / 2}]int /. {x -> 300.}求含有 Cos 的实振荡积分的近似展开式:
f[t_] := Cos[ x t] / (1 + E ^ (t ^ 2))Plot[f[t] /. {x -> 30.}, {t, 1, 2}, Filling -> Axis, PlotRange -> All]int = AsymptoticIntegrate[f[t], {t, 1, 2}, {x, ∞, 2}]//SimplifyNIntegrate[Cos[ 300 t] / (1 + E ^ t ^ 2), {t, 1, 2}]int /. {x -> 300.}普通定积分 (4)
AsymptoticIntegrate[1 / (t ^ 2 + x ^ 2), {t, 1, 3}, {x, 0, 4}]与 Integrate 给出的结果相比较:
Integrate[1 / (t ^ 2 + x ^ 2), {t, 1, 3}, Assumptions -> x > 0]Series[%, {x, 0, 4}]//NormalAsymptoticIntegrate[Sin[t + x] ^ 5, {t, 0, π}, {x, π / 2, 3}]与 Integrate 给出的结果相比较:
Integrate[Sin[t + x] ^ 5, {t, 0, π}]Collect[Series[%, {x, π / 2, 3}]//Normal, x]AsymptoticIntegrate[1 / Sqrt[1 - m * Sin[θ] ^ 2], {θ, 0, Pi / 2}, {m, 0, 5}]与 Integrate 给出的结果相比较:
Integrate[1 / Sqrt[1 - m * Sin[θ] ^ 2], {θ, 0, Pi / 2}, Assumptions -> 0 < m < 1]Series[%, {m, 0, 4}]//Normal积分在 x=-Infinity 处的渐近展开式:
AsymptoticIntegrate[E ^ (x t) / (1 + t ^ 2), {t, 0, Infinity}, {x, -Infinity, 6}]% /. {x -> -30.}NIntegrate[E ^ (-30 t) / (1 + t ^ 2), {t, 0, Infinity}]变换积分 (4)
f[t_] := 1 / (1 + t)Plot[f[t], {t, 0, 4}, Filling -> Axis]AsymptoticIntegrate[f[t] E ^ (-s t), {t, 0, Infinity}, {s, Infinity, 5}]与使用 LaplaceTransform 得到的精确结果的级数展开式相比较:
LaplaceTransform[f[t], t, s]Series[%, {s, Infinity, 5}]//Normalf[t_] := t / (t ^ 3 + 1)Plot[f[t], {t, 0, 4}, Exclusions -> None, Filling -> Axis]AsymptoticIntegrate[f[t] t ^ (s - 1), {t, 0, Infinity}, {s, 0, 2}]与使用 MellinTransform 得到的精确结果的级数展开式相比较:
MellinTransform[f[t], t, s]Series[%, {s, 0, 2}]//Normalf[t_] := E ^ (-t ^ 2)Sqrt[2 / Pi] AsymptoticIntegrate[f[t] Cos[s t], {t, 0, Infinity}, {s, 0, 2}]//Expand与使用 FourierCosTransform 得到的精确结果的级数展开式相比较:
FourierCosTransform[f[t], t, s]Series[%, {s, 0, 2}]//Normalf[t_] := E ^ (-t ^ 2 + t)afs = Sqrt[2 / Pi] AsymptoticIntegrate[f[t] Sin[s t], {t, 0, Infinity}, {s, 0, 2}]//FullSimplify与使用 FourierSinTransform 得到的精确结果的级数展开式相比较:
FourierSinTransform[f[t], t, s]Series[%, {s, 0, 2}, Assumptions -> s > 0]//Normal//FullSimplify% - afs//FullSimplify选项 (1)
应用 (7)
int = AsymptoticIntegrate[1 / Sqrt[1 - ϵ t ^ 14], {t, 0, 1}, {ϵ, 0, 2}]int /. {ϵ -> 0.3}NIntegrate[1 / Sqrt[1 - 0.3 t ^ 14], {t, 0, 1}]AsymptoticIntegrate[1 / Sqrt[1 - ϵ t ^ 14], {t, 0, 1}, {ϵ, 0, 5}]% /. {ϵ -> 0.3}Plot[Sin[a x] ^ 2 Cos[x] ^ 4 /. {a -> 1.05}, {x, -π, π}, Filling -> Axis]AsymptoticIntegrate[Sin[a x] ^ 2 Cos[x] ^ 4, {x, -π, π}, {a, 1, 4}]% /. {a -> 1.05}AsymptoticIntegrate[π(Sin[a x] ^ 2) ^ 2, {x, 0, π}, {a, 3, 4}]% /. {a -> 3.1}RevolutionPlot3D[Sin[3.1 x] ^ 2, {x, 0, π}, RevolutionAxis -> "X"]定义渐近版本的 LaplaceTransform:
asymLaplaceTransform[f_, t_, s_, n_] := AsymptoticIntegrate[f E ^ (-s t), {t, 0, ∞}, {s, ∞, n}]f[t_] := 1 / (3 + Sin[t] ^ 3)Plot[f[t], {t, 0, 20}, PlotRange -> All, Filling -> Axis]lap = asymLaplaceTransform[f[t], t, s, 8]Plot[lap, {s, 100, 1000}]Table[NIntegrate[f[t] E ^ (-s t), {t, 0, ∞}], {s, 100, 1000, 200}]Table[lap, {s, 100, 1000, 200}]//N计算域为
的 ProbabilityDistribution 的渐近均值:
dist = ProbabilityDistribution[1 / (1 + ϵ (x - 2 / 3) ^ 2), {x, 0, 1}, Assumptions -> ϵ > 0];归一化 PDF:
norm = Integrate[1 / (1 + ϵ (x - 1 / 3) ^ 2), {x, 0, 1}, Assumptions -> ϵ > 0]Plot[PDF[dist, x] / norm /. {ϵ -> 1 / 20}, {x, 0, 1}, Filling -> Axis]asympMean[dist_, ϵ_, n_] := AsymptoticIntegrate[x Refine[PDF[dist, x], 0 < x < 1], {x, 0, 1}, {ϵ, 0, n}]asympMean[dist, ϵ, 3]Mean[dist]Series[%, {ϵ, 0, 3}, Assumptions -> ϵ > 0]//Normal获取 BesselJ 的积分表示的渐近展开式:
besselint = ((2 ^ (1 - ν) * z ^ ν) / (Sqrt[Pi] * Gamma[ν + 1 / 2]))AsymptoticIntegrate[(1 - t ^ 2) ^ (ν - 1 / 2) * Cos[z * t], {t, 0, 1}, {z, 0, 2}, Assumptions -> ν > 0]Table[besselint /. {ν -> 2}, {z, 0.1, 0.5, 0.1}]Table[BesselJ[2, z], {z, 0.1, 0.5, 0.1}]f[t_] := Sin[t] E ^ (I x (t ^ 3 + 3 t))ReImPlot[f[t] /. {x -> 30}, {t, -2, 2}, PlotLegends -> "ReIm"]AsymptoticIntegrate[f[t], {t, -2, 2}, {x, ∞, 4}]% /. {x -> 30.}NIntegrate[Sin[t] E ^ (I 30 (t ^ 3 + 3 t)), {t, -2, 2}]属性和关系 (4)
AsymptoticIntegrate 按给定阶数计算积分:
AsymptoticIntegrate[Sin[x], x, {x, 0, 6}]Series[-Cos[x] - %, {x, 0, 6}]用 Integrate 算出解析形式的积分:
AsymptoticIntegrate[Sin[x t] ^ 2, {t, 0, 1}, {x, 0, 8}]Integrate[Sin[x t] ^ 2, {t, 0, 1}]Series[%, {x, 0, 8}]//Normal用 NIntegrate 计算数值近似:
AsymptoticIntegrate[E ^ (-ω t ^ 2)(t + 5) ^ (1 / 2), {t, 0, 5}, {ω, ∞, 3}]% /. {ω -> 300.}NIntegrate[E ^ (-300 t ^ 2)(t + 5) ^ (1 / 2), {t, 0, 5}]用 AsymptoticExpectation 计算渐近期望:
dist = UniformDistribution[{-1, 2}];AsymptoticExpectation[E ^ (a x), xdist, {a, 0, 3}]用 AsymptoticIntegrate 获取同样的结果:
AsymptoticIntegrate[E ^ (a x) PDF[dist, x], {x, -∞, ∞}, {a, 0, 3}]文本
Wolfram Research (2018),AsymptoticIntegrate,Wolfram 语言函数,https://reference.wolfram.com/language/ref/AsymptoticIntegrate.html (更新于 2020 年).
CMS
Wolfram 语言. 2018. "AsymptoticIntegrate." Wolfram 语言与系统参考资料中心. Wolfram Research. 最新版本 2020. https://reference.wolfram.com/language/ref/AsymptoticIntegrate.html.
APA
Wolfram 语言. (2018). AsymptoticIntegrate. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/AsymptoticIntegrate.html 年
BibTeX
@misc{reference.wolfram_2026_asymptoticintegrate, author="Wolfram Research", title="{AsymptoticIntegrate}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/AsymptoticIntegrate.html}", note=[Accessed: 11-August-2026]}
BibLaTeX
@online{reference.wolfram_2026_asymptoticintegrate, organization={Wolfram Research}, title={AsymptoticIntegrate}, year={2020}, url={https://reference.wolfram.com/language/ref/AsymptoticIntegrate.html}, note=[Accessed: 11-August-2026]}