A-Level Mathematics: Calculating Gradient and Slope of Curves | A-Level数学：曲线梯度与斜率的计算

📚 A-Level Mathematics: Calculating Gradient and Slope of Curves | A-Level数学：曲线梯度与斜率的计算

The gradient of a curve at a point is one of the most fundamental ideas in A-Level Mathematics. It measures how steep a curve is at any given location and forms the gateway to differential calculus. Understanding how to calculate gradients of curves, as opposed to straight lines, requires the concept of a limit and leads directly to the derivative.

曲线在某点的梯度是A-Level数学中最基本的概念之一。它衡量曲线在任意给定位置的陡峭程度，也是进入微积分学的入门钥匙。理解如何计算曲线（而非直线）的梯度，需要引入极限的概念，并直接引出导数。

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1. The Concept of Gradient | 梯度的概念

= (y₂ − y₁) / (x₂ − x₁). This value remains the same no matter which two points we choose.

对于直线，梯度是恒定的。它定义为直线上任意两点之间的垂直变化量与水平变化量之比：m = (y₂ − y₁) / (x₂ − x₁)。无论我们选择哪两点，这个值都保持不变。

es, so it does not have a single gradient. Instead, we speak of the gradient at a particular point, which is represented by the gradient of the tangent line drawn at that point.

然而，对于曲线来说，梯度逐点变化。像 y = x² 这样的曲线随着 x 增大而变得越来越陡，因此它没有单一的梯度。我们转而讨论曲线在某个特定点的梯度，即该点处切线的梯度。

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2. Average Gradient over an Interval | 区间上的平均梯度

Before we can find the gradient at a single point, we first consider the average gradient over an interval. Given two points A(x₁, y₁) and B(x₂, y₂) on a curve, the line passing through them is called a secant. Its gradient is the average rate of change of y with respect to x over that interval.

在求某一点的梯度之前，我们先考虑一个区间上的平均梯度。给定曲线上的两点 A(x₁, y₁) 和 B(x₂, y₂)，经过这两点的直线称为割线。它的梯度就是 y 关于 x 在该区间内的平均变化率。

Average gradient = Δy / Δx = (y₂ − y₁) / (x₂ − x₁)

antity gives us a rough sense of how the curve behaves between the two points, but it does not capture the behaviour at any specific point.

其中 Δy = y₂ − y₁，Δx = x₂ − x₁。这个量让我们粗略了解曲线在两个点之间的行为，但它并不能反映任何一个具体点处的行为。

= 1, y = 1; at x = 3, y = 9. Hence the average gradient is (9 − 1) / (3 − 1) = 8 / 2 = 4.

例：对于曲线 y = x²，求 x = 1 到 x = 3 之间的平均梯度。当 x = 1 时，y = 1；当 x = 3 时，y = 9。因此平均梯度为 (9 − 1) / (3 − 1) = 8 / 2 = 4。

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3. The Limit Definition of the Derivative | 导数的极限定义

To obtain the gradient at a single point A, we let point B move closer and closer to A along the curve. As B approaches A, the secant AB approaches the tangent at A. The gradient of this tangent is the limit of the average gradient as Δx tends to zero.

为了得到某个点 A 处的梯度，我们让点 B 沿曲线逐渐靠近 A。当 B 趋近 A 时，割线 AB 趋近于 A 点处的切线。该切线的梯度就是平均梯度在 Δx 趋于零时的极限。

f'(x) = lim (h→0) [f(x + h) − f(x)] / h

Here h represents Δx, the small change in x, and f'(x) is called the derivative of f at x. This limit, when it exists, equals the gradient of the tangent at the point (x, f(x)).

这里的 h 代表 Δx，即 x 的微小变化量，而 f'(x) 称为 f 在 x 处的导数。当这个极限存在时，它等于点 (x, f(x)) 处切线的梯度。

ne pivots around the fixed point until it becomes the tangent line. This limiting process is the central idea of differentiation.

从几何上看，当 h → 0 时，割线围绕固定点旋转，直到变成切线。这个极限过程正是微分的核心思想。

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4. Differentiation from First Principles | 从第一性原理求导

les. Let us work through a classic example: differentiate f(x) = x² from first principles.

从第一性原理求导指的是直接运用极限定义，而不使用标准法则。我们来看一个经典例子：用第一性原理求 f(x) = x² 的导数。

We set up the limit: f' )² − x²] / h. Expanding (x + h)² gives x² + 2xh + h². Cancelling the x² terms, we obtain f') = lim (h→0) (2xh + h²) / h = lim (h→0) (2x + h). As h tends to zero, this approaches 2x.

我们建立极限：f'(x) = lim (h→0) [(x + h)² − x²] / h。展开 (x + h

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