For current students facing Chapter 10 for the first time: the frustration is real, but so is the triumph. The chapter’s exercises are not busywork; they are mental push-ups. And when you finally solve that related rates problem — the one with the rotating lighthouse and the moving ship — you will have earned not just a correct answer, but a new way of seeing change itself.
Moreover, the chapter introduces — problem-solving strategies. For optimization, students are taught: 1) Draw a diagram. 2) Identify the quantity to be optimized. 3) Express it in terms of one variable. 4) Differentiate. 5) Test critical points. This recipe-like clarity is comforting to students who find pure mathematics intimidating. For current students facing Chapter 10 for the
Yet the chapter does not coddle. The difficulty ramps up sharply. By the last set of problems, students face (e.g., $x^3 + y^3 = 6xy$, the folium of Descartes) and must find tangents, normals, and extreme values without explicitly solving for $y$. This prepares them for higher-level courses like differential equations and multivariable calculus. A Cultural Touchstone In the Philippines, Feliciano and Uy is more than a textbook — it’s a cultural artifact. Chapter 10, in particular, is where study groups form, where tutors earn their keep, and where many students first encounter the satisfying click of a difficult word problem solved correctly. The shared trauma of “the ladder problem” or “the box problem” creates camaraderie. 3) Express it in terms of one variable
Additionally, the chapter provides scant attention to or sensitivity analysis — topics that would benefit engineering students today. And the examples, while clear, are almost exclusively deterministic; there is no discussion of error propagation or uncertainty quantification using differentials, though the “approximations” section touches on it lightly. Conclusion: The Unfinished Journey Chapter 10 of Differential and Integral Calculus by Feliciano and Uy is more than a collection of problems on tangents, optimization, and related rates. It is a rite of passage — a demanding, patient, and ultimately rewarding bridge from the rules of differentiation to the art of applying them. Its legacy is visible in every Filipino engineer who can quickly sketch a curve, size a container for maximum volume, or compute how fast a shadow lengthens as a streetlight walks away. in Chapter 4). Instead
Chapter 10, typically titled or “Further Applications of the Derivative” (depending on the edition), is where the abstract machinery of limits, slopes, and derivatives transforms into a toolkit for solving real-world problems. This feature explores the chapter’s structure, its signature problems, the pedagogical philosophy behind it, and why it continues to challenge and inspire students today. The Bridge from Theory to Practice By the time a student reaches Chapter 10, they have survived the foundational gauntlet: limits (Chapter 1), continuity (Chapter 2), derivatives of algebraic functions (Chapters 3–5), trigonometric, logarithmic, and exponential functions (Chapters 6–8), and implicit differentiation (Chapter 9). They can compute $dy/dx$ in their sleep. But Chapter 10 asks a disarming question: Now that you can differentiate anything, what is it good for?
The section is particularly feared. One problem might involve a lighthouse rotating its beam, a moving ship, and the speed of the light spot along a shore — requiring implicit differentiation with respect to time, the chain rule, and trigonometric substitution all at once. Pedagogical Philosophy: Why It Works Chapter 10 exemplifies a teaching philosophy that prioritizes application over abstraction . Feliciano and Uy were writing for students who would become practitioners — civil engineers calculating beam deflections, electrical engineers analyzing rates of change in circuits, business students finding break-even points. The chapter does not spend pages proving the Mean Value Theorem (that appears earlier, in Chapter 4). Instead, it shows how to use derivatives to solve a concrete problem.
Furthermore, the chapter’s emphasis on — “What does the sign of the second derivative tell you about the shape of the profit curve?” — cultivates critical thinking that software cannot replace. Criticisms and Limitations No chapter is perfect. Some educators argue that Feliciano and Uy’s Chapter 10 focuses too heavily on geometric and physical applications (ladders, cones, boxes) at the expense of modern applications like marginal analysis in machine learning (gradient descent), or rates of change in biological systems (population dynamics, enzyme kinetics). The problems, while classic, can feel dated. A 2024 student might roll their eyes at “a conical tank filling with water” but find “a social media post going viral” as a related rates problem more engaging.