Perimeter constraint
The perimeter stays fixed, so every width choice forces one matching height.
Concept module
Move one rectangle width under a fixed perimeter, watch the area curve peak, and use the local slope to see why the square is the best constrained shape.
The simulation shows one rectangle under a fixed 24 meter perimeter. Dragging the top-right corner changes the width and automatically adjusts the height. A smaller objective graph shows the matching point on the area curve, and an optional square overlay marks the best-area case. Moving the width updates the rectangle dimensions, the area readout, and the local slope of the objective together so the learner can compare the constrained shape and the turning point on one compact bench. With perimeter 24 m, the rectangle is 3.4 m by 8.6 m for area 29.24 m^2. The local area slope is 5.2. The area curve is still rising here, so making the rectangle a little wider would increase the area. The rectangle is narrower than the best-area square, so it still has room to gain area by trading a little height for width.
Interactive lab
Optimization under constraints
Drag the top-right corner or the objective point to change the width.
Controls
Move the width while the fixed 24 meter perimeter forces the matching height.
Presets
Predict -> manipulate -> observe
Keep the active prompt next to the controls so each change has an immediate visible consequence.
Graphs
Switch graph views without breaking the live stage and time link.
Objective graph
The constrained area function. The peak marks the best possible rectangle under the fixed perimeter.
Equation map
Select a symbol to highlight the matching control and the graph or overlay it most directly changes.
Moves the current rectangle width while the fixed perimeter forces the matching height, the objective point, and the local area slope to update together.
Equations in play
Choose an equation to sync the active symbol, control highlight, and related graph mapping.
More tools
Detailed noticing prompts, guided overlays, and challenge tasks stay available without taking over the main bench.
What to notice
Use the rectangle, the objective graph, and the slope graph as one bench.
Guided overlays
Focus one overlay at a time to see what it represents and what to notice in the live motion.
Overlay focus
Keep the fixed perimeter visible on the current rectangle.
What to notice
Why it matters
It keeps the optimization story honest by showing the tradeoff instead of hiding it behind symbols.
Challenge mode
Use the rectangle, the objective graph, and the slope readout together. The goal is to land the maximum for a reason, not just to hunt 36.
4 of 5 checks
Suggested start
The checklist updates from the live simulation state, active graph, overlays, inspect time, and compare setup.
Perimeter constraint
The perimeter stays fixed, so every width choice forces one matching height.
Objective function
Substitutes the fixed-perimeter constraint into the area formula so the whole tradeoff depends on one variable.
Local area slope
Tells whether making the rectangle a little wider would increase area, decrease area, or land at the turning point.
Progress
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Jump to a named bench state or copy the one you are looking at now. Shared links reopen the same controls, graph, overlays, and compare context.
Saved setups
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Stable links
Short explanation
Optimization becomes easier to trust when one constrained picture stays visible. This bench keeps one rectangle under a fixed 24 meter perimeter, lets you move only the width, and forces the height, the area, and the objective graph to respond together so maxima and minima stay tied to a real tradeoff instead of a detached worksheet trick.
The important idea is that width and height are not free to change independently. Every extra meter of width costs one meter of height because the perimeter is fixed. The area curve peaks when that tradeoff has stopped helping, and the local slope of the objective graph is the cleanest way to see that turning point.
Key ideas
Worked example
Live worked examples are available on Premium. You can still read the full frozen walkthrough on the free tier.
View plans3.4 m
8.6 m
1. Rewrite the perimeter constraint
2. Solve for the matching height
3. Compute the constrained area
Current constrained area
Common misconception
The maximum area must happen at the widest rectangle because a larger width always means a larger area.
A wider rectangle also has to become shorter when the perimeter is fixed, so width and height are trading against each other.
Past the best square, the lost height hurts the product more than the extra width helps it, so the area curve turns downward.
Mini challenge
Prediction prompt
Check your reasoning
Quick test
Graph reading
Question 1 of 3
Choose one answer to reveal feedback, then test the idea in the live system if a guided example is available.
Accessible description
The simulation shows one rectangle under a fixed 24 meter perimeter. Dragging the top-right corner changes the width and automatically adjusts the height. A smaller objective graph shows the matching point on the area curve, and an optional square overlay marks the best-area case.
Moving the width updates the rectangle dimensions, the area readout, and the local slope of the objective together so the learner can compare the constrained shape and the turning point on one compact bench.
Graph summary
The objective graph plots area against width for the fixed perimeter, so its peak marks the maximum-area rectangle. The local-slope graph plots A'(w), which crosses zero at the same width.
The constraint-tradeoff graph shows h = 12 - w, making the one-for-one exchange between width and height visible instead of hidden.
Keep the calculus branch connected
These suggestions come from the concept registry, so the reason label reflects either curated guidance or the fallback progression logic.
Move one upper bound across a source curve and watch signed area build into a running total so accumulation stays visual instead of symbolic.
Approach one target point from the left and right, compare the limiting height with the actual function value, and contrast continuous, removable, jump, and blow-up behavior on one honest graph.
Slide a point along one curve, tighten a secant into a tangent, and connect local steepness to the derivative graph without leaving the same live bench.