Implement Gradient Descent for a 1D Quadratic

Optimization

Easy

Implement vanilla gradient descent to minimize a 1-D quadratic:

f(x) = ax^2 + bx + c

Loading visualization...

Examples

Input: a = 1, b = -4, c = 3, x0 = 0, lr = 0.1, steps = 50

Output: ≈ 2.0

Input: a = 0.5, b = -1, c = 0, x0 = -5, lr = 0.2, steps = 100

Output: ≈ 1.0

Hint 1

Use a loop to update x repeatedly.

Requirements

Use the update: $x=x−lr⋅f′(x)x=x−lr⋅f′(x)$ repeated steps times (where lr is the learning rate)
Do not use the closed-form minimizer during updates
Return a Python float (not list/array)
Assume a > 0, lr > 0, steps >= 1

Constraints

Time limit: 200 ms; Memory: 64 MB
Pure Python / NumPy (no ML libs)

Try Similar Problems

Adam Optimizer Rmsprop Optimizer Nesterov Momentum Adamw Optimizer Gradient Clipping

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Implement Gradient Descent for a 1D Quadratic

Optimization

Easy

Implement vanilla gradient descent to minimize a 1-D quadratic:

f(x) = ax^2 + bx + c

Loading visualization...

Examples

Input: a = 1, b = -4, c = 3, x0 = 0, lr = 0.1, steps = 50

Output: ≈ 2.0

Input: a = 0.5, b = -1, c = 0, x0 = -5, lr = 0.2, steps = 100

Output: ≈ 1.0

Hint 1

Use a loop to update x repeatedly.

Requirements

Use the update: $x=x−lr⋅f′(x)x=x−lr⋅f′(x)$ repeated steps times (where lr is the learning rate)
Do not use the closed-form minimizer during updates
Return a Python float (not list/array)
Assume a > 0, lr > 0, steps >= 1

Constraints

Time limit: 200 ms; Memory: 64 MB
Pure Python / NumPy (no ML libs)

Try Similar Problems

Adam Optimizer Rmsprop Optimizer Nesterov Momentum Adamw Optimizer Gradient Clipping

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a =

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b =

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c =

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x0 =

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lr =

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steps =

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