Easy

L2 Regularization Penalty

Easy

~10 min

code completion

L2 Regularization (Weight Decay)

L2 regularization discourages large weights by adding a penalty term to the loss:

L_{reg} = L_{task} + λ i \sum w_{i}^{2}

where $λ$ (lambda) controls regularization strength. This is equivalent to weight decay — weights are nudged toward zero at each step.

Effect: L2 spreads weight values smoothly. In contrast, L1 encourages exactly-zero weights (sparse solutions).

Your task:

Implement l2_penalty(weights, lambda_reg) that returns the L2 penalty term $λ \sum_{i} w_{i}^{2}$ .

Example Tests

Standard case

Input: {"weights":[1,2,3],"lambda_reg":0.1}

Expected: 1.4

Zero weights: penalty = 0

Input: {"weights":[0,0,0],"lambda_reg":1}

Expected: 0

Sign does not matter: squares remove it

Input: {"weights":[-1,1,-1,1],"lambda_reg":0.5}

Expected: 2

You can read the full problem statement above. Create a free account to run code in the browser, submit solutions, and track your progress.