Imagine being a thief with taste.Every item has a weight and a value.Your bag has a hard limit.Your mission: maximize value without breaking your back.That’s the 0/1 Knapsack Problem.You either take the item (1) or leave it (0).No cutting gold bars in half to cheat the system.Dynamic Programming cracks it by:• Splitting the problem into tiny sub-decisions• Saving results in a 2D table• Reusing those results instead of recomputingEvery cell asks a simple but brilliant question:The formula for the optimal choice:B[i][j] = max( B[i−1][j], V[i] + B[i−1][ j−W[i] ] )Take it if it improves your value.Skip it if it weighs you down.We fill the table bottom-up, then trace back the best loot.We reconstruct exactly what the thief walks out with — mathematically optimal crime.It’s not just for burglars:• Cargo loading• Budget optimization• Portfolio selection• Memory constraints in softwareKnapsack teaches a powerful truth:Optimization is just saying no to the wrong things.🎓 Episode powered by:📘 Kill All Bugs: Learn Software Testing in 1 Day → https://testingin1day.com🎙️ Hear this and more: https://testingin1day.com/podcastBuild smarter. Carry wisely. Maximize everywhere.

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