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Topic: Greedy Algorithm Problem / Level: advanced
Problem:
Design a Greedy algorithm to allocate limited advertising slots on a popular streaming service where each ad has varying viewer engagement and potential revenue. Maximize total ad revenue while minimizing viewer dissatisfaction.
More Problems
You are tasked with scheduling the release of high-demand digital products in a tech company where each product has different development timelines, market demand, and profitability. Maximize total revenue while minimizing production delays.
Design a Greedy algorithm to allocate limited bandwidth across telecommunications networks in a densely populated area where each network has varying user demands and data traffic. Maximize network efficiency while minimizing bandwidth congestion.
You are given a set of scientific experiments where each experiment has different resource needs, project timelines, and potential discoveries. Implement a Greedy algorithm to maximize the number of experiments conducted while minimizing resource wastage.
Design a Greedy algorithm to allocate limited research funding to university departments where each department has varying success probabilities, project timelines, and societal impact. Maximize total research output while minimizing financial risk.
You are tasked with scheduling the repair of high-traffic roads in a city where each road has varying traffic volumes, repair costs, and repair durations. Maximize the number of roads repaired while minimizing traffic disruptions.
Design a Greedy algorithm to allocate limited resources to social welfare programs where each program has varying public benefits, costs, and impact. Maximize total social welfare while minimizing budget overruns.
You are given a set of jobs in a distributed computing environment where each job has different memory, processing, and network requirements. Implement a Greedy algorithm to maximize total job completion while minimizing resource conflicts.
Design a Greedy algorithm to allocate limited advertising slots on a popular online platform where each ad has varying viewer engagement potential and revenue. Maximize total ad revenue while minimizing user dissatisfaction.
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