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Topic: Greedy Algorithm Problem / Level: advanced
Problem:
Design a Greedy algorithm to allocate limited computing resources in a data center where each task has varying memory, CPU, and execution requirements. Maximize total task completion while minimizing power usage.
More Problems
You are tasked with scheduling repairs for critical infrastructure in a city where each project has varying costs, repair durations, and public benefits. Design a Greedy algorithm to maximize the number of repairs completed while minimizing public disruptions.
Design a Greedy algorithm to allocate limited research grants to scientific projects where each project has different risks, potential breakthroughs, and funding requirements. Maximize total research output while minimizing financial risk.
You are given a set of tasks in a distributed computing system where each task has different execution times, memory requirements, and processing needs. Implement a Greedy algorithm to maximize total task completion while minimizing resource conflicts.
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.
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.
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