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  2. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.

  3. Pareto principle - Wikipedia

    en.wikipedia.org/wiki/Pareto_principle

    The Pareto principle may apply to fundraising, i.e. 20% of the donors contributing towards 80% of the total. The Pareto principle (also known as the 80/20 rule, the law of the vital few and the principle of factor sparsity [1] [2]) states that for many outcomes, roughly 80% of consequences come from 20% of causes (the "vital few"). [1]

  4. Pareto front - Wikipedia

    en.wikipedia.org/wiki/Pareto_front

    In multi-objective optimization, the Pareto front (also called Pareto frontier or Pareto curve) is the set of all Pareto efficient solutions. [1] The concept is widely used in engineering. [2] : 111–148 It allows the designer to restrict attention to the set of efficient choices, and to make tradeoffs within this set, rather than considering ...

  5. Pareto chart - Wikipedia

    en.wikipedia.org/wiki/Pareto_chart

    Pareto chart. A Pareto chart is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The chart is named for the Pareto principle, which, in turn, derives its name from Vilfredo Pareto, a noted Italian economist.

  6. Proximal policy optimization - Wikipedia

    en.wikipedia.org/wiki/Proximal_Policy_Optimization

    t. e. Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default reinforcement learning algorithm at American artificial intelligence company OpenAI. [2]

  7. Scale-free network - Wikipedia

    en.wikipedia.org/wiki/Scale-free_network

    A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P ( k) of nodes in the network having k connections to other nodes goes for large values of k as. where is a parameter whose value is typically in the range (wherein the second moment ( scale parameter) of is infinite ...

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