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.

Mar 18, 2024 · We call a solution Pareto-optimal if any other solution in the feasible solution space doesn’t dominates it. In other words, it’s a solution that we can’t improve in one or more objectives without worsening other objectives. Based on the Pareto ranking, we identify the Pareto frontier solutions.

We propose a model in which a designer chooses an algorithm that maps observed inputs into decisions, and introduce a fairness-accuracy Pareto fron-tier. We identify how the algorithm's inputs govern the shape of this frontier, showing (for example) that access to group identity reduces the error for the worse-o group everywhere along the frontier.

In this paper we present an efficient and effective method of using surrogate approximations to explore the design space and capture the Pareto frontier during multiobjective optimization.

Oct 31, 2021 · Therefore, in this paper, we first develop application models for performance, error, and power, followed by formulation of an optimization problem to maximize system performance under error and power constraints, using three approximation knobs, which are loop truncation, data dropping, and computational precision scaling.

Jul 1, 2018 · This paper defines the new Pareto front, an extensive exploration of the error– energy trade-off, when combining arithmetic-level approximation techniques, and proposes 5 hybrid design families for approximate and energy-friendly hardware multipliers.

Aug 9, 2018 · Driven by the lack of a clear solution on the energy-error efficiency of existing approximate multiplication techniques, we present a new, efficient, and easily applied approximation design, as well as explore the current state-of-the-art design space.

To demonstrate the method, two mathematical example problems are presented. The results indicate that the proposed method is effective at capturing convex and concave Pareto frontiers even when discontinuities are present.

Pareto frontier. This criterion only generated three alternative solutions (see Table I for details) whereas GC criterion generated a larger set of alternative solutions. Figure 1 shows that GC criterion yielded a satisfactory representation of the Pareto frontier for this problem,

We pro-pose a pre-processing algorithm for fair data representation via which supervised learning results in estimations of the Pareto frontier between prediction error and statistical dispar-ity.