Tootfinder

Evolutionary Preference Sampling for Pareto Set Learning
Rongguang Ye, Longcan Chen, Jinyuan Zhang, Hisao Ishibuchi
https://arxiv.org/abs/2404.08414 https:…

Evolutionary Preference Sampling for Pareto Set Learning
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network. PSL employs preference vectors to scalarize multiple objectives, facilitating the learning of mappings from preference vectors to specific Pareto optimal solutions. Previous PSL methods have shown their effectiveness in solving artificial multi-objective optimization problems (MOPs) with uniform preference vector sampling. The quality of the learned Pareto set is influenced by the sa…

Pareto-Optimal Algorithms for Learning in Games
Eshwar Ram Arunachaleswaran, Natalie Collina, Jon Schneider
https://arxiv.org/abs/2402.09549 https://

Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
Yingrong Wang, Anpeng Wu, Haoxuan Li, Weiming Liu, Qiaowei Miao, Ruoxuan Xiong, Fei Wu, Kun Kuang
https://arxiv.org/abs/2403.02624

Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient's recovery (short-term) but could also result in severe long-term side effects. Although recent works have investigated the problems about short-term or long-term effects or the both, …

Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
Moise Blanchard
https://arxiv.org/abs/2404.06720 h…

Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
In this paper we provide oracle complexity lower bounds for finding a point in a given set using a memory-constrained algorithm that has access to a separation oracle. We assume that the set is contained within the unit $d$-dimensional ball and contains a ball of known radius $ε>0$. This setup is commonly referred to as the feasibility problem. We show that to solve feasibility problems with accuracy $ε\geq e^{-d^{o(1)}}$, any deterministic algorithm either uses $d^{1+δ}$ bits of memory or m…

#libjxl 0.10 is out, and now #JPEGXL beats #AVIF even more than before.
"""
The new version of libjxl brings a very substantial reduction in memory consumption, by an order of magnitude, for both lossy and lossless compression. Also the speed is improved, especially for multi-threaded lossless encoding where the default effort setting is now an order of magnitude faster.
This consolidates JPEG XL’s position as the best image codec currently available, for both lossless and lossy compression, across the quality range but in particular for high quality to visually lossless quality. It is Pareto-optimal across a wide range of speed settings.
"""
https://cloudinary.com/blog/jpeg-xl-and-the-pareto-front

A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms
Hao Wang, Angel E. Rodriguez-Fernandez, Lourdes Uribe, Andr\'e Deutz, Oziel Cort\'es-Pi\~na, Oliver Sch\"utze
https://arxiv.org/abs/2405.05721

A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms
A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very efficient in finding good Pareto front approximations, they may need quite a few resources or may even fail to obtain optimal or nearly approximations. Hereby, optimality is implicitly defined by the chosen performance indicator. In this work, we pro…

Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings
Phuong Dinh Mai, Duc-Trong Le, Tuan-Anh Hoang, Dung D. Le
https://arxiv.org/abs/2402.14305

Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings
In this paper, we tackle the problem of computing a sequence of rankings with the guarantee of the Pareto-optimal balance between (1) maximizing the utility of the consumers and (2) minimizing unfairness between producers of the items. Such a multi-objective optimization problem is typically solved using a combination of a scalarization method and linear programming on bi-stochastic matrices, representing the distribution of possible rankings of items. However, the above-mentioned approach reli…

This https://arxiv.org/abs/2209.03919 has been replaced.
link: https://scholar.google.com/scholar?q=a

Bi-objective Ranking and Selection Using Stochastic Kriging
We consider bi-objective ranking and selection problems, where the goal is to correctly identify the Pareto optimal solutions among a finite set of candidates for which the two objective outcomes have been observed with uncertainty (e.g., after running a multiobjective stochastic simulation optimization procedure). When identifying these solutions, the noise perturbing the observed performance may lead to two types of errors: solutions that are truly Pareto-optimal can be wrongly considered dom…

Homotopy methods for higher order shape optimization: A globalized shape-Newton method and Pareto-front tracing
A. Cesarano, B. Endtmayer, P. Gangl
https://arxiv.org/abs/2405.03421

Homotopy methods for higher order shape optimization: A globalized shape-Newton method and Pareto-front tracing
First order shape optimization methods, in general, require a large number of iterations until they reach a locally optimal design. While higher order methods can significantly reduce the number of iterations, they exhibit only local convergence properties, necessitating a sufficiently close initial guess. In this work, we present an unregularized shape-Newton method and combine shape optimization with homotopy (or continuation) methods in order to allow for the use of higher order methods even…

Priority-Neutral Matching Lattices Are Not Distributive
Clayton Thomas
https://arxiv.org/abs/2404.02142 https://arxiv.org/pdf/2404.02…

Priority-Neutral Matching Lattices Are Not Distributive
Stable matchings are a cornerstone of market design, with numerous practical deployments backed by a rich, theoretically-tractable structure. However, in school-choice problems, stable matchings are not Pareto optimal for the students. Priority-neutral matchings, introduced by Reny (AER, 2022), generalizes the set of stable matchings by allowing for certain priority violations, and there is always a Pareto optimal priority-neutral matching. Moreover, like stable matchings, the set of priority-n…

This https://arxiv.org/abs/2310.04176 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approximating the set of Nash equilibria for convex games
In Feinstein and Rudloff (2023), it was shown that the set of Nash equilibria for any non-cooperative $N$ player game coincides with the set of Pareto optimal points of a certain vector optimization problem with non-convex ordering cone. To avoid dealing with a non-convex ordering cone, an equivalent characterization of the set of Nash equilibria as the intersection of the Pareto optimal points of $N$ multi-objective problems (i.e.\ with the natural ordering cone) is proven. So far, algorithms …

Risk exchange under infinite-mean Pareto models
Yuyu Chen, Paul Embrechts, Ruodu Wang
https://arxiv.org/abs/2403.20171 https://arxiv.…

Risk exchange under infinite-mean Pareto models
We study the optimal decisions of agents who aim to minimize their risks by allocating their positions over extremely heavy-tailed (i.e., infinite-mean) and possibly dependent losses. The loss distributions of our focus are super-Pareto distributions which include the class of extremely heavy-tailed Pareto distributions. For a portfolio of super-Pareto losses, non-diversification is preferred by decision makers equipped with well-defined and monotone risk measures. The phenomenon that diversifi…

This https://arxiv.org/abs/2310.02669 has been replaced.
initial toot: https://mastoxiv.page/@a…

Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP
The safety factor profile is a key property in determining the stability of tokamak plasmas. To design the safety factor profile in the United Kingdom's proposed Spherical Tokamak for Energy Production (STEP), we apply multi-objective Bayesian optimisation to design electron-cyclotron heating profiles. Bayesian optimisation is an iterative machine learning technique that uses an uncertainty-aware predictive model to choose the next designs to evaluate based on the data gathered during optimisat…

This https://arxiv.org/abs/2302.01815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Optimal Capacity Modification for Many-To-One Matching Problems
We consider many-to-one matching problems, where one side consists of students and the other side of schools with capacity constraints. We study how to optimally increase the capacities of the schools so as to obtain a stable and perfect matching (i.e., every student is matched) or a matching that is stable and Pareto-efficient for the students. We consider two common optimality criteria, one aiming to minimize the sum of capacity increases of all schools (abbrv. as MinSum) and the other aiming…

Obtaining properly Pareto optimal solutions of multiobjective optimization problems via the branch and bound method
Weitian Wu, Xinmin Yang
https://arxiv.org/abs/2402.18015

Obtaining properly Pareto optimal solutions of multiobjective optimization problems via the branch and bound method
In multiobjective optimization, most branch and bound algorithms provide the decision maker with the whole Pareto front, and then decision maker could select a single solution finally. However, if the number of objectives is large, the number of candidate solutions may be also large, and it may be difficult for the decision maker to select the most interesting solution. As we argue in this paper, the most interesting solutions are the ones whose trade-offs are bounded. These solutions are usual…

Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning
Yuchao Liao, Tosiron Adegbija, Roman Lysecky, Ravi Tandon
https://arxiv.org/abs/2404.14754

Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning
High-Level Synthesis (HLS) Design Space Exploration (DSE) is a widely accepted approach for efficiently exploring Pareto-optimal and optimal hardware solutions during the HLS process. Several HLS benchmarks and datasets are available for the research community to evaluate their methodologies. Unfortunately, these resources are limited and may not be sufficient for complex, multi-component system-level explorations. Generating new data using existing HLS benchmarks can be cumbersome, given the e…

This https://arxiv.org/abs/2310.20426 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evolutionary Pareto Set Learning with Structure Constraints
The multiobjective evolutionary optimization algorithm (MOEA) is a powerful approach for tackling multiobjective optimization problems (MOPs), which can find a finite set of approximate Pareto solutions in a single run. However, under mild regularity conditions, the Pareto optimal set of a continuous MOP could be a low dimensional continuous manifold that contains infinite solutions. In addition, structure constraints on the whole optimal solution set, which characterize the patterns shared amo…

This https://arxiv.org/abs/2302.01815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Optimal Capacity Modification for Many-To-One Matching Problems
We consider many-to-one matching problems, where one side consists of students and the other side of schools with capacity constraints. We study how to optimally increase the capacities of the schools so as to obtain a stable and perfect matching (i.e., every student is matched) or a matching that is stable and Pareto-efficient for the students. We consider two common optimality criteria, one aiming to minimize the sum of capacity increases of all schools (abbrv. as MinSum) and the other aiming…

This https://arxiv.org/abs/2401.14593 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Estimation of Pareto's Scale Parameter from Grouped Data
Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods like least squares, minimum Hellinger distance, and optimal bounded influence function available. This paper introduces a novel robust estimation technique, the Method of…

Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, Qingfu Zhang
https://arxiv.org/abs/2402.19078

Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent different optimal trade-offs among the objectives for a given problem. However, these existing methods could have high computational complexity or may not have good theoretical properties for solving a general differentia…

Optimal Budget Aggregation with Single-Peaked Preferences
Felix Brandt, Matthias Greger, Erel Segal-Halevi, Warut Suksompong
https://arxiv.org/abs/2402.15904

Optimal Budget Aggregation with Single-Peaked Preferences
We study the problem of aggregating distributions, such as budget proposals, into a collective distribution. An ideal aggregation mechanism would be Pareto efficient, strategyproof, and fair. Most previous work assumes that agents evaluate budgets according to the $\ell_1$ distance to their ideal budget. We investigate and compare different models from the larger class of star-shaped utility functions - a multi-dimensional generalization of single-peaked preferences. For the case of two alterna…

Tracing Pareto-optimal points for multi-objective shape optimization applied to electric machines
Alessio Cesarano, Peter Gangl
https://arxiv.org/abs/2404.12205

Tracing Pareto-optimal points for multi-objective shape optimization applied to electric machines
In the context of the optimization of rotating electric machines, many different objective functions are of interest and considering this during the optimization is of crucial importance. While evolutionary algorithms can provide a Pareto front straightforwardly and are widely used in this context, derivative-based optimization algorithms can be computationally more efficient. In this case, a Pareto front can be obtained by performing several optimization runs with different weights. In this wo…

This https://arxiv.org/abs/2310.20426 has been replaced.
link: https://scholar.google.com/scholar?q=a

Dealing with Structure Constraints in Evolutionary Pareto Set Learning
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this wo…

This https://arxiv.org/abs/2310.20426 has been replaced.
link: https://scholar.google.com/scholar?q=a

Dealing with Structure Constraints in Evolutionary Pareto Set Learning
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this wo…

This https://arxiv.org/abs/2102.10154 has been replaced.
link: https://scholar.google.com/scholar?q=a

Truncated, Censored, and Actuarial Payment-type Moments for Robust Fitting of a Single-parameter Pareto Distribution
With some regularity conditions maximum likelihood estimators (MLEs) always produce asymptotically optimal (in the sense of consistency, efficiency, sufficiency, and unbiasedness) estimators. But in general, the MLEs lead to non-robust statistical inference, for example, pricing models and risk measures. Actuarial claim severity is continuous, right-skewed, and frequently heavy-tailed. The data sets that such models are usually fitted to contain outliers that are difficult to identify and separ…

This https://arxiv.org/abs/2310.04176 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approximating the set of Nash equilibria for convex games
In Feinstein and Rudloff (2023), it was shown that the set of Nash equilibria for any non-cooperative $N$ player game coincides with the set of Pareto optimal points of a certain vector optimization problem with non-convex ordering cone. To avoid dealing with a non-convex ordering cone, an equivalent characterization of the set of Nash equilibria as the intersection of the Pareto optimal points of $N$ multi-objective problems (i.e.\ with the natural ordering cone) is proven. So far, algorithms …

This https://arxiv.org/abs/2401.14593 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Estimation of Pareto's Scale Parameter from Grouped Data
Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods like least squares, minimum Hellinger distance, and optimal bounded influence function available. This paper introduces a novel robust estimation technique, the Method of…

This https://arxiv.org/abs/2401.14593 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Estimation of Pareto's Scale Parameter from Grouped Data
Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods like least squares, minimum Hellinger distance, and optimal bounded influence function available. This paper introduces a novel robust estimation technique, the Method of…