This paper is about learning a continuous approximation of the Pareto frontier in Multi–Objective Markov Decision Problems (MOMDPs). We propose a policy–based approach that exploits gradient information to generate solutions close to the Pareto ones. Differently from previous policy–gradient multi–objective algorithms, where n optimization routines are use to have n solutions, our approach perf...

Pareto optimization solves a constrained optimization task by reformulating the task as a bi-objective problem. Pareto optimization has been shown quite effective in applications; however, it has little theoretical support. This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimiza...

Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circu...

The Pareto distribution, or power-law distribution, has long been used to model phenomena in many fields, including wildfire sizes, earthquake seismic moments and stock price changes. Recent observations have brought the fit of the Pareto into question, however, particularly in the upper tail where it often overestimates the frequency of the largest events. This paper proposes a graphical self-...

Dynamic Efficiency and Pareto Optimality in a Stochastic OLG Model with Production and Social Security We analyze the interaction between risk sharing and capital accumulation in a stochastic OLG model with production. We give a complete characterization of interim Pareto optimality. Our characterization also subsumes equilibria with a PAYG social security system. In a competitive equilibrium i...

designer is solving an optimization problem, which involves different disciplines and where all design criteria have to be optimized simultaneously. Mathematically this problem can be reduced to a vector optimization problem. The solution of this problem is not unique and is represented by a Pareto surface in the space of the objective functions. Once a Pareto solution is obtained, it may be ve...

This vignette is designed to give a short overview about Pareto Distributions and Generalized Pareto Distributions (GPD). We will work with the SPC.we data of our quantmod vignette. Therefore we have to reproduce the SPC.we data in exactly the same way as described the quantmod vignette. In financial data analysis stock indices as the S&P 500 index are typically analyzed by using the returns of...

Patterns are the key building blocks in the logical analysis of data (LAD). It has been observed in empirical studies and practical applications that some patterns are more “suitable” than others for use in LAD. In this paper, we model various such suitability criteria as partial preorders defined on the set of patterns. We introduce three such preferences, and describe patterns which are Paret...

The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. In this paper, we design a genetic algorithm for this purpose. We compose multiple...

We give an efficient algorithm to enumerate all elements of a Pareto front in a multi-objective optimization problem in which the space of values is finite for all objectives. Our algorithm uses a feasibility check for a search space element as an oracle and minimizes the number of oracle calls that are necessary to identify the Pareto front of the problem. Given a k-dimensional search space in...