Search results for " optimization."

Prices and Pareto optima

2006

We provide necessary conditions for Pareto optimum in economies where tastes or technologies may be nonconvex, nonsmooth, and affected by externalities. Firms can pursue own objectives, much like the consumers. Infinite-dimensional commodity spaces are accommodated. Public goods and material balances are accounted for as special instances of linear restrictions.

Optimal Energy Management in Smart-Grid

2017

In this chapter, the problem of energy management in smart-grids is outlined. Optimized energy management is here considered as the operation of energy and power flow control in the aim of attaining minimum cost or minimum power losses while meeting technical constraints. Of course, according to the type of energy system in which such operation is carried out, the meaningful variables and objectives in the problem may largely change. As the extension of the system increases, the influence of the physical behaviour of the electrical power lines takes a more important role. Power electronics takes instead an increasing influence, as the dimension of the power system decreases although Kirchho…

OnMLM: An Online Formulation for the Minimal Learning Machine

2019

Minimal Learning Machine (MLM) is a nonlinear learning algorithm designed to work on both classification and regression tasks. In its original formulation, MLM builds a linear mapping between distance matrices in the input and output spaces using the Ordinary Least Squares (OLS) algorithm. Although the OLS algorithm is a very efficient choice, when it comes to applications in big data and streams of data, online learning is more scalable and thus applicable. In that regard, our objective of this work is to propose an online version of the MLM. The Online Minimal Learning Machine (OnMLM), a new MLM-based formulation capable of online and incremental learning. The achievements of OnMLM in our…

Approximations and Metric Regularity in Mathematical Programming in Banach Space

1993

This paper establishes verifiable conditions ensuring the important notion of metric regularity for general nondifferentiable programming problems in Banach spaces. These conditions are used to obtain Lagrange-Kuhn-Tucker multipliers for minimization problems with infinitely many inequality and equality constraints.

A Nondifferentiable Optimization Approach to Ratio-Cut Partitioning

2003

We propose a new method for finding the minimum ratio-cut of a graph. Ratio-cut is NP-hard problem for which the best previously known algorithm gives an O(log n)-factor approximation by solving its dually related maximum concurrent flow problem.We formulate the minimum ratio-cut as a certain nondifferentiable optimization problem, and show that the global minimum of the optimization problem is equal to the minimum ratio-cut. Moreover, we provide strong symbolic computation based evidence that any strict local minimum gives an approximation by a factor of 2. We also give an efficient heuristic algorithm for finding a local minimum of the proposed optimization problem based on standard nondi…

An improved iterative nonlinear least square approximation method for the design of measurement-based wideband mobile radio channel simulators

2011

This paper deals with the design of measurement-based simulation models for wideband single-input single-output (SISO) mobile radio channels. We present an improved version of the iterative nonlinear least square approximation (INLSA) method for computing the parameters of measurement-based simulation models. The proposed method aims to fit the temporal-frequency correlation function (TFCF) of the simulation model to that of the measured channel. Unlike the original INLSA method, the proposed approach provides a unique optimal set of estimated model parameters. The proposed iterative procedure involves numerical optimization techniques to determine a set of parameters that minimizes the Euc…

Mixed integer optimal compensation: Decompositions and mean-field approximations

2012

Mixed integer optimal compensation deals with optimizing integer- and real-valued control variables to compensate disturbances in dynamic systems. The mixed integer nature of controls might be a cause of intractability for instances of larger dimensions. To tackle this issue, we propose a decomposition method which turns the original n-dimensional problem into n independent scalar problems of lot sizing form. Each scalar problem is then reformulated as a shortest path one and solved through linear programming over a receding horizon. This last reformulation step mirrors a standard procedure in mixed integer programming. We apply the decomposition method to a mean-field coupled multi-agent s…

2014

This paper deals with the problem of robust model predictive control (RMPC) for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI) constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.

Mean‐Variance Portfolio Optimization

2010

On the definition of viscosity solutions for parabolic equations

2001

In this short note we suggest a refinement for the definition of viscosity solutions for parabolic equations. The new version of the definition is equivalent to the usual one and it better adapts to the properties of parabolic equations. The basic idea is to determine the admissibility of a test function based on its behavior prior to the given moment of time and ignore what happens at times after that.