Constraint Programming Applied to Portfolio Design Problem

2020 ◽  
pp. 103-118
Keyword(s):  
Symmetry Breaking ◽  
Constraint Programming ◽  
Design Problem ◽  
Experimental Results ◽  
Complex Problems ◽  
Portfolio Design

This chapter introduces Constraint Programming (CP) approaches for solving efficiently a ðnancial portfolio design problem. The CP includes powerful techniques for modeling and solving complex problems. Symmetry breaking coming firstly from CP has proved its efficiency in minimizing CPU times when the problem is symmetric. The authors have adopted CP techniques to model the problem in a constraints system to capitalize on the flexibility of the CP paradigm and to take into consideration the symmetric aspect of the problem. The authors propose different CP models and different hybridizations of symmetry breaking techniques to tackle the problem. Experimental results on non-trivial instances of the problem show the effectiveness of the CP approach.

Resolution Methods

2020 ◽  
pp. 29-60
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Symmetry Breaking ◽  
Constraint Programming ◽  
Design Problem ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Performance Profiles ◽  
Portfolio Design

The aim of this chapter is to introduce the different notions of the techniques used to solve the portfolio design problem. These techniques can be divided into two exact (or complete) methods and approached (or incomplete) methods. In the first part, the authors provide the exact approaches, namely linear programming and constraint programming, as well as the techniques of symmetry breaking, the modeling notions, and the different solving algorithms. The second part concerns approached methods, namely Simulated Annealing, IDWalk, Tabu Search, GWW, and Variable Neighborhood Search, including the techniques of studying the performance profiles of a method.

scholarly journals IMPROVING THE AUTOMATIC PROCUREMENT OF WEB SERVICES USING CONSTRAINT PROGRAMMING

2005 ◽  
Vol 14 (04) ◽  
pp. 439-467 ◽  
Author(s):  
ANTONIO RUIZ–CORTÉS ◽  
OCTAVIO MARTÍN–DÍAZ ◽  
AMADOR DURÁN ◽  
M. TORO
Keyword(s):  
Web Services ◽  
Internal Consistency ◽  
Constraint Programming ◽  
Experimental Results ◽  
Objective Functions ◽  
Run Time ◽  
Linear Objective Functions ◽  
Best Offer

Software solutions to automate the procurement of web services are gaining importance when technology evolves, the number of providers increases and the needs of the clients become more complex. There are several proposals in this field, but they all have important drawbacks, namely: many of them are not able to check offers and demands for internal consistency; selecting the best offer usually relies on evaluating linear objective functions, which is quite a naive solution; the language to express offers is usually less expressive than the language to express demands; and, last but not least, providers cannot impose constraints on their clients. In this article, we present a solution to overcome these problems that relies on constraint programming; furthermore, we present a run-time framework, some experimental results, and a comparison with other proposals.

Multi-Vehicle Missions

2008 ◽  
pp. 1-22
Author(s):  
Johan Baltié ◽  
Eric Bensana ◽  
Patrick Fabiani ◽  
Jean-Loup Farges ◽  
Stéphane Millet ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Constraint Programming ◽  
Management System ◽  
Experimental Results ◽  
Application Domain ◽  
Research Directions ◽  
Air Defense ◽  
Mission Management ◽  
Unmanned Combat Air Vehicles ◽  
Air Vehicles

This chapter deals with the issues associated with the autonomy of vehicle fleets, as well as some of the dimensions provided by an Artificial Intelligence (AI) solution. This presentation is developed using the example of a suppression of enemy air defense mission carried out by a group of Unmanned Combat Air Vehicles (UCAV). The environment of the Mission Management System (MMS) includes the theatre of operations, vehicle sub-systems and the MMS of other UCAV. An MMS architecture, organized around a database, including reactive and deliberative layers is described in detail. The deliberative layer includes a distributed mission planner developed using constraint programming and an agent framework. Experimental results demonstrate that the MMS is able, in a bounded time, to carry out missions, to activate the contingent behaviors, to decide whether to plan or not. Some research directions remain open in this application domain of AI.

Geometric Simulation for Thick Origami

2019 ◽  
Author(s):  
Tsz-Ho Kwok
Keyword(s):  
Design Problem ◽  
Three Dimensional ◽  
Experimental Results ◽  
Engineering Applications ◽  
Research Attention ◽  
3D Shape ◽  
Mountain Valley ◽  
Geometry Design ◽  
Geometric Simulation ◽  
Geometric Formulation

Abstract Origami is an art that creates a three-dimensional (3D) shape only by folding. This capability has drawn much research attention recently, and its applied or inspired designs are utilized in various engineering applications. Most current designs are based on the existing origami patterns and their known deformation, but origami patterns are universally designed for zero-thickness like a paper. To extend the designs for engineering applications, simulation of origami is needed to help designers explore and understand the designs, and the simulation must take the material thickness into account. With the observation that origami is mainly a geometry design problem, this paper develops a geometric simulation for thick origami, similar to a pseudo-physics approach. The actuation, constraints, and mountain/valley assignments of origami are also incorporated in the geometric formulation. Experimental results show that the proposed method is efficient and accurate. It can simulate successfully the bistable property of a waterbomb base, two different action origami, and the elasticity of origami panels when they are not rigid.

A multi-attribute service portfolio design problem

2011 ◽  
Author(s):  
Rico Knapper ◽  
Christoph M. Flath ◽  
Benjamin Blau ◽  
Anca Sailer ◽  
Christof Weinhardt
Keyword(s):  
Design Problem ◽  
Service Portfolio ◽  
Portfolio Design

scholarly journals A Smart Microcontroller Architecture for the Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1821 ◽  
Author(s):  
Zhenyu Wu ◽  
Kai Qiu ◽  
Jianguo Zhang
Keyword(s):  
Internet Of Things ◽  
Real World ◽  
Simulation Experiment ◽  
Cloud Model ◽  
Experimental Results ◽  
The Internet ◽  
Complex Problems ◽  
Logic Control ◽  
The Internet Of Things ◽  
Embedded Intelligence

The interoperations of endpoint devices are generally achieved by gateways in Internet of Things (IoT) systems. However, the gateways mainly focus on networking communication, which is lack of data logic control capabilities. The microcontrollers with embedded intelligence could work as an intermediate device to help the interconnections of the endpoint devices. Moreover, they could help control the endpoint devices. In this paper, a microcontroller architecture with intelligent and scalable characteristics is proposed. The intelligence means that the microcontroller could control the target endpoint devices by its logical circuits, and the scalability means that the microcontroller architecture could be easily extended to deal with more complex problems. Two real world industrial implementations of the proposed architecture are introduced. The implementations show that the microcontroller is important to provide the intelligent services to users in IoT systems. Furthermore, a simulation experiment based on the cloud model is designed to evaluate the proposed method. The experimental results demonstrate the effectiveness of the proposed architecture.

MULTI-LAYERED GREEDY NETWORK-GROWING ALGORITHM: EXTENSION OF GREEDY NETWORK-GROWING ALGORITHM TO MULTI-LAYERED NETWORKS

2004 ◽  
Vol 14 (01) ◽  
pp. 9-26 ◽  
Author(s):  
RYOTARO KAMIMURA
Keyword(s):  
Classification Problem ◽  
Medical Data ◽  
Experimental Results ◽  
Detection Problem ◽  
Complex Problems ◽  
Layered Networks ◽  
Data Problem ◽  
Information Maximization ◽  
Directed Learning ◽  
Present Algorithm

In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.

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