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Determinant maximization with linear matrix inequality constraints
 SIAM Journal on Matrix Analysis and Applications
, 1998
"... constraints ..."
Pattern Separation Via Ellipsoids and Conic Programming
, 1998
"... this document. The first chapter is about mathematical programming. We will start by describing how and why researchers were led to study special types of mathematical programs, namely convex programs and conic programs. We will also provide a detailed discussion about conic duality and give a class ..."
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Cited by 9 (0 self)
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this document. The first chapter is about mathematical programming. We will start by describing how and why researchers were led to study special types of mathematical programs, namely convex programs and conic programs. We will also provide a detailed discussion about conic duality and give a classification of conic programs. We will then describe what are selfscaled cones and why they are so useful in conic programming. Finally, we will give an overview of what can be modelled using a SQL conic program, keeping in mind our pattern separation problem. Since most of the material in the chapter is standard, many of the proofs are omitted. The second chapter will concentrate on pattern separation. After a short description of the problem, we will successively describe four different separation methods using SQL conic programming. For each method, various properties are investigated. Each algorithm has in fact been successively designed with the objective of eliminating the drawbacks of the previous one, CONTENTS 3 while keeping its good properties. We conclude this chapter with a small section describing the state of the art in pattern separation with ellipsoids. The third chapter reports some computational experiments with our four methods, and provides a comparison with other separation procedures. Finally, we conclude this work by providing a short summary, highlighting the author's personal contribution and giving some interesting perspectives for further research. Chapter 1 Conic programming 1.1 Introduction
Applications of Semidefinite Programming
, 1998
"... A wide variety of nonlinear convex optimization problems can be cast as problems involving linear matrix inequalities (LMIs), and hence efficiently solved using recently developed interiorpoint methods. In this paper, we will consider two classes of optimization problems with LMI constraints: ffl ..."
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A wide variety of nonlinear convex optimization problems can be cast as problems involving linear matrix inequalities (LMIs), and hence efficiently solved using recently developed interiorpoint methods. In this paper, we will consider two classes of optimization problems with LMI constraints: ffl The semidefinite programming problem, i.e., the problem of minimizing a linear function subject to a linear matrix inequality. Semidefinite programming is an important numerical tool for analysis and synthesis in systems and control theory. It has also been recognized in combinatorial optimization as a valuable technique for obtaining bounds on the solution of NPhard problems.
Connections Between SemiInfinite and Semidefinite Programming
"... We consider convex optimization problems with linear matrix inequality (LMI) constraints, i.e., constraints of the form F (x) =F0+x1F1+ +xmFm 0; (1.1) where the matrices Fi = F T ..."
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Cited by 5 (2 self)
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We consider convex optimization problems with linear matrix inequality (LMI) constraints, i.e., constraints of the form F (x) =F0+x1F1+ +xmFm 0; (1.1) where the matrices Fi = F T
Correspondence should be addressed to Stanislav Uryasev
, 2002
"... The paper considers a general approach for classifying objects using mathematical programming algorithms. The approach is based on optimizing a utility function, which is quadratic in indicator parameters and is linear in control parameters (which need to be identified). Qualitative characteristics ..."
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The paper considers a general approach for classifying objects using mathematical programming algorithms. The approach is based on optimizing a utility function, which is quadratic in indicator parameters and is linear in control parameters (which need to be identified). Qualitative characteristics of the utility function, such as monotonicity in some variables, are included using additional constraints. The methodology was tested with a "credit cards scoring" problem. Credit scoring is a way of separating specific subgroups in a population of objects (such as applications for credit), which have significantly different credit risk characteristics. A new feature of our approach is incorporating expert judgments in the model. For instance, the following preference was included with an additional constraint: “give more preference to customers with higher incomes. ” Numerical experiments showed that including constraints based on expert judgments improves the performance of the algorithm. Introduction. In this paper, we consider a general approach for classifying objects and explain it with credit cards scoring problem. Classification can be defined by a classification function assigning to each object some categorical value called the class number. However, this classification
SEMIDEFINITE PROGRAMMING*
"... Abstract. In sernidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of synunetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefin ..."
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Abstract. In sernidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of synunetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefinite programming unifies several standard problems (e.g., linear and quadratic programming) and finds many applications in engineering and combinatorial optimization. Although semidefinite programs are much more general than linear programs, they are not much harder to solve. Most interiorpoint methods for linear programming have been generalized to semidefinite programs. As in linear programming, these methods have polynomial worstcase complexity and perform very well in practice. This paper gives a survey of the theory and applications of semidefinite programs and an introduction to primaldual interiorpoint methods for their solution. Key words, semidefinite programming, convex optimization, interiorpoint methods, eigenvalue optimization, combinatorial optimization, system and control theory AMS subject classifications. 65K05, 49M45, 93B51, 90C25, 90C27, 90C90, 15A18 1. Introduction. 1.1. Semidefinite programming. We consider the problem of minimizing a linear function
Approximation of nDimensional Data Using Spherical and Ellipsoidal Primitives
"... Abstract—This paper discusses the problem of approximating data points indimensional Euclidean space using spherical and ellipsoidal surfaces. A closed form solution is provided for spherical approximation, while an efficient, globally optimal solution for the ellipsoidal problem is proposed in ter ..."
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Abstract—This paper discusses the problem of approximating data points indimensional Euclidean space using spherical and ellipsoidal surfaces. A closed form solution is provided for spherical approximation, while an efficient, globally optimal solution for the ellipsoidal problem is proposed in terms of semidefinite programming (SDP). In addition, the paper presents a result for robust fitting in presence of outliers, and illustrates the theory with several numerical examples. A brief survey is also presented on the solutions to other relevant geometric approximation problems, such as ellipsoidal covering of convex hulls and pattern separation. Index Terms—Geometric approximation, semidefinite programming, spherical and ellipsoidal fitting, outliers. I.