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45
Sequential Quadratic Programming
, 1995
"... this paper we examine the underlying ideas of the SQP method and the theory that establishes it as a framework from which effective algorithms can ..."
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Cited by 144 (4 self)
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this paper we examine the underlying ideas of the SQP method and the theory that establishes it as a framework from which effective algorithms can
A robust gradient sampling algorithm for nonsmooth, nonconvex optimization
 SIAM Journal on Optimization
"... Let f be a continuous function on R n, and suppose f is continuously differentiable on an open dense subset. Such functions arise in many applications, and very often minimizers are points at which f is not differentiable. Of particular interest is the case where f is not convex, and perhaps not eve ..."
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Cited by 75 (22 self)
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Let f be a continuous function on R n, and suppose f is continuously differentiable on an open dense subset. Such functions arise in many applications, and very often minimizers are points at which f is not differentiable. Of particular interest is the case where f is not convex, and perhaps not even locally Lipschitz, but whose gradient is easily computed where it is defined. We present a practical, robust algorithm to locally minimize such functions, based on gradient sampling. No subgradient information is required by the algorithm. When f is locally Lipschitz and has bounded level sets, and the sampling radius ǫ is fixed, we show that, with probability one, the algorithm generates a sequence with a cluster point that is Clarke ǫstationary. Furthermore, we show that if f has a unique Clarke stationary point ¯x, then the set of all cluster points generated by the algorithm converges to ¯x as ǫ is reduced to zero.
Convex Nondifferentiable Optimization: A Survey Focussed On The Analytic Center Cutting Plane Method.
, 1999
"... We present a survey of nondifferentiable optimization problems and methods with special focus on the analytic center cutting plane method. We propose a selfcontained convergence analysis, that uses the formalism of the theory of selfconcordant functions, but for the main results, we give direct pr ..."
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Cited by 69 (2 self)
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We present a survey of nondifferentiable optimization problems and methods with special focus on the analytic center cutting plane method. We propose a selfcontained convergence analysis, that uses the formalism of the theory of selfconcordant functions, but for the main results, we give direct proofs based on the properties of the logarithmic function. We also provide an in depth analysis of two extensions that are very relevant to practical problems: the case of multiple cuts and the case of deep cuts. We further examine extensions to problems including feasible sets partially described by an explicit barrier function, and to the case of nonlinear cuts. Finally, we review several implementation issues and discuss some applications.
Optimal ShortTerm Scheduling of LargeScale Power Systems
, 1983
"... This paper is concerned with the longstanding problem of optimal unit commitment in an electric power system. We follow the traditional formulation of this problem which gives rise to a largescale, dynamic, mixedinteger programming problem. We describe a solution methodology based on duality, Lagr ..."
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Cited by 46 (0 self)
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This paper is concerned with the longstanding problem of optimal unit commitment in an electric power system. We follow the traditional formulation of this problem which gives rise to a largescale, dynamic, mixedinteger programming problem. We describe a solution methodology based on duality, Lagrangian relaxation and nondifferentiable optimization that has two unique features. First, computational requirements typically grow only linearly witb the number of generating units. Second, the duality gap decreases in relative terms as the number of units increases, and as a result our algorithm tends to actually perform better for problems of large size. This allows for the first time consistently reliable solution of large practical problems involving several hundreds of units within realistic time constraints. Aside from the unit commilment problem. this methodology is applicable to a broad class of largescale dpamic scheduling and resource allocation problems involving integer variables.
A quasiNewton approach to nonsmooth convex optimization
 In ICML
, 2008
"... We extend the wellknown BFGS quasiNewton method and its limitedmemory variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by generalizing three components of BFGS to subdifferentials: The local quadratic model, the identification of a descent direc ..."
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Cited by 33 (2 self)
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We extend the wellknown BFGS quasiNewton method and its limitedmemory variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by generalizing three components of BFGS to subdifferentials: The local quadratic model, the identification of a descent direction, and the Wolfe line search conditions. We apply the resulting subLBFGS algorithm to L2regularized risk minimization with binary hinge loss, and its directionfinding component to L1regularized risk minimization with logistic loss. In both settings our generic algorithms perform comparable to or better than their counterparts in specialized stateoftheart solvers. 1.
Approximating Subdifferentials By Random Sampling Of Gradients
 MATHEMATICS OF OPERATIONS RESEARCH
, 2001
"... Many interesting real functions on Euclidean space are differentiable almost everywhere. All Lipschitz functions have this property, but so, for example, does the spectral abscissa of a matrix (a nonLipschitz function). In practice, the gradient is often easy to compute. We investigate to what exte ..."
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Cited by 27 (10 self)
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Many interesting real functions on Euclidean space are differentiable almost everywhere. All Lipschitz functions have this property, but so, for example, does the spectral abscissa of a matrix (a nonLipschitz function). In practice, the gradient is often easy to compute. We investigate to what extent we can approximate the Clarke subdifferential of such a function at some point by calculating the convex hull of some gradients sampled at random nearby points.
A Survey of Algorithms for Convex Multicommodity Flow Problems
, 1997
"... There are many problems related to the design of networks. Among them, the message routing problem plays a determinant role in the optimization of network performance. Much of the motivation for this work comes from this problem which is shown to belong to the class of nonlinear convex multicommodit ..."
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Cited by 22 (3 self)
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There are many problems related to the design of networks. Among them, the message routing problem plays a determinant role in the optimization of network performance. Much of the motivation for this work comes from this problem which is shown to belong to the class of nonlinear convex multicommodity flow problems. This paper emphasizes the message routing problem in data networks, but it includes a broader literature overview of convex multicommodity flow problems. We present and discuss the main solution techniques proposed for solving this class of largescale convex optimization problems. We conduct some numerical experiments on the message routing problem with some different techniques. 1 Introduction The literature dealing with multicommodity flow problems is rich since the publication of the works of Ford and Fulkerson's [19] and T.C. Hu [30] in the beginning of the 1960s. These problems usually have a very large number of variables and constraints and arise in a great variety o...