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@Book{Greenberg:Mathematical, 
 author = "H.J. Greenberg", 
 title = "Mathematical Programming Glossary", 
 publisher ="INFORMS Computing Society", 
 address="\url{http://glossary.computing.society.informs.org/}", 
 year = "1996--2006",
 annote="An extensive glossary of terms used in the mathematical programming literature."
}  

@TechReport{billups.lana.ea:clustering,
 author = "S. C. Billups and L. Lana and P. Gee",
 title = "Clustering Gene Expression Profiles Via Permutation Vectors",
 type = "CCM Technical Report",
 institution = "University of Colorado at Denver and Health Sciences Center",
 year = " 2001",
 note = "Available for download at \url{http:www-math.cudenver.edu/ccm/reports/180.pdf}",
 annote = "Presents an algorithm for clustering gene expression profiles based on using 
          permutation vectors."
}

@article{mifflin:semismooth,
  author    = "R. Mifflin",
  title     = "Semismooth and semiconvex functions in constrained
               optimization",
  journal   = "Siam Journal on Control",
  year      = "1977",
  volume    = "15",
  pages     = "957--972",
  annote    = "This is the first appearance in the literature of the
concept of a semismooth function.  Semismooth functions are closed
under addition and composition, and also guarantee the local
convergence of nonsmooth generalizations of Newton's method."  
}

@book{winter.periaux.ea:genetic,
    author    = {G. Winter and J. Periaux and M. Galan and P. Cuesta},
    title     = {Genetic Algorithms in Engineering and Computer
Science},
    publisher = {John Wiley and Sons},
    address   = {Chickester, England},
    year      = {1995},
    annote ={This book contains a general overview of
		  genetic algorithms as well as a few other topics
		  such as neural networks.  The book also talks about
		  many applications of genetic algorithms and how
		  researchers slightly changed from standard genetic
		  algorithms by using different crossover techniques
		  different mutations etc.  It also discusses using
		  Fuzzy logic in a genetic algorithm and gives
		  techniques to finding the best sample size and
		  mutation probability. } }

@techreport{savic.walters:genetic,
    author    = {D. A. Savic and G. A. Walters},
    title     = {Genetic Algorithm Techniques for Calibrating Network
Models},
    institution  = { University of Exeter},
    year      = {1995},
    number         = {95/12},

    annote = {Savic seems to have spent much time and
		            effort in using genetic algorithms in
		            water system design.  In this paper he
		            discusses genetic algorithms and gives a
		            brief overview, he discusses how he used a
		            genetic algorithm to calibrate a small
		            water system.  He talks about what he was
		            trying to find (c-factors and demands) and
		            how he used a genetic algorithm to do
		            this.}  }
		  
@InProceedings{anderson:primal,
  author    = "E. J. Anderson",
  title     = "A New Primal Algorithm for Semi-Infinite Linear
                 Programming",
  booktitle = "Proceedings of an International Symposium on Infinite
                 Dimensional Linear Programming, Cambridge, September
                 1984",
  year      = "1985",
  editor =       "E. J. Anderson and A. B. Philpott",
  publisher =    "Springer-Verlag",
  address =      "Berlin"
}

@phdthesis{dearaujo:statistically,
   author  = {Jose Vicente Granato {de Araujo}},
   title   = {A Statistically Based Procedure for Calibration of Water
              Distribution Systems},
   school  = {Oklahoma State University},
   year    = {1992},
   address = {Stillwater, Oklahoma},
   month   = {May},
   annote   = {This Ph.D. thesis discusses a statistically based
              calibration method for water distribution systems.  The
              author gives an in-depth analysis of the calibration 
              procedure discussing analytical methods, optimization methods,
              and uncertainty analysis for estimating demands and C-factors.
              A linear regression technique for estimating the C-factors is
              discussed.  Also, a procedure for transferring uncertainties in
              input data to the parameter estimation is explained.},
}


 @techreport{billups.watson:probability-one,
    author    = {S. C. Billups and L. T. Watson},
    title     = {A probability-one homotopy algorithm for nonsmooth equations and mixed complementarity problems},
    institution = {University of Colorado at Denver},
    year      = {2000},
    month     = {September},
    address   = {Denver, Colorado},
    type      = {UCD/CCM Report No.},		     
    number    = {165},
    annote    = "{This paper extends the probability-one homotopy algorithm of Chow-Yorke and Li, which solves $C^2$ systems of equations.  The resulting algorithm is capable of solving semismooth systems of equations.  The basis of the algorithm is to "smooth" the nonsmooth system of equations using a smoothing parameter that is a function of the homotopy parameter.
 }"
}


@Proceedings{conn.scheinberg.ea:recent,
     author = {A. R. Conn and K. Scheinberg and Ph. L. Toint},
     title  = {Recent progress in unconstrained nonlinear optimization without derivatives},
     booktitle  = {Mathematical Programming},
     year       = {1997},
     editor     = {Thomas M. Liebling and Dominique de Werra},
     publisher  = {Mathematical Programming Society},
     address    = {North-Holland}
}