Olvi L. Mangasarian

John von Neumann Professor Emeritus of Mathematics and Computer Sciences
Research Scientist Department of Mathematics, University of California, San Diego

Computer Sciences Department
University of Wisconsin
1210 W. Dayton St.
Madison, WI 53706-1685

Email: olvi at cs dot wisc dot edu

Interests: Optimization, data mining, classification & support vector machines

Currently not taking on post docs, interns or graduate students.

The research described here is supported by the National Science Foundation through grants CCR-0138308 and IIS-0511905, and the Data Mining Institute .

Talks Since 1998

Olvi Mangasarian: Unsupervised Classification via Convex Absolute Value Inequalities (PDF)
Center for Computational Mathematics Seminar
University of California at San Diego January 17, 2017.

Olvi Mangasarian & Glenn Fung: The Disputed Federalist Papers: Resolution via Support Vector Machine Feature Selection (PowerPoint)
Center for Computational Mathematics Seminar
University of California at San Diego January 21, 2014.

Olvi Mangasarian & Glenn Fung: Privacy Preserving Approximation (PowerPoint)
Center for Computational Mathematics Seminar
University of California at San Diego January 24, 2012.

Olvi Mangasarian: Privacy-Preserving Linear Programming (PowerPoint)
Center for Computational Mathematics Seminar
University of California at San Diego January 11, 2011.

Olvi Mangasarian & Glenn Fung: Proximal Support Vector Machine Classifiication (PowerPoint)
Center for Computational Mathematics Seminar
University of California at San Diego January 26, 2010.

Olvi Mangasarian & Edward Wild: Privacy-Preserving Support Vector Machine Classification via Random Kernels (PowerPoint)
International Symposium on Mathematical Programming 2009.
Chicago August 23-28, 2009.

Olvi Mangasarian & Edward Wild: Privacy-Preserving Support Vector Machines via Random Kernels (PowerPoint)
DMIN'08 The 4th International Conference on Data Mining
Las Vegas, Nevada July 14-17, 2008 DMIN08 Best Academic Research Paper Award.

Olvi Mangasarian & Edward Wild: Privacy-Preserving Support Vector Machine Classification via Random Kernels (PowerPoint)
Artificial Intelligence Research Talk
Computer Sciences Department, UW Madison February 20, 2008
Center for Computational Mathematics Seminar, Mathematics Department
University of California at San Diego, La Jolla April 7, 2009

Olvi Mangasarian & Edward Wild: Exact Differentiable Exterior Penalty for Linear Programming (PowerPoint)
Center for Computational Mathematics Seminar, Mathematics Department
University of California at San Diego, La Jolla February 12, 2008

Olvi Mangasarian & Edward Wild: Feature Selection in Nonlinear Kernel Classification (PowerPoint)
IEEE International Conference on Data Mining
Omaha, Nebraska
October 28, 2007

Olvi Mangasarian & Edward Wild: Knowledge-Based Breast Cancer Prognosis (PowerPoint) (pdf)
Computation & Informatics in Biology and Medicine (CIBM) Retreat
Pyle Center, Madison, Wisconsin
October 13, 2006

Olvi Mangasarian & Edward Wild: Nonlinear Knowledge in Kernel Machines (PowerPoint)
Data Mining and Mathematical Programming Workshop, University of Montreal, Quebec, Canada, October 10-13, 2006
Computational & Applied Mathematics Seminar, Mathematics Department
University of California at San Diego, La Jolla April 24, 2007

Olvi Mangasarian & Edward Wild: Feature Selection in Nonlinear Kernel Classification (PowerPoint)
Artificial Intelligence Seminar, University of Wisconsin, Madison, September 19, 2006

Olvi Mangasarian: Absolute Value Equation Solution via Concave Minimization (PowerPoint)
Computational & Applied Mathematics Seminar, Mathematics Department
University of California at San Diego, La Jolla April 11, 2006

Olvi Mangasarian & Edward Wild: Nonlinear Knowledge in Kernel Approximation (PowerPoint)
Joint Optimization/Artificial Intelligence Seminar, Computer Sciences Department
University of Wisconsin, Madison November 14, 2005
Computational & Applied Mathematics Seminar, Mathematics Department
University of California at San Diego, La Jolla January 24, 2006

Olvi Mangasarian & Edward Wild: Multiple Instance Learning via Successive Linear Programming (PowerPoint)
Computation & Informatics in Biology and Medicine (CIBM) Retreat
Pyle Center, Madison, Wisconsin
October 28, 2005

Olvi Mangasarian: Optimization in Data Mining (PowerPoint) (pdf)
International Conference on Continuous Optimization ICCOPT I, August 2-4, 2004, Rensselaer Polytechnic Institute Troy, New York. Computational and Applied Mathematics Seminar, Mathematics Department, University of California at San Diego, February 1, 2005.

Olvi Mangasarian & Edward Wild: Feature Selection in k-Median Clustering (PowerPoint)
Fourth SIAM International Conference on Data Mining (SDM 2004)
Workshop on Clustering in High Diemensional Data and its Applications
Lake Buena Vista, Florida, April 24, 2004.

Olvi Mangasarian, Jude Shavlik & Edward Wild: Knowledge-Based Kernel Approximation (PowerPoint)
Numerical Analysis Seminar, Mathematics Department, University of California at San Diego, January 13, 2004.

Glenn Fung, Olvi Mangasarian & Jude Shavlik: Knowledge-Based Nonlinear Support Vector Machine Classifiers (PowerPoint)
COLT 2003: The Sixteenth Annual Conference on Learning Theory and The Seventh Workshop on Kernel Machines Washington D.C. August 24-27, 2003. http://learningtheory.org/colt2003/

Y.-J. Lee, O. L. Mangasarian & W. H. Wolberg: Survival Time Classification of Breast Cancer Patients and Chemotherapy (PowerPoint)
Invited Talk, International Symposium on Mathematical Programming, Copenhagen, August 24-29, 2003.; Revised Talk (PowerPoint)
Computational & Applied Mathematics Seminar, Department of Mathematics, University of California at San Diego, April 19, 2005.

O. L. Mangasarian: Support Vector Machine Data Mining (PowerPoint)
Invited Talk, ExonHit Therapeutics, Paris, August 13, 2003.

O. L. Mangasarian: Support Vector Machines: Classification Algorithms and Applications (PowerPoint)
Scientific Computation Seminar, Mathematics Department, University of California at San Diego, February 4, 2003.

Glenn Fung, Olvi Mangasarian & Jude Shavlik: Knowledge-Based Support Vector Machine Classifiers (PowerPoint)
Neural Information Processing Systems NIPS*2002, Vancouver December 9-14, 2002.

G. Fung, O. L. Mangasarian & Alexnader J. Smola: Minimal Kernel Classifiers (PowerPoint)
INFORMS 2002, San Jose, California, November 17-20, 2002.

O. L. Mangasarian: Support Vector Machine Classification (PowerPoint)
Computation and Informatics in Biology and Medicine Retreat, Madison, November 15, 2002.

O. L. Mangasarian: A Newton Method for Linear Programming (PowerPoint)
Mathematics Department, University of California at San Diego, July 26, 2002.

G. Fung & O. L. Mangasarian: The Disputed Federalist Papers: SVM Feature Selection via Concave Minimization (PowerPoint)
CSNA 2002: Classification Society of North America Annual Meeting, Madison, Wisconsin, June 13-16, 2002.

O. L. Mangasarian: Support Vector Machines in Data Mining (PowerPoint)
AFOSR Systems & Software Annual Meeting, Syracuse, NY, June 3-7, 2002

G. Fung & O. L. Mangasarian: Incremental Support Vector Machine Classification (PowerPoint)
Second SIAM International Conference on Data Mining SDM 2002 Arlington, Virginia, April 11-13, 2002

G. Fung & O. L. Mangasarian: Proximal Support Vector Machine Classifiers (PowerPoint)
KDD 2001: Seventh ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
San Francisco August 26-29, 2001

O. L. Mangasarian: Data Mining with Support Vector Machines (PowerPoint)
IFIP 2001 System Modeling and Optimization Conference
Trier, Germany July 23 - 27, 2001

O. L. Mangasarian: Mathematical Programming for Support Vector Machines (PowerPoint)
INRIA
Rocquencourt, France July 17, 2001

Y.-J. Lee & O. L. Mangasarian: RSVM: Reduced Support Vector Machines (PowerPoint)
First SIAM International Conference on Data Mining
Chicago April 5-7, 2001. CD ROM Proceedings.

David R. Musicant & O. L. Mangasarian: LSVM: Lagrangian Support Vector Machines (PowerPoint)
NIPS 2000, Workshop on New Perspectives in Kernel-Based Learning Methods
Breckenridge, CO, December 1st, 2000

Glenn Fung & O. L. Mangasarian: Unlabeled Data Classification
INFORMS Annual Meeting, San Antonio, Texas, November 5-8, 2000.

O. L. Mangasarian: Mathematical Programming in Support Vector Machines (PowerPoint)
Video of Talk (RealVideo Stream)
HPCES (High Performance Computation for Engineering Systems) Seminar, MIT, October 4, 2000.

Glenn Fung & O. L. Mangasarian: Data Selection for Support Vector Machine Classification
(PowerPoint file here)
KDD-00 Knowledge Discovery and Data Mining, Boston, MA, August 20-23, 2000.

O. L. Mangasarian: Machine Learning & Data Mining via Support Vector Machines
(PowerPoint file here)
AFOSR Software & Systems Program Review, Marina Del Rey, CA, March 28-29, 2000

Y.-J. Lee, O. L. Mangasarian & W. H. Wolberg: Breast Cancer Survival Analysis and Chemotherapy via Generalized Support Vector Machines
Center for Discrete Mathematics and Theoretical Computer Science at Rutgers University (DIMACS), Workshop on Discrete Mathematical Problems with Medical Applications, December 8-10, 1999

David R. Musicant & O. L. Mangasarian: Massive Support Vector Regression.
(PowerPoint file here)
NIPS*99 Workshop on Learning with Support Vectors: Theory and Applications December 3, 1999.

Y.-J. Lee & O. L. Mangasarian: SSVM: A Smooth Support Vector Machine
Philadelphia INFORMS, November 7-10, 1999

O. L. Mangasarian & D. R. Musicant: Nonlinear Data Discrimination via Generalized Support Vector Machines
(PowerPoint file here - Philadelphia INFORMS, November 7-10, 1999)
ICCP99: International Conference on Complementarity Problems, Madison, Wisconsin, June 9-12, 1999

O. L. Mangasarian: Optimization in Machine Learning and Data Mining
Joint SIAM Annual Meeting & Optimization Conference, Atlanta, May 12, 1999.
Related SIAM News Article Volume 32, Number 10, December 1999 (pdf)

O. L. Mangasarian: Massive Data Discrimination via Generalized Support Vector Machines
AFOSR Software & Systems Program Review, Colorado Springs, February 3-4, 1999.

O. L. Mangasarian: Mathematical Programming in Machine Learning
NIPS*98 Workshop on Large Margin Classifiers, Breckenridge, Colorado, December 4-5, 1998.

O. L. Mangasarian & David R. Musicant: Successive Overrelaxation for Support Vector Machines
NIPS*98 Workshop on Mining Massive Databases, Breckenridge, Colorado, December 5, 1998.

Publications Since 1990

O. L. Mangasarian: Sufficient Conditions for the Unsolvability and Solvability of the Absolute Value Equation Data Mining Institute Technical Report 16-01, August 2016. Optimization Letters 11(7) 2017, 1469-1475.

G. M. Fung & O. L. Mangasarian: Unsupervised and Semisupervised Classification via Absolute Value Inequalities Data Mining Institute Technical Report 14-03, May 2014. Journal of Optimization Theory and Applications 168(2) 2016, 551-558.

O. L. Mangasarian: A Hybrid Algorithm for Solving the Absolute Value Equation Data Mining Institute Technical Report 14-02, April 2014. Optimization Letters 9(7) 2015, 1469-1474.

O. L. Mangasarian: Unsupervised Classification via Convex Absolute Value Inequalities Data Mining Institute Technical Report 14-01, March 2014. Optimization 64(1) 2015, 81-86.

O. L. Mangasarian: Linear Complementarity as Absolute Value Equation Solution Data Mining Institute Technical Report 13-02, March 2013. Optimization Letters 8(4) 2014, 1529-1534.

O. L. Mangasarian: Absolute Value Equation Solution via Linear Programming Data Mining Institute Technical Report 13-01, Februaty 2013. Journal of Optimization Theory and Applications 161, 870-876, 2014.

G. M. Fung and O. L. Mangasarian: Privacy-Preserving Linear and Nonlinear Approximation via Linear Programming
PDF Version
Data Mining Institute Technical Report 11-04, October 2011. Optimization Methods and Software 28(1), 207-216, 2013.

O. L. Mangasarian: Absolute Value Equation Solution via Dual Complementarity
PDF Version
Data Mining Institute Technical Report 11-03, September 2011. Optimization Letters 7(4), 2013, 625-630.

G. M. Fung and O. L. Mangasarian: Equivalence of Minimal 0-Norm and p-Norm Solutions of Linear Equalities, Inequalities and Linear Programs for Sufficiently Small p
PDF Version
Data Mining Institute Technical Report 11-02, April 2011. Journal of Optimization Theory and Applications 151, 2011, 1-10.

O. L. Mangasarian: Primal-Dual Bilinear Programming Solution of the AbsoluteValue Equation
PDF Version
Data Mining Institute Technical Report 11-01, February 2011. Optimization Letters 6(7), 1527-1533, 2012.

O. L. Mangasarian: Privacy-Preserving Horizontally-Partitioned Linear Programs
PDF Version
Data Mining Institute Technical Report 10-02, April 2010. Optimization Letters 6(3), 431-436, 2012.
Example of Security Breach Attempt

O. L. Mangasarian: Privacy-Preserving Linear Programming
PDF Version
Data Mining Institute Technical Report 10-01, March 2010. Optimization Letters 5, 165-172, 2011.

O. L. Mangasarian and Benjamin Recht: Probability of Unique Integer Solution to a System of Linear Equations
PDF Version
Data Mining Institute Technical Report 09-02, September 2009. European Journal of Operations Research 214 (2011) 27-30.

O. L. Mangasarian and M. C. Ferris: Uniqueness of Integer Solution of Linear Equations
PDF Version
Data Mining Institute Technical Report 09-01, July 2009. Optimization Letters 4, 559-565, 2010.

O. L. Mangasarian: Knapsack Feasibility as an Absolute Value Equation Solvable by Successive Linear Programming
PDF Version
Data Mining Institute Technical Report 08-03, September 2008. Optimization Letters 3(2) March 2009, 161-170. Online Version

O. L. Mangasarian and E. W. Wild: Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data
PDF Version
Data Mining Institute Technical Report 08-02, September 2008. Annals of Information Systems XIII, 2010, 375-387.

O. L. Mangasarian: A Generalized Newton Method for Absolute Value Equations
PDF Version
Data Mining Institute Technical Report 08-01, May 2008. Optimization Letters 3(1), January 2009, 101-108. Online Version

O. L. Mangasarian and E. W. Wild: Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels
PDF Version
Data Mining Institute Technical Report 07-03, November 2007. Proceedings of the 2008 International Conference on Data Mining DMIN08, Las Vegas July 2008, Volume II, 473-479, R. Stahlbock, S.V. Crone and S. Lessman, Editors.
DMIN08 Best Academic Research Paper Award.

O. L. Mangasarian, E. W. Wild and G. M. Fung: Privacy-Preserving Classification of Vertically Partitioned Data via Random Kernels
PDF Version
Data Mining Institute Technical Report 07-02, September 2007. ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 3, Number 2, 2008.

O. L. Mangasarian and E. W. Wild: Exactness Conditions for a Convex Differentiable Exterior Penalty for Linear Programming
PDF Version
Data Mining Institute Technical Report 07-01, July 2007. Optimization, Volume 60, Nos. 1-2, Jan-Feb 2011, 3-14.

O. L. Mangasarian and M. E. Thompson: Chunking for Massive Nonlinear Kernel Classification
PDF Version
Data Mining Institute Technical Report 06-07, December 2006. Optimization Methods and Software, 23, 2008, 365-274.

O. L. Mangasarian and E. W. Wild: Nonlinear Knowledge in Kernel Machines
PDF Version
Data Mining Institute Technical Report 06-06, November 2006. CRM Proceedings \& Lecture Notes, Volume 45, American Mathematical Society and Centre de Recherches Math\'{e}matiques at the Universit\'{e} de Montr\'{e}al, 2008, 181-198.

O. L. Mangasarian, E. W. Wild and G. M. Fung: Proximal Knowledge-Based Classification
PDF Version
Data Mining Institute Technical Report 06-05, November 2006. Statistical Analysis and Data Mining 1(4) 2009, 215-222.

O. L. Mangasarian & E. W. Wild: Nonlinear Knowledge-Based Classification
PDF Version
Data Mining Institute Technical Report 06-04, August 2006. IEEE Transactions on Neural Networks 19, October 2008, 1826-1832.

O. L. Mangasarian & E. W. Wild: Feature Selection for Nonlinear Kernel Support Vector Machines
PDF Version
Data Mining Institute Technical Report 06-03, July 2006. IEEE Seventh International Conference on Data Mining (ICDM'07) October 28, 2007, Omaha, NE, Workshop Proceedings 231-236.

O. L. Mangasarian: Absolute Value Equation Solution via Concave Minimization
PDF Version
Data Mining Institute Technical Report 06-02, March 2006. Optimization Letters 1(1), 2007, 3-8.

O. L. Mangasarian and M. E. Thompson: Massive Data Classification via Unconstrained Support Vector Machines
PDF Version
Data Mining Institute Technical Report 06-01, March 2006. Journal of Optimization Theory and Applications 131(3), December 2006, 315-325.

O. L. Mangasarian and R. R. Meyer: Absolute Value Equations
PDF Version
Data Mining Institute Technical Report 05-06, December 2005. Linear Algebra and Its Applications 419 (2006) 359-367.

O. L. Mangasarian and E. W. Wild: Nonlinear Knowledge in Kernel Approximation
PDF Version
Data Mining Institute Technical Report 05-05, October 2005. Revised June 2006. IEEE Transactions on Neural Networks 18, January 2007, 300-306.

O. L. Mangasarian: Absolute Value Programming
PDF Version
Data Mining Institute Technical Report 05-04, September 2005. Computational Optimization and Applications 36(1), January 2007, 43-53.
Computational Optimization and Applications 2007 Best Paper Award. COAP (2008) 41:147-149.

O. L. Mangasarian: Exact 1-Norm Support Vector Machines via Unconstrained Convex Differentiable Minimization
PDF Version
Data Mining Institute Technical Report 05-03, August 2005. Revised January 2006. Journal of Machine Learning Research 7, 2006, 1517-1530.

O. L. Mangasarian and E. W. Wild: Multiple Instance Classification via Successive Linear Programming
PDF Version
Data Mining Institute Technical Report 05-02, May 2005. Journal of Optimization Theory and Applications 137(1), 2008, 555-568.

O. L. Mangasarian, J. B. Rosen and M. E. Thompson: Nonconvex Piecewise-Quadratic Underestimation for Global Minimization
PDF Version
Data Mining Institute Technical Report 05-01, March 2005, Journal of Global Optimization 34(4), 2006, 475-488.

O. L. Mangasarian and E. W. Wild: Multisurface Proximal Support Vector Classification via Generalized Eigenvalues
PDF Version
Data Mining Institute Technical Report 04-03, June 2004. Revised September 2004. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 2006, 69-74.
"This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

O. L. Mangasarian, J. B. Rosen and M. E. Thompson: Convex Kernel Estimation of Functions with Multiple Local Minima
PDF Version
Data Mining Institute Technical Report 04-02, May 2004. Computational Optimization and Applications 34(1), 2006, 35-45.

O. L. Mangasarian and E. W. Wild: Feature Selection in k-Median Clustering
PDF Version
Data Mining Institute Technical Report 04-01, January 2004. SIAM International Conference on Data Mining, Workshop on Clustering High Dimensional Data and its Applications, April 24, 2004, La Buena Vista, FL, Proceedings, pages 23-28.

G. M. Fung and O. L. Mangasarian: Breast Tumor Susceptibility to Chemotherapy via Support Vector Machines
PDF Version
Data Mining Institute Technical Report 03-06, November 2003. Computational Management Science 3, 2006, 103-112.

O. L. Mangasarian, J. W. Shavlik and E. W. Wild: Knowledge-Based Kernel Approximation
PDF Version
Data Mining Institute Technical Report 03-05, October 2003. Journal of Machine Learning Research 5, 1127-1141, 2004.

O. L. Mangasarian: Knowledge-Based Linear Programming
PDF Version
Data Mining Institute Technical Report 03-04, July 2003. SIAM Journal on Optimization 15, 2005, 375-382.

O. L. Mangasarian, J. B. Rosen and M. E. Thompson: Global Minimization via Piecewise-Linear Underestimation
PDF Version
Data Mining Institute Technical Report 03-03, June 2003. Journal of Global Optimization 32, 2005, 1-9.

G. M. Fung, O. L. Mangasarian and J. W. Shavlik: Knowledge-Based Nonlinear Kernel Classifiers
PDF Version
Data Mining Institute Technical Report 03-02, March 2003. Conference On Learning Theory (COLT 03) and Workshop on Kernel Machines, Washington, D.C., August 24 - 27, 2003. Proceedings edited by Manfred Warmuth and Bernhard Sch\"olkopf, Springer Verlag, Berlin, 2003, 102-113.

O. L. Mangasarian: Support Vector Machine Classification via Parameterless Robust Linear Programming
PDF Version
Data Mining Institute Technical Report 03-01, March 2003. Optimization Methods and Software 20, 2005, 115-125.

Glenn Fung and O. L. Mangasarian: A Feature Selection Newton Method for Support Vector Machine Classification
PDF Version
Data Mining Institute Technical Report 02-03, September 2002. Computational Optimization and Applications 28(2) 185-202, 2004.

O. L. Mangasarian: A Newton Method for Linear Programming
PDF Version
Data Mining Institute Technical Report 02-02, March 2002. Revised December 2002. Journal of Optimization Theory and Applications 121, 2004, 1-18. MATLAB Files

G. Fung and O. L. Mangasarian: Finite Newton Method for Lagrangian Support Vector Machine Classification
PDF Version
Data Mining Institute Technical Report 02-01, February 2002. Neurocomputing 55, September 2003, 39-55.

O. L. Mangasarian: A Finite Newton Method for Classification
PDF Version
Data Mining Institute Technical Report 01-11, December 2001. Optimization Methods and Software 17, 2002, 913-929.

O. L. Mangasarian: Set Containment Characterization
PDF Version
Data Mining Institute Technical Report 01-10, November 2001. Journal of Global Optimization 24(4) December 2002, 473-480.

Glenn Fung, O. L. Mangasarian and Jude Shavlik: Knowledge-Based Support Vector Machine Classifiers
PDF Version
Data Mining Institute Technical Report 01-09, November 2001. Neural Information Processing Systems 2002 (NIPS 2002), Vancouver, BC, December 10-12, 2002. ``Neural Information Processing Systems 15", S. Becker, S. Thrun and K. Obermayer, editors, MIT Press, Cambridge, MA, 2003, 521-528.

G. Fung and O. L. Mangasarian: Incremental Support Vector Machine Classification
PDF Version
Data Mining Institute Technical Report 01-08, September 2001. Proceedings of the Second SIAM International Conference on Data Mining, Arlington, Virginia, April 11-13, 2002,R. Grossman, H. Mannila and R. Motwani (editors), SIAM, Philadelphia 2002, 247-260.

G. Fung and O. L. Mangasarian: Multicategory Proximal Support Vector Machine Classifiers
PDF Version
Data Mining Institute Technical Report 01-06, July 2001. Machine Learning 59, 2005, 77-97.

O. L. Mangasarian: Data Mining via Support Vector Machines
PDF Version
Data Mining Institute Technical Report 01-05, May 2001. IFIP Conference on System Modelling and Optimization, Trier, Germany, July 23-27, 2001. ``System Modeling and Optimization XX", E. W. Sachs and R. Tichatschke, editors, Kluwer Academic Publishers, Boston 2003, 91-112.

Y.-J. Lee, O. L. Mangasarian and W. H. Wolberg: Survival-Time Classification of Breast Cancer Patients
PDF Version
Data Mining Institute Technical Report 01-03, March 2001. Computational Optimization and Applications 25, 2003, 151-166.

Glenn Fung and O. L. Mangasarian: Proximal Support Vector Machine Classifiers
PDF Version
Data Mining Institute Technical Report 01-02, February 2001. Proceedings KDD-2001, San Francisco August 26-29, 2001. Association for Computing Machinery, New York, 2001, 77-86.

Glenn Fung, O. L. Mangasarian & Alexander J. Smola: Minimal Kernel Classifiers
PDF Version
Data Mining Institute Technical Report 00-08, November 2000. Journal of Machine Learning Research 3, 2002, 303-321. http://www.ai.mit.edu/projects/jmlr/

Yuh-Jye Lee and O. L. Mangasarian: RSVM: Reduced Support Vector Machines
PDF Version
Data Mining Institute Technical Report 00-07, July 2000. CD Proceedings of the SIAM International Conference on Data Mining, Chicago, April 5-7, 2001, SIAM, Philadelphia, ISBN 0-89871-495-8.

O. L. Mangasarian and David R. Musicant: Lagrangian Support Vector Machines
PDF Version
Data Mining Institute Technical Report 00-06, June 2000.
Journal of Machine Learning Research 1, March 2001, 161-177.
Lagrangian Support Vector Machine web page with MATLAB code.

O. L. Mangasarian and David R. Musicant: Active Set Support Vector Machine Classification
Neural Information Processing Systems 2000 (NIPS 2000), Todd K. Lee, Thomas G. Dietterich and Volker Tresp, editors, MIT Press 2001, 577-583.
Active Support Vector Machine web page with MATLAB code.

G. Fung and O. L. Mangasarian: Data Selection for Support Vector Machine Classifiers
PDF Version
Data Mining Institute Technical Report 00-02, February 2000. "KDD-2000", Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 20-23, 2000, Boston, MA, R. Ramakrishnan & S. Stolfo, editors, ACM, NY 2000, 64-70.

Y.-J. Lee, O. L. Mangasarian and W. H. Wolberg: Breast Cancer Survival and Chemotherapy: A Support Vector Machine Analysis
Data Mining Institute Technical Report 99-10, December 1999. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 55, American Mathematical Society 2000, 1-10.

O. L. Mangasarian and D. R. Musicant: Robust Linear and Support Vector Regression
PDF Version
Data Mining Institute Technical Report 99-09, November 1999. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 2000, 950-955.

Glenn Fung and O. L. Mangasarian: Semi-Supervised Support Vector Machines for Unlabeled Data Classification.
PDF Version
Data Mining Institute Technical Report 99-05, October 1999. Optimization Methods and Software 15, 2001, 29-44.

Alex J. Smola, Olvi L. Mangasarian and Bernhard Schoelkopf: Sparse Kernel Feature Analysis.
Data Mining Institute Technical Report 99-04, October 1999. 24th Annual Conference of Gesellschaft für Klassifikation, University of Passau, Passau, Germany March 15-17, 2000.

Y.-J. Lee and O. L. Mangasarian: SSVM: A Smooth Support Vector Machine for Classification
PDF Version
Data Mining Institute Technical Report 99-03, September 1999, Computational Optimization and Applications 20(1), October 2001, 5-22.

O. L. Mangasarian and David R. Musicant: Large Scale Kernel Regression via Linear Programming
PDF Version
Data Mining Institute Technical Report 99-02, August 1999, Machine Learning 46(1/3), 255-269, January 2002.

P. S. Bradley, O. L. Mangasarian and David R. Musicant: Optimization Methods in Massive Datasets.
Data Mining Technical Report 99-01, June 1999. "Handbook of Massive Datasets", J. Abello , P. M. Pardalos, M. G. C. Resende, editors, Kluwer Academic Publishers, 2002, 439-472.

O. L. Mangasarian and David R. Musicant: Data Discrimination via Nonlinear Generalized Support Vector Machines
Mathematical Programming Technical Report 99-03, March 1999. "Complementarity: Applications, Algorithms and Extensions", in M. C. Ferris, O. L. Mangasarian and J.-S. Pang, editors, Kluwer Academic Publishers, 2001, 233-251.

O. L. Mangasarian and David R. Musicant: Successive Overrelaxation for Support Vector Machines.
Mathematical Programming Technical Report 98-18, November 1998. IEEE Transactions on Neural Networks, 10, September 1999, 1032-1037.

O. L. Mangasarian: Generalized Support Vector Machines (Technical Report Version) PDF Version (MIT Press Version)
Mathematical Programming Technical Report 98-14, October 1998. "Advances in Large Margin Classifiers", A. J. Smola, P. Bartlett, B. Sch\"{o}kopf and D. Schuurmans, editors, MIT Press, 2000, 135-146.

P. S. Bradley & O. L. Mangasarian: k-Plane Clustering.
Mathematical Programming Technical Report 98-08, August 1998. Journal of Global Optimization 16, Number 1, 2000, 23-32.

P. S. Bradley & O. L. Mangasarian: Massive Data Discrimination via Linear Support Vector Machines.
Mathematical Programming Technical Report 98-05, May 1998. Revised March 31, 1999. Optimization Methods and Software, 13(1), 2000, 1-10.

P. S. Bradley & O. L. Mangasarian: Feature Selection via Concave Minimization and Support Vector Machines.
Mathematical Programming Technical Report 98-03, February 1998. "Machine Learning Proceedings of the Fifteenth International Conference(ICML '98)", J. Shavlik, editor, Morgan Kaufmann, San Francisco, California, 82-90, 1998.

P. S. Bradley, Usama M. Fayyad & O. L. Mangasarian: Mathematical Programming for Data Mining: Formulations and Challenges.
PDF Version
Mathematical Programming Technical Report 98-01, January 1998. Revised July 1998 . INFORMS Journal on Computing 11, 1999, 217-238.

O. L. Mangasarian: Regularized Linear Programs with Equilibrium Constraints.
Mathematical Programming Technical Report 97-13, November 1997. In "Reformulation-Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods", M. Fukushima and Liqun Qi, editors, Kluwer Academic Publishers, 1998, 259-268.

P. S. Bradley & O. L. Mangasarian: Parsimonious Side Propagation,
Mathematical Programming Technical Report 97-11, October 1997. In "ICASSP98: IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle May 12-15, 1998, Volume 3, pages 1873-1876.

O. L. Mangasarian: Polyhedral Boundary Projection.
Mathematical Programming Technical Report 97-10, October 1997, SIAM Journal on Optimization 9, 1999, 1128-1134.

O. L. Mangasarian: Arbitrary-Norm Separating Plane.
Mathematical Programming Technical Report 97-07, May 1997.

O. L. Mangasarian: Arbitrary-Norm Separating Plane.
Mathematical Programming Technical Report 97-07r, May 1997, Revised September 1998. Operations Research Letters 24, 1999, 15-23.

O. L. Mangasarian: Minimum-Support Solutions of Polyhedral Concave Programs
Mathematical Programming Technical Report 97-05, April 1997. Revised March 1998, Optimization 45, 1999, 149-162.

P. S. Bradley, O. L. Mangasarian and J. B. Rosen: Parsimonious Least Norm Approximation
Mathematical Programming Technical Report 97-03, March 1997. Computational Optimization and Applications 11(1), October 1998, 5-21.

O. L. Mangasarian: Solution of General Linear Complementarity Problems via Nondifferentiable Concave Minimization.
Mathematical Programming Technical Report 96-10, November 1996, Acta Mathematica Vietnamica, 22(1), 1997, 199-205.

O. L. Mangasarian and M. V. Solodov: A Linearly Convergent Derivative-Free Descent Method for Strongly Monotone Complementarity Problems.
Mathematical Programming Technical Report 96-07, October 1996, Computational Optimization & Applications 14, 1999, 5-16.

O. L. Mangasarian and Jong-Shi Pang: Exact Penalty Functions for Mathematical Programs with Linear Complementarity Constraints.
Mathematical Programming Technical Report 96-06, August 1996, Optimization 42(1), 1997, 1-8.

O. L. Mangasarian: Mathematical Programming in Data Mining
Mathematical Programming Technical Report 96-05, August 1996 -- Revised November 1996 and March 1997, Data Mining and Knowledge Discovery, 1(2), 1997, 183-201.

O. L. Mangasarian: Error Bounds for Nondifferentiable Convex Inequalities under a Strong Slater Constraint Qualification.
Mathematical Programming Technical Report 96-04, July 1996. Revised March 1997, Mathematical Programming, 83, 1998, 187-194.

P. S. Bradley, O. L. Mangasarian and W. N. Street: Clustering via Concave Minimization.
Mathematical Programming Technical Report 96-03, May 1996. Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, MA 1997, 368-374.

W. N. Street, O. L. Mangasarian and W. H. Wolberg: Individual and Collective Prognostic Prediction.
Mathematical Programming Technical Report 96-01, January 1996.

P. S. Bradley, O. L. Mangasarian and W. N. Street: Feature Selection via Mathematical Programming.
Mathematical Programming Technical Report 95-21, December 1995. INFORMS Journal on Computing 10, 1998, 209-217.

O. L. Mangasarian: Machine Learning via Polyhedral Concave Minimization.
Mathematical Programming Technical Report 95-20, November 1995. "Applied Mathematics and Parallel Computing -- Festschrift for Klaus Ritter", H. Fischer, B. Riedmueller, S. Schaeffler, editors, Physica-Verlag, Germany 1996, 175-188.

O. L. Mangasarian: The Ill-Posed Linear Complementarity Problem.
Mathematical Programming Technical Report 95-15, August 1995, Revised November 1995. "Complementarity and Variational Problems", M. C. Ferris and J.-S. Pang, editors, SIAM, Philadelphia, PA, 1997, 226-233.

W. Nick Street and O. L. Mangasarian: Improved Generalization via Tolerant Training.
Mathematical Programming Technical Report 95-11, July 1995. Journal of Optimization Theory and Applications, 96(2), February 1998, 259-279.

O. L. Mangasarian: Mathematical Programming in Machine Learning.
Mathematical Programming Technical Report 95-06, April 1995, Revised July 1995 in "Nonlinear Optimization and Applications", G. Di Pillo and F. Giannessi, editors, Proceedings of Nonlinear Optimization and Applications Workshop, Erice, June 1995, Plenum Press, New York 1996, 283-295.

W. N. Street, O. L. Mangasarian and W. H. Wolberg: An inductive learning approach to prognostic prediction.
Proceedings of the Twelfth International Conference on Machine Learning, A. Prieditis and S. Russell, editors, San Francisco, 522-530, 1995.

Chunhui Chen and O. L. Mangasarian: Hybrid Misclassification Minimization.
Mathematical Programming Technical Report 95-05, February 1995, Revised July 1995 and August 1995. Advances in Computational Mathematics 5(2) 1996, 127-136.

O. L. Mangasarian: Optimization in Machine Learning.
Mathematical Programming Technical Report 95-01, January 1995. SIAG/OPT Views-and-News 6, 1995, 3-7.

W. H. Wolberg, W. N. Street and O. L. Mangasarian: Computerized breast cancer diagnosis from fine needle aspirates.
Archives of Surgery 130, 511-516, 1995.

Chunhui Chen and O. L. Mangasarian: A Class of Smoothing Functions for Nonlinear and Mixed Complementarity Problems.
Mathematical Programming Technical Report 94-11, August 1994. Revised October 1994, February 1995 and September 1995. Computational Optimization and Applications 5, 1996, 97-138.

O. L. Mangasarian, W. Nick Street and W. H. Wolberg: Breast Cancer Diagnosis and Prognosis via Linear Programming.
Mathematical Programming Technical Report 94-10, August 1994. Revised December 1994. Operations Research 43(4), July-August 1995, 570-577.

O. L. Mangasarian: The Linear Complementarity Problem as a Separable Bilinear Program.
Mathematical Programming Technical Report 94-09, July 1994. Journal of Global Optimization 6, 1995, 153-161.

O. L. Mangasarian and M. V. Solodov: Backpropagation Convergence via Deterministic Nonmonotone Perturbed Minimization.
Mathematical Programming Technical Report 94-06, June 1994. Advances in Neural Information Processing Systems 6, (J. D. Cowan, G. Tesauro and J. Alspector, editors) 383-390, Morgan Kaufmann Publishers, San Francisco, California 1994.

Chunhui Chen and O. L. Mangasarian: Smoothing Methods for Convex Inequalities and Linear Complementarity Problems.
Computer Sciences Technical Report 1191r, November 1993. Revised November 1994. Mathematical Programming 71, 1995, 51-69.

O. L. Mangasarian and J.-S. Pang: The Extended Linear Complementarity Problem.
Computer Sciences Technical Report 1188, November 1993. SIAM Journal on Matrix Analysis and Applications 16, 1995, 359-368.

O. L. Mangasarian: Misclassification Minimization.
Computer Sciences Technical Report 1186, October 1993. Revised September 1994. Journal of Global Optimization 5(4), December 1994, 309-323.

O. L. Mangasarian: Error Bounds for Inconsistent Linear Inequalities and Programs.
Computer Sciences Technical Report 1166, July 1993. Operations Research Letters 15, May 1994, 187-192.

O. L. Mangasarian and M. V. Solodov: Serial and Parallel Backpropagation for Neural Nets via Nonmonotone Perturbed Minimization.
Computer Sciences Technical Report 1149r, April 1993. Revised December 1993. Optimization Methods and Software 4, 1994, 103-116.

O. L. Mangasarian: Parallel Gradient Distribution in Unconstrained Optimization.
Computer Sciences Technical Report 1145, 1993. SIAM Journal on Control and Optim ization 33(6), 1995, 1916-1925.

W. N. Street, W. H. Wolberg and O. L. Mangasarian: Nuclear feature extraction for breast tumor diagnosis.
IS&E/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, Volume 1905, pages 861-870, San Jose, California, 1993.

O. L. Mangasarian: Mathematical Programming in Neural Networks.
Computer Sciences Technical Report 1129, 1992. ORSA Journal on Computing 5, 1993, 349-360.

K. P. Bennett and O. L. Mangasarian: Multicategory Discrimination via Linear Programming.
Computer Sciences Technical Report 1127, 1992. Optimization Methods and Software 3, 1994, 27-39.

W. H. Wolberg, W. N. Street and O. L. Mangasarian: Breast cytology diagnosis with digital image analysis.
Analytical and Quantitative Cytology and Histology 15(6), 1993, 396-404

K. P. Bennett and O. L. Mangasarian: Bilinear Separation of Two Sets in n-Space.
Computer Sciences Technical Report 1109, 1992. Computational Optimization and Applications 2, 1993, 207-227.

O. L. Mangasarian and M. V. Solodov: Nonlinear Complementarity as Unconstrained and Constrained Minimization.
Computer Sciences Technical Report 1074, 1992. Mathematical Programming, Series B, 62, 1993, 277-297.

Michael C. Ferris and Olvi L. Mangasarian: Error Bounds and Strong Upper Semicontinuity for Monotone Affine Variational Inequalities.
Computer Sciences Technical Report 1056a, 1991. Annals of Operations Research, 47, 1993, 293-305.

K. P. Bennett and O. L. Mangasarian: Robust Linear Programming Discrimination of Two Linearly Inseparable Sets.
Computer Sciences Technical Report 1054a, 1991. Optimization Methods and Software 1, 1992, 23-34.

Michael C. Ferris and O. L. Mangasarian: Parallel Constraint Distribution.
Computer Sciences Technical Report 971, 1990. SIAM Journal on Optimization 1, 1991, 487-500.

O. L. Mangasarian, R. Setiono and W. H. Wolberg: Pattern recognition via linear programming :theory and application to medical diagnosis.
Computer Sciences Technical Report 878, 1989."Large-Scale Numerical Optimization ", T. F. Coleman and Y. Li, editors, SIAM, Philadelphia, Pennsylvania 1990, 22-3 1.

Some Publications Prior to 1990

O. L. Mangasarian: A Simple Characterization of Solution Sets of Convex Programs.
Computer Sciences Technical Report 685r, 1987. Operations Research Letters 7, 1988, 21-26.

O. L. Mangasarian and T.-H. Shiau: Variable Complexity Norm Maximization Problem
SIAM Journal on Algebraic and Discrete Methods 7(3), 1986, 455-461.