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Turku Optimization Group (TOpGroup)

The mathematical and computational tools in optimization are used more frequently these days, as they provide efficient tools with negligible costs for many industries in the race for greater profits and better efficiency. The optimization group does research in both modeling and implementation of practical problems from industry and further development of algorithms. The following areas of optimization are at focus:

Research Unit Web Page: http://www.math.utu.fi/en/research/groups/opt/

Leader of the unit

Marko M. Mäkelä

Senior Researchers

Yury Nikulin Napsu Karmitsa Stefan Emet

Researchers

Kiril Kuzmin

Doctoral Students

Seppo Pulkkinen Napsu Karmitsa Stefan Emet

Projects 

3D Packing Problem

With Kine Robot Solutions Oy

Large Scale Mixed Integer Global Optimization

With Åbo Akademi University

Modeling the Ferry Services of the Archipelago

With the Centre for Maritime Studies

Publications 

Click here to see the full list of publications from the TUCS Publication Database

The latest updated publications:

Sepinoud Azimi, Joonas Jalonen, Jarkko Kari, Ion Petre (Eds.), Computability in Europe 2017, TUCS General Publication, 2017.

Noora Nieminen, Garbling Schemes and Applications. TUCS Dissertations 219. 2017.

Vladimir Emelichev, Yury Nikulin, Aspects of Stability for Multicriteria Quadratic Problems of Boolean Programming. TUCS Technical Reports 1188, TUCS, 2017.

Vladimir Emelichev, Yury Nikulin, Stability of Extremum Solutions in Vector Quadratic Discrete Optimization. TUCS Technical Reports 1189, 2017.

Ville-Pekka Eronen, Jan Kronqvist, Tapio Westerlund, Marko M. Mäkelä, Napsu Karmitsa, Extended Supporting Hyperplane Algorithm for Generalized Convex Nonsmooth MINLP Problems. TUCS Technical Reports 1179, TUCS, 2017.

Kaisa Joki, Adil M. Bagirov, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri, Double Bundle Method for Nonsmooth DC Optimization. TUCS Technical Reports 1173, TUCS, 2017.

Napsu Karmitsa, Manlio Gaudioso, Kaisa Joki, Splitting Metrics Diagonal Bundle Method for Large-Scale Nonconvex Nonsmooth Optimization. TUCS Technical Reports 1178, TUCS, 2017.

Outi Montonen, Kaisa Joki, Multiobjective Double Bundle Method for Nonsmooth Constrained Multiobjective DC Optimization. TUCS Technical Reports 1174, TUCS, 2017.

Tapio Westerlund, Ville-Pekka Eronen, Marko M. Mäkelä, Using Supporting Hyperplane Techniques in Solving Generalized Convex MINLP Problems. TUCS Technical Reports 1186, TUCS, 2017.

Seppo Pulkkinen, Nonlinear Kernel Density Principal Component Analysis with Application to Climate Data. Statistics and Computing 26(1), 471–492, 2016.

Jetro Vesti, Rich Words and Balanced Words. TUCS Dissertations 213. 2016.

Charalampos Zinoviadis, Hierarchy and Expansiveness in Two-Dimensional Subshifts of Finite type. TUCS Dissertations 209. 2016.

Vladimir Emelichev, Vadim Mychov, Yury Nikulin, Postoptimal Analysis for One Vector Venturesome Investment Problem. TUCS Technical Reports 1153, TUCS, 2016.

Napsu Karmitsa, Adil Bagirov, Sona Taheri, Diagonal Bundle Method for Solving the Minimum Sum-of-Squares Clustering Problems. TUCS Technical Reports 1156, TUCS, 2016.

Napsu Karmitsa, Adil Bagirov, Sona Taheri, MSSC Clustering of Large Data Using the Limited Memory Bundle Method. TUCS Technical Reports 1164, TUCS, 2016.

Napsu Karmitsa, Adil Bagirov, Sona Taheri, Limited Memory Bundle Method for Solving Large Clusterwise Linear Regression Problems. TUCS Technical Reports 1172, TUCS, 2016.