Best Paper Award

The Best Paper Award for the Journal of Global Optimization is awarded annually to honor the authors of the best paper published in the journal in the previous year.


  • Palagi, L. (2019). Global optimization issues in deep network regression: an overview. Journal of Global Optimization 73(2), pp. 239–277 (Click here to read)
  • de Oliveira, W. (2019). Proximal bundle methods for nonsmooth DC programming. Journal of Global Optimization 75 (2), pp.523–563 (Click here to read)
  • Chirkov, A.Y., Gribanov, D.V., Malyshev, D.S. et al. (2019). On the complexity of quasiconvex integer minimization problem. Journal of Global Optimization 73(4), pp.761–788 (Click here to read)


  • Bomze, I. M., Jeyakumar, V., and Li, G.  (2018). Extended trust-region problems with one or two balls: exact copositive and Lagrangian relaxations. Journal of Global Optimization 71(3), pp. 551–569. (Click here to read)
  • Kuang, X. and Zuluaga, L. F. (2018). Completely positive and completely positive semidefinite tensor relations for polynomial optimization. Journal of Global Optimization 70(3), pp.  551–577. (Click here to read)


  • Boukouvala, F., Faruque Hasan, M. M., and Floudas, C. A. (2017). Global optimization of general constrained grey-box models: New methods and its application to constrained PDEs for pressure swing. Journal of Global Optimization 67(1–2), pp. 3–42. (Click here to read)
  • Lu, C., Deng, Z., and Jin, Q. (2017). An eigenvalue decomposition based branch-and-bound algorithm for nonconvex quadratic programming problems with convex quadratic constraints. Journal of Global Optimization 67(3), pp. 475–493.  (Click here to read)
  • Beck, A. and Pan, D. (2017). A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints. Journal of Global Optimization 69(2), pp. 309–342. (Click here to read)


  • Bauschke, H.H., Dao, M. N., Noll, D., and Phan, H. M. (2016). On Slater's condition and finite convergence of the Douglas-Rachford algorithm for solving convex feasibility problems in Euclidean spaces. Journal of Global Optimization 69(2), pp. 309–342. (Click here to read)
  • Locatelli, M. (2016). Non polyhedral convex envelopes for 1-convex functions. Journal of Global Optimization 65(4), pp. 637–655. (Click here to read)


  • Patrascu, A. and Necoara, I. (2015). Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization. Journal of Global Optimization  61(1), 19–46. (Click here to read)


  • Bomze, I. M., Gollowitzer, S., and Yildirim, E. A. (2014). Rounding on the standard simplex: regular grids for global optimization. Journal of Global Optimization 59(2–3), pp. 243–258. (Click here to read)
  • Paulavicius, R., Sergeyev, Y. D., Kvasov, D. E., and Žilinskas, J. (2014). Globally-biased DISIMPL algorithm for expensive global optimization. Journal of Global Optimization 59(2–3), pp. 545–567. (Click here to read)
  • Tsoukalas, A. and Mitsos, A. (2014). Multivariate McCormick relaxations. Journal of Global Optimization 59(2–3), pp. 633–662. (Click here to read)


  • Misener, R. and Floudas, C. A. (2013). GloMIQO: Global mixed-integer quadratic optimizer. Journal of Global Optimization 57(1), pp 3–50. (Click here to read)


  • Sherali, H. D., Dalkiran, E., and Liberti, L. (2012). Reduced RLT representations for nonconvex polynomial programming problems. Journal of Global Optimization 52(3), pp. 447–469. (Click here to read)
  • Li, D.,  Sun, X. L., and Liu, C. L. (2012). An exact solution method for unconstrained quadratic 0–1 programming: a geometric approach. Journal of Global Optimization 52(4), pp. 797–829. (Click here to read)


  • Scott, J. K., Stuber, M. D., and Barton, P. I. (2011). Generalized McCormick Relaxations. Journal of Global Optimization 51(4), pp. 569-606. (Click here to read)