We present a model for clustering which combines two criteria: Given a collection of objects with pairwise similarity measure, the problem is to find a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. The two objectives are combined either as a ratio or with linear weights. The ratio problem... Read More
MOSEK is a software package for solving large scale sparse optimization problems. To be precise MOSEK is capable of solving linear, convex quadratic and conic quadratic optimization problems possibly having some integer constrained variables. In addition MOSEK can solve continuous semidefinite optimization problems.
In this presentation we will review what is... Read More
Product and content personalization is now ubiquitous in e-commerce. Available transactional data is typically too sparse for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We formalize this problem as one of recovering a large-scale matrix, with side... Read More
Lehigh’s Industrial and Systems Engineering (ISE) Council will be holding their seventh ISE Career Fair on September 14, 2016, a day before the Lehigh University Career Fair on September 15th. Employers and students will be able to meet in a personal setting that and discuss the company’s internship/co-op/job opportunities! *This event is for both ISE & HSE (... Read More
"Industrial Engineering: Key Principles and Paradigms Developed to Date and the Opportunities and Challenges for the Future"
By adapting and applying the advances in physical, mathematical, social and management sciences, Industrial Engineers have played a major role in developing the modern economy and continually advancing our quality of life. Industrial... Read More
This talk is an “eye witness account” of the evolution of nonlinear optimization methods over the last 4 decades. Starting from the early days of simplex-inspired methods, to augmented Lagrangians and interior-points, this talk highlights the need for new active set methods and the opportunities (not challenges) provided by stochasticity. We conclude with a few... Read More
In this talk I will show some very recent results on optimal strategies for betting on individual sequences of binary outcomes, that is betting against a non-stochastic coin. This naturally extends the well-known Kelly strategy to the adversarial domain.
Moreover, I will show some surprising links between betting, online learning, and adaptive stochastic... Read More