# ISE Seminar Archive

We present a new method of blackbox optimization via gradient sensing with the use of structured random orthogonal matrices, providing more accurate estimators than baselines with provable theoretical guarantees. We show that this algorithm can be...

Schedule of Events Spencer C. Schantz Lecture and 50th Anniversary Symposium (celebrating 50 years of the Ph.D. program) Featuring two talks, a panel discussion and the Spencer C. Schantz Lecture by Mikell Groover Time: 2:30 p.m. - 5:00 p.m. Location...

We present a simple restart scheme for speeding-up first order methods for convex optimization problems. Restarts are made based only on achieving specified decreases in objective values, the specified amounts being the same for all optimization...

The ability of traffic flows to adapt their rate and fairly use all available resources is one of the Internet’s pillars. This traffic characteristic, often referred to as elasticity, has however not been fully considered so far in network routing...

We propose an unsupervised learning framework for automatically tagging events in basketball game. Our framework uses the the optical player tracking data in the NBA. We first learn the time series of defensive assignment using a novel player and...

Cutting planes are the go-to technique for obtaining lower bounds (for minimization problems) in mixed-integer programming and discrete optimization. But a practical weakness is the sequential nature of the method, proceeding from one LP solution to...

**Date**: Wednesday, November 29, 2017

**Time**: 4:00 pm

**Title**: In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation.

This talk describes a new class of splitting methods for monotone operator problems developed by the author and P. Combettes. It can solve problems involving any finite number of operators and can operate in an asynchronous parallel manner. It has a...

We present a branch-and-cut algorithm for solving discrete bilevel linear programs where the upper-level variables are binary and the lowerlevel variables are either pure integer or pure binary. This algorithm performs local search to find improved...

We present a framework for a class of sequential decision-making problems in the context of max-min bilevel programming, where a leader and a follower repeatedly interact. At each period, the leader allocates resources to disrupt the performance of...

For convex optimization problems deterministic first order methods have linear convergence provided that the objective function is smooth (Lipschitz continuous gradient) and strongly convex. Moreover, under the same conditions – smoothness and strong...

This technical talk will show live calculations in Mathematica 11 and other Wolfram technologies relevant to courses and research. Specific topics include:* Visualize data, functions, surfaces, and more in 2D or 3D* Store and share documents locally...

Empirical risk minimization (ERM) problems express optimal classifiers as solutions of optimization problems in which the objective is the sum of a very large number of sample costs. Established approaches to solve ERM rely on computing stochastic...

Lehigh’s Industrial and Systems Engineering (ISE) Council will be holding their seventh ISE Career Fair on September 13, 2017, a day before the Lehigh University Career Fair on September 14th. Employers and students will be able to meet in a personal...

MOPTA aims at bringing together a diverse group of people from both discrete and continuous optimization, working on both theoretical and applied aspects. There will be a small number of invited talks from distinguished speakers and contributed talks...

Please take the opportunity to visit the ISE Department, have a drink with your fellow classmates and professors and meet graduating students! RSVP to aeb213@lehigh.edu if you are planning to stop by!

Please join the ISE Department at the annual ISE Banquet! This year's cocktail reception & banquet will be held in the ASA Packer Dining Room, University Center. The Cocktail Reception will begin at 5:30 pm and then the banquet at 6:30 pm!

...**Responsible Business – Rebuilding Trust and Creating Value** Over the last few decades, there's been a steady erosion of public trust in government and business institutions. In recent years, some of the biggest names in business have...

ETEM-SG and ETEM-SC are recently proposed bottom-up models specifically designed to study the long term impact on urban energy systems of the Smart Grid (SG) and Smart City (SC) developments. These models can be considered as an outgrowth of the...

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...

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...

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...

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...

Click here to read more about Richard Verma as the recipient of the 2016 ISE Distinguished Alumni Award in Industry

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"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...

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...

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...

In this talk we present an asynchronous multistart algorithm for identifying high-quality local minima. We highlight strong theoretical results that limit the number of local optimization runs under reasonable assumptions. Though the results are valid...

All are welcome to attend! Come and ask questions to the best in the field!

Our Advisory Council Members include:

Ray Hoving '69, '71G - Formerly of Bernard Hodes Group

Richard Simek '94, '95G - Hypertherm, Inc.

Karen...

ADMM algorithms have been applied to a variety of problems in the last few years due in part to: the simplicity of the iteration and the ability to exploit problem structure. In this talk, we present an ADMM algorithm for the class of convex quadratic...

Over the last several years I have developed a variety of algorithms for faster solution of mixed-integer linear programs, based on a number of insights about branch and bound methods. The seminar will review the insights, the resulting algorithms,...

**Public Lecture **

Computational Progress in Linear and Mixed Integer Programming

We will look at progress in Linear Programming (LP) and Mixed Integer Programming (MIP) software over the last 25 years. As a result of this...

Please register here: https://www.eventville.com/catalog/eventregistration1.asp?eventid=1011619

Lehigh’s Industrial and Systems Engineering (ISE) Council...

In this talk, we will present our recent work on two fundamental optimization problems in electric power systems, namely the optimal power flow (OPF) problem and the unit commitment (UC) problem. The first one is highly non-convex, and the second one...

We consider distributionally robust (DR) single-server scheduling problem variants with a fixed sequence of appointments with random no-shows and service durations. The joint probability distribution is ambiguous and only the support and first moments...

In this talk, we consider two online optimization problems. The first one is the online linear programming problem. In this problem, the underlying optimization problem is a linear program, however, its constraint matrix is revealed column by column...

MOPTA aims at bringing together a diverse group of people from both discrete and continuous optimization, working on both theoretical and applied aspects. There will be a small number of invited talks from distinguished speakers and contributed talks...

Wing shape is a crucial characteristic that has a large impact on aircraft performance. Wing design optimization has been an active area of research for several decades, but achieving practical designs has been a challenge. One of the main challenges...

We consider a Poisson hail on an infinite d-dimensional ground. In other words, there is a Poisson rain of “hailstones” of a random size (height+width). In the case of a cold ground, we analyze conditions for at most linear growth. In the case of a...

Please join the ISE Department at the annual ISE Banquet.

A Cocktail Reception will begin at 5:00 p.m. in the Siegel Gallery, Iacocca Hall followed by the Banquet Dinner at 6:00 p.m. in the Wood Dining Room, Iaccocca Hall.

Inventory Control in Assemble-to-Order Systems with Identical Lead Times: Lower bound, Control Policies, and Asymptotic Analysis

Assemble-to-order (ATO) is a widely-adopted supply-chain strategy to facilitate product variety, mitigate demand...

This paper discusses a number of important spatial optimization problems, including path routing and location planning, highlighting how they have evolved from simplified expressions to more advanced formalizations. Computing, enhanced data and GIS (...

Step Down Units (SDUs) provide an intermediate level of care between the Intensive Care Units (ICUs) and the general medical-surgical wards. Because SDUs are less richly staffed than ICUs, they are less costly to operate; however, they also are unable...

Flexibility from energy storage and flexible load aggregations is essential to renewable energy integration. The broad adoption of storage in power systems is hindered by its cost and awkward regulatory rules. In this talk, we present a new financial...

We provide a new proof of the fact that Mathematical Programming is Turing-complete, and show how it can be useful in the analysis of code, by presenting two applications. The first aims to find hard inputs for given programs. The second finds...

Public Lecture - "Succeding with Business Analytics - Key Challenges"

Big Data, Analytics, Data-driven decisions, predictive modeling, machine learning and so on. All these terms have become ubiquitous in our daily lives. We hear about the...

**Public Lecture - "Cognitive Computing: Behind and Beyond Jeopardy!"**

In 2011 a computer named Watson demonstrated super human question answering ability by defeating two Jeopardy! grand champions marking the...

In this work we show that randomized (block) coordinate descent methods can be accelerated by parallelization when applied to the problem of minimizing the sum of a partially separable smooth convex function and a simple separable convex function....

In the interconnected world of today, large-scale networked systems are ubiquitous. Some examples include communication networks, electricity grid and sensor networks. In this talk, we describe two recent results related to these networked systems....

Decision-making problems that arise in complex systems (e.g., power grids, emergency logistics, communications networks and supply chains) invariably involve uncertainty and risk. These problems are further complicated by the combinatorial nature of...

Massive graph datasets are used operationally by providers of internet, social network and search services. Sampling can reduce storage requirements as well as query execution times, while prolonging the useful life of the data for baselining and...

Nature loves symmetry. Artists love symmetry. People love symmetry. Mathematicians and computer scientists also love symmetry, with the only exception of mathematical programmers, who always want to break it. Why? Symmetry is of great help in...

This work studies the management of compliance with sustainability requirements in a supply chain in a business environment in which monitoring and legal enforcement of penalties is challenging. This situation is relevant to firms operating in...

We study the performance of linear and piecewise-linear decision rules for adaptive optimization problems based only on the geometry of uncertainty sets. In particular, we show that Minkowski Symmetry and Banach-Mazur distance play a signicant role in...

Sampling large operational datasets such as ISP usage measurements can be effective for reducing storage requirements and execution time, while prolonging the useful life of the data for baselining and retrospective analysis. Sampling needs to mediate...

On the solution of Stochastic Optimization and Variational Problems in Imperfect Information Regimes

A convex feasibility problem is concerned with finding a point in the intersection of convex sets. These problems are fundamental in optimization because any convex optimization problem can be cast in this form. Elementary algorithms are iterative...

The pooling problem is a challenging non-convex optimization problem that is motivated by refinery processes in the petroleum industry, and also finds application in other areas, such as waste water treatment, emissions regulation and agricultural...

Over the past 10 years there has been a growing body of work at the intersection of mathematical programming as commonly studied in Operations Research and constraint programming (CP) with its origins in Artificial Intelligence (AI) and programming...

A major challenge for machine learning in the next decade is the development of methods that continuously predict and learn from a stream of data. The scale of data and the non-stationary nature of the environment make the prevalent "learn from a...

Bayesian statistical models can be used to represent the beliefs of a decision-maker about an uncertain environment. For example, in revenue management, a seller formulates beliefs about customers' willingness to pay; in energy, we may have a belief...

For efficient and physically reliable numerical computations for time dependent differential equations it is very important to find positively invariant subsets of the state space and determine time step sizes of the numerical method that guarantee...

For all-quadratic problems (without any linear constraints), it is well known that the semidefinite relaxation coincides basically with the Lagrangian dual problem. Here we study a more general case where the constraints can be either quadratic or...

The first part of this talk reviews some modern randomized linear algebra techniques. The goal of these methods is to perform approximate matrix multiplication or matrix factorizations (e.g., SVD) with lower computational cost than conventional...

**Public Lecture - "New Goals for American Corporations"**

What should we expect of our great American Corporations? Is the present dominant corporate goal of maximizing return to the shareholders the right answer to that question...

The role of hospital bed management staff and processes has gained increased attention in recent years due to the impact of bed management practices on hospital performance metrics including average boarding time, patient safety, overflow rate, and...

A symmetric matrix is called copositive, if it generates a quadratic form taking no negative values over the positive orthant. Contrasting to positive-semideniteness, checking copositivity is NP-hard. In a copositive optimization problem, we have to...

We will explore the development of efficient batch optimization algorithms for solving large-scale statistical learning applications; particularly those that can be formulated as a nonlinear programming problem. We rst investigate smooth,...

We study the design of reliably connected networks. Given a graph with arcs that may fail at random, the goal is to select a minimum cost set of arcs such that a connectivity requirement is met with high probability. We first compare this model with a...

Set functions, i.e., real mappings form the family of subsets of a nite set to the reals are known and widely used in discrete mathematics for almost a century, and in particular in the last 50 years. If we replace a finite set with its characteristic...

The software systems commonly used to solve linear and integer programming problems today make use of oating-point computation and the inexactness of these computations can lead to errors in the returned results. Although such numerical errors are...

How should the Centers for Disease Control and Prevention revise national immunization recommendations so that gaps in vaccination coverage will be filled in a cost-effective manner? What is the most cost-effective way to use limited HIV prevention...

One of the core technologies for obtaining protein mixtures is provided by the two dimensional polyacrylamide gel electrophoresis (2D-gel). In order to analyze variations in the protein gels obtained from different groups that account for biological...

Public Lecture - "Optimal and Near Optimal Supply Policy for Deterministic Multiperiod Supply Networks"

Click here to view the abstract.

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Public Lecture - "Industrial Engineering - Quo Vadis? One Man’s Idiosyncratic View of His Profession"

Industrial engineering was born in the work of F. W. Taylor at the Midvale Steel Company, and grew under the moniker of “scientific management...

Public Lecture - "How an Engineering Education affected my career"

Mr. Young will discuss how his Industrial Engineering education helped him function in...

Public Lecture - "The Role of Embedded Optimization in Smart Systems and Products"

Many current products and systems employ sophisticated mathematical algorithms to automatically make complex decisions, or take action, in...

Public Lecture - "What Does a Rocket Scientist Really Do?"

When describing something dreadfully simple or blatantly obvious we often proclaim "It is Not Rocket Science." Yet while we may readily agree that "rocket science"...