Spring 2017

Monday, January 30th
(See posters in Tome Hall for talk title & abstract)

11:30am
Tome 115
Lunch provided

Wednesday, February 1st
(See posters in Tome Hall for talk title & abstract)

11:30am
Tome 115
Lunch provided

Monday, February 6th
(See posters in Tome Hall for talk title & abstract)

11:30am
Tome 115
Lunch provided

Friday, February 10th
(See posters in Tome Hall for talk title & abstract)

11:30am
Tome 115
Lunch provided

Tuesday, February 14th
(See posters in Tome Hall for talk title & abstract)

Noon
Tome 115
Lunch provided

Thursday, February 16th
(See posters in Tome Hall for talk title & abstract)

Noon
Tome 115
Lunch provided

Friday, February 17th
(See posters in Tome Hall for talk title & abstract)

11:30am
Tome 115
Lunch provided

Tuesday, March 28th
Heidi Goodson, Haverford College
One Great Sequence!

Number theory is sometimes called the "Queen of Mathematics" because it is so foundational: it is primarily the study of properties of integers. Often this means studying prime numbers or solutions to Diophantine Equations, but in this talk we'll investigate sequences of numbers. Our starting point will be a classic tiling problem. From there we will (literally) spiral through a tale of a surprisingly ever-present sequence.

Noon
Tome 115
Lunch provided

Wednesday, April 12th
Poster Session - 4:30pm in HUB Social Hall

Poster titles & abstracts due by Wednesday, April 5th (email wissj@dickinson.edu)

Tuesday, April 18th
Professor Dick Forrester, Dickinson College
"Assigning Students to Schools to Minimize Socioeconomic Variation between Schools: An Introduction to Optimization Modeling"

Numerous studies have found that a student's academic achievement is as much determined by the socioeconomic composition of their school as their own socioeconomic status. In this talk we provide a methodology for assigning students to schools so as to balance the socioeconomic compositions of the schools while taking into consideration the total travel distance. Our technique utilizes a bi-objective general 0-1 fractional program that is linearized into a mixed 0-1 linear program which can be submitted directly to a standard optimization package. If you didn't understand that last sentence, don't worry, the purpose of this talk is to introduce you to optimization modeling.  As a test case for our approach we analyze data from the Greenville County School District in Greenville, South Carolina.

Noon
Tome 115
Lunch provided

Tuesday, April 25th
Computer Science Honors Presentation- James Midkiff
"Heuristic Approaches to Nonlinear 0-1 Knapsack Problems"

The classical 0-1 knapsack problem is a challenging discrete optimization problem of maximizing the value of items to be selected to include in a “knapsack” of limited capacity. We examine several extensions of the 0-1 knapsack problem that include nonlinear terms. In particular, we examine the 0-1 cubic knapsack problem, the 0-1 cubic multiple knapsack problem, the 0-1 fractional knapsack problem, and the 0-1 maximum probability problem. Exact solution approaches for solving these nonlinear problems are only effective on smaller-sized instances. In this research, we develop numerous heuristic approaches, including a tabu search algorithm, a simulated annealing algorithm, and a genetic algorithm for each problem type.  These algorithms have the advantage of being able to find good quality solutions to large instances in a very efficient manner. We perform a detailed computational study to examine the effectiveness of our techniques.

Noon
Tome 115
Lunch provided

Thursday, April 27th
Computer Science Honors Presentation - Peixin Sun
"Construction of Test Problems for the 0-1 Quadratic Knapsack Problem"

First introduced by Gallo et al., the 0-1 quadratic knapsack problem (QKP) is an optimization problem which maximizes a quadratic objective function subject to a single knapsack constraint. Gallo et al. randomly generated instances of the QKP in a particular manner that has been adopted almost universally by all the subsequent authors studying the QKP. However, it has been observed that generating QKP instances following this approach yields problems with highly variable and unpredictable difficulty. The goal of this research is to develop a methodology to generate instances of the QKP with a predictable and consistent level of difficulty. In this talk, we examine different factors that influence the difficulty level of the QKP and develop a number of different methods for constructing test problems. We also introduce a technique for constructing problems with a specified correlation between the objective and knapsack coefficients.

Noon
Tome 117
Lunch provided

Wednesday, May 10th
Computer Science Senior Symposium
Title & abstract TBD

9:30am-Noon
Tome 115
Lunch provided

Wednesday, May 10th
Math/CS BBQ

Noon
Rector Courtyard (Rector Atrium - rain location)
Professors will grill hot dogs, hamburger & veggie burgers and side dishes will be provided.