
THE
FIELDS INSTITUTE FOR RESEARCH IN MATHEMATICAL SCIENCES


201314
Fields
Industrial Optimization Seminar
at 5:00 p.m.
at
the Fields Institute, 222 College St., Toronto
Map
to Fields


The inaugural meeting of the Fields Industrial Optimization Seminar
took place on November 2, 2004. The seminar meets in the early evening
of the first Tuesday of each month. Each meeting is comprised of
two related lectures on a topic in optimization; typically, one
speaker is a universitybased researcher and the other is from the
private or government sector. The series welcomes the participation
of everyone in the academic or industrial community with an interest
in optimization – theory or practice, expert or student . Please
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informed of upcoming seminars.
The Fields Institute makes a video record of this seminar through
FieldsLive. If you make a presentation
to the Seminar, the Institute will be videorecording the presentation
and will make the video record
available to the public.
Upcoming
Seminars
Talks streamed live at FieldsLive

October 14

Tamás Terlaky

December 2

TBA 
Past
Seminars 
May 20 
5:00 p.m.
Thomas Adams, McMaster University
Green power plants of the future (slides)
Although it is possible to capture CO2 emissions from stateoftheart
natural gas and coal power plants, it is extremely expensive
and energy intensive to do so. Instead, future green power
plants will produce electricity without combustion in air
such that CO2 capture is considerably easier by design.
One promising possibility is a solid oxide fuel cell power
plant integrated with compressed air energy storage. This
proposed system has the capability to achieve 100% carbon
capture while raising or lowering the power output according
to demand. However, in order to make the system work efficiently
and economically, a rolling horizon optimization (RHO) control
strategy can be applied. The RHO determines the amount of
energy to store or release in real time by considering information
such as the current state of the system, current and predicted
prices of electricity, and current and predicted electricity
demands from the grid. The RHO can be modified to achieve
either performance or economic objectives, with very different
results in behaviour. Overall, the system is quite successful
at meeting both environmental and grid performance objectives
with only small price premiums over the status quo.
6:00 p.m.
Adam Warren, National Renewable Energy Laboratory
REOpt: Renewable Energy Integration and Optimization
REopt is an energy planning platform offering concurrent,multiple
technology integration and optimization capabilities to
help clients meet their cost savings and energy performance
goals. The REopt platform provides technoeconomic decision
support for project screening and energy asset operation.
REopt employs an integrated approach to optimizing the energy
costs of a site by considering electricity and thermal consumption,
resource availability, and complex tariff structures , incentives,
and interconnection limitations.Formulated as a mixed integer
linear program, REopt recommends an optimallysized mix
of conventional and renewable energy, and energy storage
technologies; estimates the net present value associated
with implementing those technologies; and provides the costoptimal
dispatch strategy for operating them at maximum economic
efficiency.

March
4 
5:00 p.m.
Adrian Nachman, University of Toronto
A Variational Problem Arising in Conductivity Imaging from
Interior Measurements
Imaging electric conductivity of tissue is both desirable
and challenging. The classical Electric Impedance Tomography
Problem seeks to determine the conductivity from measurements
of voltages and currents at the boundary; it has spurred
deep and farreaching mathematical developments. The illposedness
of the problem is now well understood, and places severe
limitations on the resolution that can be achieved. We will
discuss one approach to overcome these limitations: using
interior current density data obtainable by a method pioneered
by Joy, Scott and Henkelmann at the University of Toronto
which makes use of Magnetic Resonance Imagers in a novel
way.
We only require knowledge of the magnitude J of one current
for a given voltage f on the boundary. We show that the
corresponding electric potential is the unique solution
of a constrained minimization problem with respect to a
weighted total variation functional defined in terms of
the physical data. Working with the dual variational problem
leads naturally to an alternating split Bregman algorithm,
for which we prove convergence. The dual problem also turns
out to yield a novel method to recover the full vector field
J from knowledge of its magnitude, and of the voltage f
on the boundary. Time permitting, we will discuss the corresponding
problem for anisotropic conductivities.
The results presented are from joint work with Nicholas
Hoell, Robert Jerrard, Amir Moradifam, Alexandru Tamasan
and Alexander Timonov. Experimental results are joint work
with Nahla Elsaid, Michael Joy, Weijing Ma, and Tim DeMonte.
6:00 p.m.
Douglas C. White, Emerson Process Management
Process Plant Optimization in Real Time: Energy and Environmental
Interactions
Many industrial plants produce products worth millions
of dollars per day and continuous financial optimization
of their operations is obviously attractive. Applications
of real time optimization technology in the process industries
have been attempted for at least the last fifty years; sometimes
successfully, sometimes not. In this presentation there
will be a short review of the history of these attempts
and some of the lessons learned. With the global increase
in energy costs and environmental regulations, a current
focus is real time optimization of the very complex energy
systems in these large industrial sites and the associated
environmental impact. Optimization has to fit within the
overall control hierarchy and structure at the site which
creates special system requirements. The problem structure,
issues and current status of these applications is presented
as well as open questions that are topics for future developments.

December
3, 2013

Laura Sanita, University of Waterloo (slides)
Finding small stabilizers for unstable graphs
A vertex v of a graph G is called inessential if there
exists a maximum matching in G that exposes v. G is said
to be stable if the set of its inessential vertices forms
a stable set. Stable graphs play a key role in network bargaining
games where we are given a set of players represented as
vertices of a graph G, and a set of possible deals between
players represented by the edges of G. Kleinberg and Tardos
[STOC ’08] defined the notion of a balanced outcome
for a network bargaining game, and proved that a balanced
outcome exists if and only if the correspondent graph G
is stable. This connection motivates the optimization problem
of finding a minimum cardinality stabilizer of a given unstable
graph G, that is a subset of edges F such that G \ F is
stable. In this talk we prove some structural results about
this problem and develop efficient approximation algorithms
for sparse graphs. Joint work with A. Bock, K. Chandrasekaran,
J. Koenemann, and B. Peis.
Ritchie (Yeqi) He, Royal Bank of Canada
An Improved Model for Calculation of Debt Specific Risk
VaR with Tail Fitting
Initially introduced in the 1996 Amendment of Basel Accord,
the specific (or spread) risk of a debt portfolio (DSR)
is the risk due to changes of idiosyncratic credit spreads
(bond spreads or CDS spreads) related to individual entities.
Financial institutions are allowed to use internal models
to calculate the ValueatRisk (VaR) of DSR. Internal models
usually calculate portfolio DSR PnL based on an assumption
that idiosyncratic credit spreads follow a tractable closedform
joint distribution such as multivariate normal or student's
tdistribution. This assumption may not give a satisfactory
approximation to the joint distribution because the marginal
distribution of idiosyncratic credit spreads usually has
fat tails. To better model fat tails, we propose an improved
Monte Carlobased model to calculate the DSR VaR. In the
proposed model, the marginal distribution is modeled by
a normal kernel distribution with Pareto tails, and the
dependence structure of idiosyncratic credit spreads are
captured by a student's t copula. For Pareto tails, the
calibration of shape parameters and scale parameters involves
a series of density fitting problems, which are solved by
the maximum likelihood estimation. Numerical results show
that, the proposed model is capable to generate more accurate
distributions, and consequently the quality of estimation
of the DSR VaR is improved.
Joint work with Meng Han, Royal Bank of Canada.
Please click here for Dr. He's presentation

September 24, 2013

3:15
 4:05 p.m.
Jonathan Briggs (Canada Pension Plan Investment Board)
Video of talk
A Portfolio Construction Toolkit
In the practitioner world, the value of portfolio construction
is often viewed with skepticism  a skepticism born of an
honest assessment of the dubious forecasting power of the
inputs and the opacity of the process. Despite all our wonderful
mathematical gymnastics, if we don’t know the nature
what is consumed, how can we possibly truthfully convey
the nature of what we create? Now suppose we knew the distributions,
the dynamics, the Information Ratio (IR) and the interrelationships
between a multifactor model and its returns, and further
we could disentangle each twist and turn of the raw factors
as they are transformed into trades and holdings? Well,
then maybe we really could demonstrate the value of portfolio
construction.
_____________
4:05  4:55 p.m.
Bogie Ozdemir (Sun Life Financial Group) Video
of talk
Capital and Business Mix Optimization
Basel III amounts to a climate change in the banking industry.
It increased the capital requirements significantly  especially
for certain businesses (most notably Capital Markets) and
decreased the acceptable forms of capital. Capital has become
a scarce resource under Basel III, putting significant downward
pressure on ROE. In this new environment, banks will need
to change their business mixes, exit or shrink capital heavy
businesses and adjust their operating models, while meeting
income targets. During this course correction their ROE
and Income Targets will be challenged further as some rebalancing
of operating models may compromise short term income to
improve ROE in future years. Subject to more onerous capital
requirements under Basel III banks will need to increase
the efficiency of capital utilization and place greater
emphasis on optimizing capital allocation and business mix
across their operations. In this presentation, we will discuss
how to establish an optimization framework incorporating
both economic and regulatory capital.

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