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DTSTART:20200308T030000
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DTSTART:20201101T010000
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DTSTART:20211107T010000
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BEGIN:VEVENT
SUMMARY:COCANA seminar: Modelling Rare Blood in Canada from John Blake
DTSTART:20211025T173000Z
DTEND:20211025T183000Z
UID:24
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:John Blake\nTitle: Modelling Rare Blood in Canad
a\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ
6YTRtdz09\nAbstract: BACKGROUND: Many countries maintain rare blood progr
ams to provide access to blood for patients with complex serology. These
include a process to screen donors and a registry to record information ab
out rare donors. Blood agencies may also freeze rare blood. However, froze
n blood storage is 2-10 times more expensive than liquid blood. \n\nSTUDY
DESIGN AND METHODS: A two-phase approach to analysis was used to evaluate
how rare a blood type must be before a frozen inventory is necessary and
what screening rates are required to support a rare blood program. A simul
ation model was employed to evaluate the impact of inventory on patient ac
cess.\n\nRESULTS: Results suggested that, for 24 of 29 named phenotypes m
anaged by Canadian Blood Services, insufficient donors had been identified
to ensure a stable inventory. Analytic results showed the screening rate
necessary to ensure a stable inventory and the timeframe to build a rare
donor base.\n29 simulation scenarios were executed to evaluate patient acc
ess to rare blood against inventory levels. Simulation results show that s
ome amount of frozen inventory is necessary for phenotypes rarer than 1 in
3,000. However, holding more than 2 units apiece of (O-, O+, A-, A+) did
not improve patient access.\n\nCONCLUSION: While some level of frozen blo
od is needed for rare blood, large inventories do not improve access. Mode
st amounts of frozen inventory, combined with increased door screening, pr
ovides the greatest chance of maximizing patient access.\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Scheduling with communication delays from Sami Davi
es
DTSTART:20211108T183000Z
DTEND:20211108T193000Z
UID:25
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Sami Davies\nTitle: Schedu
ling with communication delays\nLocation: https://sfu.zoom.us/j/6632213855
7?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: We study scheduling with
precedence constraints and communication delays. Here, if two dependent j
obs are scheduled on different machines, then c time units must pass betw
een their executions. Previously, the best known approximation ratio was O
(c), though an open problem in the top-10 list by Schuurman and Woeginger
asks whether there exists a constant-factor approximation algorithm. We gi
ve a polynomial-time O(log c * log m)-approximation algorithm when given m
identical machines and delay c for minimizing makespan. Our approach uses
a Sherali-Adams lift of an LP relaxation and a clustering of the semimetr
ic space induced by the lift. We also (1) obtain similar polylogarithmic a
pproximations on related machines with the objective of minimizing the wei
ghted sum of completion times and (2) found that if the delay can vary bet
ween pairs of dependent machines, then a constant factor approximation alg
orithm is not possible (assuming NP complete problems do not admit quasi-p
olynomial time algorithms).
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Image Reconstruction: Superiorization versus Regul
arization from Warren Hare
DTSTART:20211122T183000Z
DTEND:20211122T193000Z
UID:28
LOCATION:https://ubc.zoom.us/j/69073057434?pwd=WUprZnU3NE9wbWdnVWNRT0Z0SDE4
Zz09
DESCRIPTION:War
ren Hare\nTitle: Image Reconstruction: Superiorization versus Regular
ization\nLocation: https://ubc.zoom.us/j/69073057434?pwd=WUprZnU3NE9wbWdnV
WNRT0Z0SDE4Zz09\nAbstract: Image reconstruction plays an important role in
a number of modern activities, such as experimental verification of radio
therapy. \nMathematically, image reconstruction can be viewed as a least s
quares problem, but with the caveat that the `optimal' solution is not nec
essarily the cleanest image. Several approaches have been suggested to de
al with this issue. In this talk, we review some of these approaches and
present the results of comprehensive numerical testing comparing different
approaches.\n\n\nbased on Joint work with M. Guenter, S. Collins, A. Ogil
vy, A. Jirasek\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: SGD in the Large: Average-case Analysis, Asymptotic
s, and Stepsize Criticality from Courtney Paquette
DTSTART:20211206T183000Z
DTEND:20211206T193000Z
UID:32
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Courtney Paquette\n
Title: SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize
Criticality\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNU
GU5aGs3TUZ6YTRtdz09\nAbstract: In this talk, I will present a framework, i
nspired by random matrix theory, for analyzing the dynamics of stochastic
gradient descent (SGD) when both the number of samples and dimensions are
large. Using this new framework, we show that the dynamics of SGD on a lea
st squares problem with random data becomes deterministic in the large sam
ple and dimensional limit. Furthermore, the limiting dynamics are governed
by a Volterra integral equation. This model predicts that SGD undergoes a
phase transition at an explicitly given critical stepsize that ultimately
affects its convergence rate, which we also verify experimentally. Finall
y, when input data is isotropic, we provide explicit expressions for the d
ynamics and average-case convergence rates. These rates show significant i
mprovement over the worst-case complexities.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Improving Stroke Routing Protocol from Dr. Amir Ard
estani-Jaafari
DTSTART:20220124T183000Z
DTEND:20220124T193000Z
UID:29
LOCATION:https://ubc.zoom.us/j/66022642862?pwd=dU4wMkkvSmNheWYwQVpUekVBN0l3
Zz09
DESCRIPTION:Dr. Amir Ardestani-Jaa
fari\nTitle: Improving Stroke Routing Protocol\nLocation: https://ubc.
zoom.us/j/66022642862?pwd=dU4wMkkvSmNheWYwQVpUekVBN0l3Zz09\nAbstract: Curr
ently, stroke patients are transported to the nearest stroke center. Many
factors, including the spatial variation in population density, the stroke
's severity, the time since stroke onset, and the congestion level at the
receiving stroke center are not considered in the current protocol. We dev
elop an analytical framework that enriches the stroke transport decision-m
aking process by incorporating these factors. Applying our framework to th
e city of Montreal Stroke Network, we show that adopting a triage strategy
leads to significantly improved health outcomes.\n\nKeywords: Stroke, Que
uing Optimization, Congestion, Facility Location\n
DTSTAMP:20220117T190038Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Stable, accurate and efficient deep neural networks
for reconstruction of gradient-sparse images from Maksym Neyra-Nesterenko
DTSTART:20220214T183000Z
DTEND:20220214T193000Z
UID:39
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Maksym Neyra-Nesterenko \nT
itle: Stable, accurate and efficient deep neural networks for reconstructi
on of gradient-sparse images\nLocation: https://sfu.zoom.us/j/66322138557?
pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Deep learning has recently
shown substantial potential to outperform standard methods in compressive
imaging, an inverse problem where one reconstructs an image from highly u
ndersampled measurements. Given compressive imaging arises in many importa
nt applications, such as medical imaging, the potential impact of deep lea
rning is significant. Empirical results indicate deep learning achieves su
perior accuracy on data for such tasks. However, a theoretical treatment e
nsuring the stability of deep learning for compressive imaging is mostly a
bsent in the current literature. In fact, many existing deep neural networ
ks designed for these tasks are unstable and fail to generalize.\n\nIn thi
s talk, we present a novel construction of accurate, stable and efficient
neural networks to reconstruct images under a gradient sparsity model. Thi
s is based on recent work by Adcock et al. (2021) and Antun et al. (2021).
We construct the network by unrolling an optimization algorithm similar t
o NESTA. We utilize a compressed sensing analysis to prove accuracy and st
ability of the network. This shows that deep neural networks are capable o
f performing as well as state-of-the-art compressive imaging techniques fo
r gradient-sparse images. A restart scheme enables exponential decay of th
e required network depth, yielding a shallower network. In turn this reduc
es computational costs, making the network feasible for fast image reconst
ruction.\n\nThis is joint work with Ben Adcock.\n
DTSTAMP:20220208T155947Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Feasibility-based structured nonconvex optimization
(Manish Krishan-Lal) / Positive bases with maximal cosine measure (Gabrie
l Jarry-Bolduc) from Manish Krishan-Lal /Gabriel Jarry-Bolduc
DTSTART:20220228T183000Z
DTEND:20220228T193000Z
UID:41
LOCATION:https://ubc.zoom.us/j/65210691922?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJx
UT09
DESCRIPTION:Manish Krishan-
Lal /Gabriel Jarry-Bolduc\nTitle: Feasibility-based structured nonconv
ex optimization (Manish Krishan-Lal) / Positive bases with maximal cosine
measure (Gabriel Jarry-Bolduc)\nLocation: https://ubc.zoom.us/j/6521069192
2?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJxUT09\nAbstract: Feasibility-based struct
ured nonconvex optimization: We present the onset of the theory to find cl
osed-form projection onto conics and quadrics in Hilbert spaces. These set
s are crucial in many applications. We will focus on a feasibility-based f
ramework that utilizes these sets, to train a neural network with projecti
on methods.\n\nPositive bases with maximal cosine measure: The properties
of positive bases make them a useful tool in derivative-free optimization
and an interesting concept in mathematics. The notion of cosine measure h
elps to quantify the quality of a positive basis. It provides information
on how well the vectors in the positive basis uniformly cover the space co
nsidered. In this talk, we present recent progress on how to compute the c
osine measure of a given positive basis and the structure of a positive ba
sis with maximal cosine measure.
DTSTAMP:20220224T155826Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Non-realizability of polytopes via linear programmi
ng from Amy Wiebe
DTSTART:20220314T173000Z
DTEND:20220314T183000Z
UID:46
LOCATION:https://ubc.zoom.us/j/65210691922?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJx
UT09
DESCRIPTION:Amy Wiebe\nTitle: Non-re
alizability of polytopes via linear programming\nLocation: https://ubc.zoo
m.us/j/65210691922?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJxUT09\nAbstract: A class
ical question in polytope theory is whether an abstract polytope can be re
alized as a concrete convex object. Beyond dimension 3, there seems to be
no concise answer to this question in general. In specific instances, answ
ering the question in the negative is often done via “final polynomials
introduced by Bokowski and Sturmfels. This method involves finding a po
lynomial which, based on the structure of a polytope if realizable, must b
e simultaneously zero and positive, a clear contradiction. The search spac
e for these polynomials is ideal of Grassmann-Plücker relations, which qu
ickly becomes too large to efficiently search, and in most instances where
this technique is used, additional assumptions on the structure of the de
sired polynomial are necessary. \n\nIn this talk, I will describe how by c
hanging the search space, we are able to use linear programming to exhaust
ively search for similar polynomial certificates of non-realizability with
out any assumed structure. We will see that, perhaps surprisingly, this el
ementary strategy yields results that are competitive with more elaborate
alternatives and allows us to prove non-realizability of several interesti
ng polytopes. \n
DTSTAMP:20220301T002819Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: A Colorful Steinitz Lemma Applied to Block Integer
Programs from Joseph Paat
DTSTART:20220328T173000Z
DTEND:20220328T183000Z
UID:40
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Joseph P
aat \nTitle: A Colorful Steinitz Lemma Applied to Block Integer Progra
ms\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TU
Z6YTRtdz09\nAbstract: Block integer programs (IPs) model a wide range of p
roblems including those in social choice, scheduling, and stochastic optim
ization. Recently, algorithms for block IPs have been improved by using th
e so-called Steinitz Lemma, which is a statement about the rearrangement o
f a set of vectors. In this work, we develop a variation of the Steinitz L
emma that rearranges multiple sets simultaneously. We then demonstrate how
our variation can be used to derive new results for block IPs.\n\nThis is
joint work with Timm Oertel and Robert Weismantel.
DTSTAMP:20220318T211104Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Geometry of All Pivot Rules for the Simplex Me
thod from Jesus de Loera
DTSTART:20220404T173000Z
DTEND:20220404T183000Z
UID:42
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Jesus de Loera
\nTitle: The Geometry of All Pivot Rules for the Simplex Method\nLoc
ation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtd
z09\nAbstract: The simplex method is one of the most famous and popular al
gorithms in optimization. Purely geometrically, a linear program (LP) is a
polyhedron together with an orientation of its graph. The famous simplex
method selects a path from an initial vertex to the sink (optimum) and th
us determines an arborescence (of shortest monotone paths). The engine of
any version of the simplex method is a pivot rule that selects the outgoin
g arc for a current vertex. Pivot rules come in many forms and types, but
after 80 years we are still ignorant whether there is one that can make th
e simplex method run in polynomial time. Motivated by this question we tri
ed to understand the structure of all pivot rules for a linear program.\n\
nClassic result in parametric linear optimization explain the changes of o
bjective function, eg the orientation and Sink and for a pivot rule its ar
borescence. I present a type of parametric analysis for all pivot rules be
longing to a certain class, memoryless pívot rules, we associate polytope
s that parametrize memoryless pívot rules of a given LP, an attempt to ge
t a geometric/topological picture of a “space of all pivot rules of an
LP”. This story is related to performance of pivot rules, parametric lin
ear programs, shadow-vertex-rules, and several classic polyhedral geometry
and is joint work with Alex Black, Niklas Lu ̈tjeharms, and Raman Sanya
l. \n
DTSTAMP:20220330T200239Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Cells in the Box and a Hyperplane from Imre Bárán
y
DTSTART:20220929T223000Z
DTEND:20220929T233000Z
UID:53
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Imre Bárány\nTitle: Cells in the Box and a Hyp
erplane\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5a
Gs3TUZ6YTRtdz09\nAbstract: It is well known that a line can intersect at m
ost 2n-1 cells of the n x n chessboard. What happens in higher dimensions:
how many cells of the d-dimensional [0,n]^d box can a hyperplane intersec
t? We also prove the integer analogue of the following fact. If K, L are c
onvex bodies in R^d and K is contained in L, then the surface area K is sm
aller than that of L. Joint work with Peter Frankl.
DTSTAMP:20220928T214148Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Maximum Entropy on the Mean Method for Linear I
nverse Problems (and beyond) from Tim Hoheisel
DTSTART:20221013T223000Z
DTEND:20221013T233000Z
UID:54
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Tim Hohei
sel\nTitle: The Maximum Entropy on the Mean Method for Linear Inverse
Problems (and beyond)\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGY
xeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: The principle of ‘maximum entro
py’ states that the probability distribution which best represents the c
urrent state of knowledge about a system is the one with largest entropy w
ith respect to a given prior (data) distribution. It was first formulated
in the context of statistical physics in two seminal papers by E. T. Jayne
s (Physical Review, Series II. 1957), and thus constitutes an information
theoretic manifestation of Occam’s razor. We bring the idea of maximum e
ntropy to bear in the context of linear inverse problems in that we solve
for the probability measure which is close to the (learned or chosen) prio
r and whose expectation has small residual with respect to the observation
. Duality leads to tractable, finite-dimensional (dual) problems. A core t
ool, which we then show to be useful beyond the linear inverse problem set
ting, is the ‘MEMM functional’: it is an infimal projection of the Kul
lback-Leibler divergence and a linear equation, which coincides with Cram
r’s function (ubiquitous in the theory of large deviations) in most cas
es, and is paired in duality with the cumulant generating function of the
prior measure. Numerical examples underline the efficacy of the presented
framework.\n
DTSTAMP:20221004T205347Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Improving Radiotherapy Treatment Logistics from Nad
ia Lahrichi
DTSTART:20221027T223000Z
DTEND:20221027T233000Z
UID:55
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Nadia Lahrichi\nTitle: Improving Radiotherapy Tr
eatment Logistics\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeld
zZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: The main cancer treatments are surger
y, radiation therapy and chemotherapy. The complexity of the logistical pr
ocess of scheduling treatment appointments stems from the fact that it inv
olves extremely costly resources, sometimes synchronously. Several due dat
es (i.e., appointments already scheduled, maximum wait times) and unexpect
ed events such as the arrival of patients requiring urgent palliative care
add to the difficulty. This talk will investigate how can simulation and
optimization models help improve the efficiency of cancer treatment center
s and share experiences on patient booking, physician scheduling, and capa
city assessment. All projects are conducted in close partnership with a ho
spital and rely on real data.
DTSTAMP:20221024T175059Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Case Studies in Data Science and Analytics from a U
K Business School from Jing Lu
DTSTART:20221117T233000Z
DTEND:20221118T003000Z
UID:60
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Jing Lu\nTitle: Case Studies in Data Science and
Analytics from a UK Business School\nLocation: https://sfu.zoom.us/j/6632
2138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Data science invol
ves the collection, management, processing, analysis, visualisation and in
terpretation of huge amounts of data. It is a multi-disciplinary field tha
t integrates systematic thinking, methodology, process and technology to d
evelop intelligence with respect to real-world problems. This presentation
focuses on the business environment and identifies the components of data
science forming a conceptual architecture before proposing a composite da
ta-driven process model. Representative tools and techniques are applied t
o relevant case studies demonstrating innovation in undergraduate programm
e design, customer analytics and the marketing of insurance for example.
DTSTAMP:20221107T224249Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Computing the nearest structured rank deficient mat
rix from Diego Cifuentes
DTSTART:20221110T223000Z
DTEND:20221110T233000Z
UID:57
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Diego Cifuentes\nTitle: Computing the nearest structured rank defic
ient matrix\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNU
GU5aGs3TUZ6YTRtdz09\nAbstract: Given an affine space of matrices L and a m
atrix Θ ∈ L, consider the problem of computing the closest rank deficie
nt matrix to Θ on L with respect to the Frobenius norm. This is a nonconv
ex problem with several applications in control theory, computer algebra,
and computer vision. We introduce a novel semidefinite programming (SDP) r
elaxation, and prove that it always gives the global minimizer of the nonc
onvex problem in the low noise regime, i.e., when Θ is close to be rank d
eficient. Our SDP is the first convex relaxation for this problem with pro
vable guarantees. We evaluate the performance of our SDP relaxation in exa
mples from system identification, approximate GCD, triangulation, and came
ra resectioning. Our relaxation reliably obtains the global minimizer unde
r non-adversarial noise, and its noise tolerance is significantly better t
han state of the art methods.
DTSTAMP:20221108T000132Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Individualized Dynamic Patient Monitoring Under Ala
rm Fatigue from Hossein Piri
DTSTART:20230105T233000Z
DTEND:20230106T003000Z
UID:63
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Hossein Piri\nTitle: Indi
vidualized Dynamic Patient Monitoring Under Alarm Fatigue\nLocation: https
://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstrac
t: Hospitals are rife with alarms, many of which are false. This leads to
alarm fatigue, in which clinicians become desensitized and may inadvertent
ly ignore real threats. We develop a partially observable Markov decision
process model for recommending dynamic, patient-specific alarms in which w
e incorporate a cry-wolf feedback loop of repeated false alarms. Our model
takes into account patient heterogeneity in safety limits for vital signs
and learns a patient’s safety limits by performing Bayesian updates dur
ing a patient’s hospital stay. We develop structural results of the opti
mal policy and perform a numerical case study based on clinical data from
an intensive care unit. We find that compared with current approaches of s
etting patients’ alarms, our dynamic patient-centered model significantl
y reduces the risk of patient harm.\n\nhttps://researchseminars.org/semina
r/SFUOR
DTSTAMP:20230103T202524Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Tensor Optimization and Applications from Tanmaya K
armarkar
DTSTART:20230119T233000Z
DTEND:20230120T003000Z
UID:58
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Tanmaya Karmarkar\nTitle: Tensor Optimization an
d Applications\nLocation: https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudU
l0ODhCdEZ5MFlhVnAxdz09\nAbstract: First example of applying tensor optimiz
ation to combinatorial problems was shown in IPCO 1992: pages 406-420. We
improve and strengthen those results in several ways and obtain computatio
nal results on three problems - graph partitioning, satisfiability and ana
lysis of counterexamples related to Hilbert’s 17th problem. \n\nFor this
we created a mixed symbolic-numeric model formulation package which facil
itates definition of objective function, equality and inequality constrain
ts and definition of new dependent variables.\n\nFor discrete problems cer
tain inequalities valid at candidate solutions are dynamically incorporate
d in the iterations of the continuous optimization algorithm based on unde
rlying non-Newtonian geometry of the interior-point space.\n\nFor graph pa
rtitioning we obtain optimal solutions including proof of optimality. For
satisfiability problem we either find the satisfiable assignment or constr
uct and output proof of unsatisfiability.\n\nFor Hilbert’s 17th problem
we analyse concrete examples whose non-negativity has been stablished to b
e not provable using sums of the squares expressions valid in RN. However,
for these counterexamples, we construct non-negativity proofs by computat
ionally constructing sums of squares expressions valid on certain sub-vari
eties of RN The same modeling package mentioned above is used to post proc
ess the solver output into symbolic proofs of optimality or infeasibility\
n
DTSTAMP:20230118T185318Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Restarts Subject to Approximate Sharpness: a Parame
ter-free and Optimal Scheme for Accelerating First-order Methods from Ben
Adcock
DTSTART:20230126T233000Z
DTEND:20230127T003000Z
UID:67
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Ben Adcock\nTitle: Restarts Subject to Approxima
te Sharpness: a Parameter-free and Optimal Scheme for Accelerating First-o
rder Methods\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdN
UGU5aGs3TUZ6YTRtdz09\nAbstract: Sharpness is a generic assumption in conti
nuous optimization that bounds the distance to the set of minimizers in te
rms of the suboptimality in the objective function. It leads to the accele
ration of first-order optimization methods via so-called restarts. However
, sharpness involves problem-specific constants that are typically unknown
, and previous restart schemes often result in reduced convergence rates.
Such schemes are also challenging to apply in the presence of noise or app
roximate model classes (e.g., in compressed sensing or machine learning pr
oblems). In this talk, we introduce the notion of approximate sharpness, a
generalization of sharpness that incorporates an unknown constant perturb
ation to the objective function error. By employing a new type of search o
ver the unknown constants, we then describe a restart scheme that applies
to general first-order methods. Our scheme maintains the same convergence
rate as when assuming knowledge of the constants. Moreover, for broad clas
ses of problems, it gives rates of convergence which either match known op
timal rates or improve on previously established rates. Finally, we demons
trate the practical efficacy of this scheme on applications including spar
se recovery, compressive imaging and feature selection in machine learning
.
DTSTAMP:20230118T185550Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Number of String C-groups of High Rank from Dim
itri Leemans
DTSTART:20230202T233000Z
DTEND:20230203T003000Z
UID:65
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Dimitri Leemans\nTitle: The Number of String C-g
roups of High Rank\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxel
dzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Abstract polytopes are a combinatori
al generalisation of classical objects that were already studied by the gr
eeks. They consist in posets satisfying some extra axioms. Their rank is r
oughly speaking the number of layers the poset has. When they have the hig
hest level of symmetry (namely the automorphism group has one orbit on the
set of maximal chains), they are called regular. One can then use string
C-groups to study them.\n\nIndeed, string C-groups are in one-to-one corre
spondence with abstract regular polytopes. They are also smooth quotients
of Coxeter groups.\n\nThey consist in a pair (G,S) where G is a group and
S is a set of generating involutions satisfying a string property and an i
ntersection property. The cardinality of the set S is the rank of the stri
ng C-group. It corresponds to the rank of the associated polytope.\n\nIn t
his talk, we will give the latest developments on the study of string C-gr
oups of high rank. In particular, if G is a transitive group of degree $n$
having a string C-group of rank r >= (n+3)/2, work over the last twelve y
ears permitted us to show that G is necessarily the symmetric group S_n.\n
\nWe have just proven in the last months that if n is large enough, up to
isomorphism and duality, the number of string C-groups of rank r for S_n (
with r \geq (n+3)/2) is the same as the number of string C-groups of rank
r+1 for S_{n+1}. \n\nThis result and the tools used in its proof, in part
icular the rank and degree extension, imply that if one knows the string C
-groups of rank (n+3)/2 for S_n with n odd, one can construct from them al
l string C-groups of rank (n+3)/2+k for S_{n+k} for any positive integer k
. \n\nThe classification of the string C-groups of rank r >= (n+3)/2 for
S_n is thus reduced to classifying string C-groups of rank r for S_{2r-3}.
\n\nA consequence of this result is the complete classification of all str
ing C-groups of S_n with rank n-kappa for kappa in {1,...,6}, when n >= 2
kappa+3, which extends previous known results.\n\nThe number of string C-
groups of rank $n-\kappa$, with n >= 2kappa+3, of this classification giv
es the following sequence of integers indexed by kappa and starting at kap
pa = 1: (1,1,7,9,35,48).\n\nThis sequence of integers is new according to
the On-Line Encyclopedia of Integer Sequences.\n\nJoint work with Peter J.
Cameron (University of St Andrews) and Maria Elisa Fernandes (University
of Aveiro)\n\n \n
DTSTAMP:20230118T185808Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: On Combinatorial Algorithms and Circuit Augmentatio
n for Pseudoflows from Angela Morrison
DTSTART:20230216T233000Z
DTEND:20230217T003000Z
UID:68
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Angela Morrison\nTitle: On Combinatorial Algorithms and
Circuit Augmentation for Pseudoflows\nLocation: https://ubc.zoom.us/j/692
41125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: There is a wealth
of combinatorial algorithms for classical min-cost flow problems and thei
r simpler variants like max flow or shortest-path problems. It is well-kno
wn that several of these algorithms are intimately related to the Simplex
method and the more general circuit augmentation schemes. Prime examples a
re the network Simplex method, a refinement of the primal Simplex method,
and (min-mean) cycle canceling, which corresponds to a (steepest-descent)
circuit augmentation scheme over the underlying polyhedron.\n\nWe are inte
rested in deepening and expanding the understanding of the close relations
hip between circuit augmentation and combinatorial network flows algorithm
s. To this end, we generalize from the consideration of primal or dual fea
sible flows to the so-called pseudoflows, which allow for a violation of f
low balance. We introduce what are called ‘pseudoflow polyhedra’, in w
hich slack variables are used to quantify this violation, and characterize
their circuits. This enables us to study various network flows algorithms
in view of the walks that they trace in these polyhedra, and in view of t
he pivot rules used to choose the steps.\n\nIn particular, we show that th
e Successive Shortest Path Algorithm and the Shortest/Generic Augmenting P
ath Algorithm form general, non-edge circuit walks. We also provide a proo
f outline showing that the aforementioned algorithms correspond to a Dantz
ig Rule and Steepest-ascent circuit augmentation scheme respectively.
DTSTAMP:20230125T185140Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Park-and-loop Technician Routing Problem from J
ean-François Cordeau
DTSTART:20230310T233000Z
DTEND:20230311T003000Z
UID:72
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Jean-François Cordeau\nTitle: The Park-and-loop
Technician Routing Problem\nLocation: https://sfu.zoom.us/j/66322138557?p
wd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Motivated by an application
in the routing of technicians at a French public utility, we introduce a
highly efficient heuristic together with a branch-price-and-cut algorithm
for the doubly open park-and-loop routing problem. This problem is an exte
nsion of the classical vehicle routing problem in which routes may involve
a main tour performed by driving a vehicle as well as a set of subtours t
hat are carried out on foot after parking the vehicle. In addition, routes
do not start and end at a central depot, but rather at customer locations
. We first describe a matheuristic based on a split procedure that generat
es high quality solutions fast. We present computational experiments on a
set of real instances with up to 3,800 customers. We also apply the matheu
ristic to a related problem called the vehicle routing problem with transp
ortable resources, where the method found new best solutions on 32 out of
40 benchmark instances from the literature. We then present an exact algor
ithm, based on a set-covering formulation of the problem with columns repr
esenting complete routes, which is capable of solving to optimality instan
ces with up to 50 customers.
DTSTAMP:20230222T173809Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Simulation Modelling of the BC Critical Care System
for Pandemic Response from Sandy Rutherford
DTSTART:20230302T233000Z
DTEND:20230303T003000Z
UID:71
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Sandy Rutherford\nTitle: Simulation Modelling of
the BC Critical Care System for Pandemic Response\nLocation: https://sfu.
zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: The
pandemic placed considerable stress on the critical care system in British
Columbia. In this talk, I will present simulation modelling analysis done
to support the response to the pandemic and ongoing work to improve the a
bility of the critical care system to respond to future public health cris
es. The first project that I will discuss is a queuing model to inform ven
tilator capacity planning during the first wave of the COVID-19 pandemic.
I will then describe ongoing development of a discrete event simulation mo
del for the network of major intensive care units (ICUs) in BC. Currently,
our model contains admissions and transfers for ICUs and high acuity unit
s at eight hospitals in BC. This model will help to improve patient access
to critical care, and inform planning for seasonal influenza and COVID-19
.
DTSTAMP:20230227T181454Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Data-driven Approach to Optimal Ordering batching P
roblem in Warehouse Management from Gohram Baloch
DTSTART:20230316T223000Z
DTEND:20230316T233000Z
UID:66
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Gohram Ba
loch\nTitle: Data-driven Approach to Optimal Ordering batching Problem
in Warehouse Management\nLocation: https://sfu.zoom.us/j/66322138557?pwd=
ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: In this work, we focus on the
picking process in warehouse management and study it from a data perspecti
ve. Using historical data from an industrial partner, we introduce, model,
and study the robust order batching problem (ROBP) that groups orders int
o batches to minimize total order processing time accounting for uncertain
ty caused by system congestion and human behavior. We provide a generaliza
ble, data-driven approach that overcomes warehouse-specific assumptions ch
aracterizing most of the work in the literature. We analyze historical dat
a to understand the processes in the warehouse, to predict processing time
s, and to improve order processing. We introduce the ROBP and develop an e
fficient learning-based branch-and-price algorithm based on simultaneous c
olumn and row generation, embedded with alternative prediction models such
as linear regression and random forest that predict processing time of a
batch. We conduct extensive computational experiments to test the performa
nce of the proposed approach and to derive managerial insights based on re
al data.\n\n
DTSTAMP:20230315T185937Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: On the Packing and Hitting Numbers of Axis-parallel
Segments from Marco Caoduro
DTSTART:20230406T223000Z
DTEND:20230406T233000Z
UID:74
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Marco Caoduro\nTitle: On the Packing and Hitting
Numbers of Axis-parallel Segments\nLocation: https://sfu.zoom.us/j/663221
38557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Given a family R of
rectangles in the plane, the packing number of R, denoted by $\nu$(R), is
the maximum size of a set of pairwise disjoint rectangles in R, and the hi
tting number, denoted by $\tau$(R), is the minimum size of a set of points
having a non-empty intersection with each rectangle in R. Clearly, $\tau
\ge \nu$.\n\nWegner (1965), and independently, Gyárfás and Lehel (1985),
asked whether the hitting number $\tau$ could be bounded by a linear func
tion of the packing number $\nu$. In addition, Wegner proposed a multiplic
ative constant of 2. This problem is still wide open and even if linear bo
unds are known for several particular cases, almost none of them are paire
d with lower bound examples showing their optimality.\n\nFor a family of a
xis-parallel line segments, it is easy to show that $\tau \le 2\nu$, as su
ggested by Wegner. During the talk, we will consider families of axis-para
llel segments with the additional property that no three of them meet at a
point (that is, the intersection graph is triangle-free). We show that, i
n this restricted setting, the packing number of a family is at least $n/4
+C_1\sqrt{n}$ where $n$ is the size of the considered family and $C_1$ is
a fixed positive constant. In addition, we construct examples with packing
number at most $n/4 + C_2\sqrt{n}$ for a different constant $C_2 > C_1$ s
howing that the previous bound is essentially optimal.\nAs a consequence o
f these results, we settle the Wegner-Gyárfás-Lehel’s problem for axis
-parallel segments showing that the multiplicative constant of 2 is optima
l and deduce that $\tau \le 2\nu − C_3 \sqrt{\nu}$ for triangle-free axi
s-parallel segments. This bound cannot be achieved for triangle-free axis-
parallel rectangles, marking a substantial difference in the behavior of s
egments and rectangles.\n\nAt the end of the talk, we will present several
open problems, in particular, linking our work with the recent developmen
ts on the computation of the packing number for axis-parallel rectangles o
f Mitchell (2021) and Gálvez, Khan, Mari, Mömke, Pittu, and Wiese (2022)
. This is joint work with Jana Cslovjecsek, Michał Pilipczuk, and Karol W
ęgrzycki.\n\n \n
DTSTAMP:20230319T041506Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Effects of Usage-Based Auto Insurance: A Dynamic Me
chanism-Design Approach from Mona Imanpoor Yourdshahy
DTSTART:20230323T223000Z
DTEND:20230323T233000Z
UID:69
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Mona Imanpoor Yourdshahy\nTitle: Effects of Usag
e-Based Auto Insurance: A Dynamic Mechanism-Design Approach\nLocation: htt
ps://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstr
act: Usage-Based Insurance (UBI) is one of the most recent innovations by
auto insurance companies that links the premium rates of customers to thei
r actual driving performance. In this program, drivers’ behaviours are m
onitored directly while they drive. Then, the insurance company uses this
data to offer discounts on the insurance premium to their customers. This
paper provides a theoretical model to capture the effects of this monitori
ng technology on the auto insurance market. We formulate the underlying in
surance problem as a dynamic principal-agent model with hidden information
and hidden action. An agent (customer) privately knows his type that summ
arizes his ability as a driver and can exert an unobservable effort in eac
h period, which affects his subsequent type. The principal (insurer) offer
s a long-term contract to the agent despite the fact that she observes nei
ther the type of the agent nor the actions he takes. We characterize the f
ull history-dependent optimal contract for this dynamic adverse selection
and moral hazard problem. To compute the optimal contract, we develop a ge
neral recursive formulation. The underlying system is a Markov decision pr
ocess, where the evolution of the state of the system (type of the custome
r) is endogenous, as it depends on his hidden action in the previous perio
d. We develop a dynamic programming algorithm to examine the model analyti
cally and explore structural results about the optimal contract. The model
results lead to important and interesting managerial insights for firms w
ho may consider offering UBI programs. The study sheds light on how to des
ign the contract to manage a UBI program, the extent to which a UBI policy
can outperform a traditional policy, and how the potential gains depend o
n the demographics of the target market.
DTSTAMP:20230321T224604Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Central Curve in Semidefinite Programming from Isab
elle Shankar
DTSTART:20230525T223000Z
DTEND:20230525T233000Z
UID:64
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:
Isabelle Shankar\nTitle: Central Curve in Semidefinite Programming\nLo
cation: https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09\nAbstract: The Zariski closure of the central path (which interior po
int algorithms track in convex optimization problems such as linear and se
midefinite programs) is an algebraic curve, called the central curve. Its
degree has been studied in relation to the complexity of these interior po
int algorithms. We show that the degree of the central curve for generic
semidefinite programs is equal to the maximum likelihood degree of linear
concentration models. This is joint work with Serkan Hoşten and Angélic
a Torres.
DTSTAMP:20230523T191304Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: First-order Methods for Bilevel Optimization from Z
haosong Lu
DTSTART:20230921T210000Z
DTEND:20230921T220000Z
UID:85
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Zhaosong Lu\nTitle: First-order Methods for Bile
vel Optimization\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldz
ZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Bilevel optimization has been widely u
sed in a variety of areas such as adversarial training, hyperparameter tun
ing, image reconstruction meta-learning, neural architecture search, and r
einforcement learning. In this talk, I will present first-order methods fo
r solving a class of bilevel optimization through the use of single or seq
uential minimax optimization. The first-order operation complexity of the
proposed methods will be discussed.
DTSTAMP:20230919T215037Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: On the composition of two linear projections from H
einz Bauschke
DTSTART:20231005T210000Z
DTEND:20231005T220000Z
UID:88
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Heinz Bauschke\nTitle: On the composition of two
linear projections\nLocation: https://ubc.zoom.us/j/69241125133?pwd=QUw2O
TdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: Projection operators are fundamenta
l algorithmic operators in Analysis and Optimization. It is well known tha
t these operators are firmly nonexpansive\; however, their composition is
generally only averaged and no longer firmly nonexpansive. We will intro
duce the modulus of averagedness and provide an exact result for the compo
sition of two linear projection operators. As a consequence, we deduce tha
t the Ogura-Yamada bound for the modulus of the composition is sharp. Base
d on joint work with Theo Bendit and Walaa Moursi.
DTSTAMP:20230926T001357Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Exploiting Problem Structure for Efficient Optimiza
tion in Machine Learning from Sharan Vaswani
DTSTART:20231019T210000Z
DTEND:20231019T220000Z
UID:86
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Sharan Vaswani\nTitle: Exploiting Problem Struct
ure for Efficient Optimization in Machine Learning\nLocation: https://sfu.
zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Stoc
hastic gradient descent (SGD) is the standard optimization method for trai
ning machine learning (ML) models. SGD requires a step-size that depends o
n unknown problem-dependent quantities, and the choice of this step-size h
eavily influences the algorithm's practical performance. By exploiting the
interpolation property satisfied by over-parameterized ML models, we desi
gn a stochastic line-search procedure that can automatically set the SGD s
tep-size. The resulting algorithm exhibits improved theoretical and empiri
cal convergence, without requiring the knowledge of any problem-dependent
constants. Next, we consider efficient optimization for imitation learning
(IL) and reinforcement learning. These settings involve optimizing functi
ons for which it is expensive to compute the gradient. We propose an optim
ization framework that uses the expensive gradient computation to construc
t surrogate functions that can then be minimized efficiently. This allows
for multiple model updates, thus amortizing the cost of the gradient compu
tation. The resulting majorization-minimization algorithm is equipped with
strong theoretical guarantees and exhibits fast convergence on standard I
L problems.\n\n
DTSTAMP:20231011T201710Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Pricing Shared Rides from Julia Yan
DTSTART:20231102T210000Z
DTEND:20231102T220000Z
UID:87
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Julia Yan\nTitle: Pricing Shared Rides\nLocation
: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\n
Abstract: Shared rides, which pool individual riders into a single vehicle
, are essential for mitigating congestion and promoting more sustainable u
rban transportation. However, major ridesharing platforms have long strugg
led to maintain a healthy and profitable shared rides product. To understa
nd why shared rides have struggled, we analyze procedures commonly used in
practice to set static prices for shared rides, and discuss their pitfall
s. We then propose a pricing policy that is adaptive to matching outcomes,
dubbed match-based pricing, which varies prices depending on whether a ri
der is dispatched alone or to what extent she is matched with another ride
r. Analysis on a single origin-destination setting reveals that match-base
d pricing is both profit-maximizing and altruistic, simultaneously improvi
ng cost efficiency (i.e., the fraction of cost saved by shared rides relat
ive to individual rides) and reducing rider payments relative to the optim
al static pricing policy. These theoretical results are validated on a lar
ge-scale simulation with hundreds of origin-destinations from Chicago ride
sharing data. The improvements in efficiency and reductions in payments ar
e especially noticeable when costs are high and demand density is low, ena
bling healthy operations where they have historically been most challengin
g.\n\n
DTSTAMP:20231027T185909Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Path to Energy Sovereignty: Clean and Affordable So
lutions for Remote Communities from Feyza Sahinyazan
DTSTART:20231116T220000Z
DTEND:20231116T230000Z
UID:90
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Feyza Sahinyazan\nTitle: Path to Energy Sovereig
nty: Clean and Affordable Solutions for Remote Communities\nLocation: http
s://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstra
ct: Remote communities around the globe rely on off-grid installations of
stand-alone diesel generators to cover their energy needs, which can be co
stly, harmful to the environment and subject to disruptions. Policymakers
seek sustainable solutions for these communities to meet the Sustainable D
evelopment Goals regarding clean energy and reduced inequalities. Even wit
h the best intentions, ignoring community perspectives can hamper the clea
n energy transition and energy accessibility goals of remote communities.
Our objective in this research is to identify the optimal generation capac
ity investment decisions from a remote community’s perspective and inves
tigate how common policy mechanisms interact with these decisions.\n\nI am
also planning to dedicate some portion of my talk to give a brief overvie
w of my other ongoing projects to see if there is any interest in collabor
ation.
DTSTAMP:20231103T180125Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Searching for Optimal Per-Coordinate Step-sizes wit
h Multidimensional Backtracking from Frederik Kunstner
DTSTART:20231130T220000Z
DTEND:20231130T230000Z
UID:93
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Frederik Kunstner\nTitle: Searching for Optimal
Per-Coordinate Step-sizes with Multidimensional Backtracking\nLocation: ht
tps://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbst
ract: The backtracking line-search is an effective technique to automatica
lly tune the step-size in smooth optimization. It guarantees similar perfo
rmance to using the theoretically optimal step-size. Many approaches have
been developed to instead tune per-coordinate step-sizes, also known as di
agonal preconditioners, but none of the existing methods are provably comp
etitive with the optimal per-coordinate stepsizes. We propose multidimensi
onal backtracking, an extension of the backtracking line-search to find go
od diagonal preconditioners for smooth convex problems. Our key insight is
that the gradient with respect to the step-sizes, also known as hypergrad
ients, yields separating hyperplanes that let us search for good precondit
ioners using cutting-plane methods. As black-box cutting-plane approaches
like the ellipsoid method are computationally prohibitive, we develop an e
fficient algorithm tailored to our setting. Multidimensional backtracking
is provably competitive with the best diagonal preconditioner and requires
no manual tuning.
DTSTAMP:20231103T180246Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Any-dimensional convex sets from Eitan Levin
DTSTART:20240125T220000Z
DTEND:20240125T230000Z
UID:92
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Eitan Levin\nTitle: Any-dimensional convex sets\
nLocation: https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhV
nAxdz09\nAbstract: Classical algorithms are defined on inputs of different
sizes. In contrast, data-driven algorithms, that is, algorithms learned f
rom some data, may only be defined on inputs of the same size as the data.
What does it mean for an algorithm to be defined on infinitely-many inpu
t sizes? How do we describe such algorithms, and how do we parametrize and
search over them?\nIn this talk, we tackle these questions for convex opt
imization-based algorithms. Describing such algorithms reduces to describi
ng convex sets. These, in turn, are often "freely" described, meaning that
their description makes instantiation in every dimension obvious. Example
s include unit balls of standard norms defined on vectors of any size, gra
ph parameters defined for graphs of any size, and (quantum) information th
eoretic quantities defined for distributions on any number of (qu)bits.\nW
e show that such free descriptions of convex sets arise from two ingredien
ts. First, group invariance and the recently-identified phenomenon of rep
resentation stability. Second, embeddings and projections relating differ
ent-sized problem instances. We combine these ingredients to obtain param
etrized families of infinitely instantiable convex sets. To extend a set
learned from data in a fixed dimension to higher ones, we identify consist
ency conditions relating sets in different dimensions that are satisfied i
n a variety of applications, and obtain parametrizations respecting these
conditions. Our parametrizations can be obtained computationally.\n
DTSTAMP:20231208T210905Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Expected decrease for derivative-free algorithms us
ing random subspaces from Warren Hare
DTSTART:20240111T220000Z
DTEND:20240111T230000Z
UID:94
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Warren Hare\nTitle: Expected decrease for deriva
tive-free algorithms using random subspaces\nLocation: https://ubc.zoom.us
/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: Derivative-
free algorithms seek the minimum of a given\nfunction based only on functi
on values queried at appropriate points.\nTheir performance is known to wo
rsen as the problem dimension increases.\n Recent advances in developing
randomized derivative-free techniques\nhave tackled this issue by working
in low-dimensional subspaces that are\ndrawn at random in an iterative fas
hion. In this talk, we present\nanalysis for derivative-free algorithms t
hat employing random subspaces\nto obtain understanding of the expected de
crease achieved per function\nevaluation.
DTSTAMP:20240103T181605Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Evolution of the Mads algorithm by developing speci
fic features from Charles Audet
DTSTART:20240314T210000Z
DTEND:20240314T220000Z
UID:95
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Charles Audet\nTitle: Evolution of the Mads algo
rithm by developing specific features\nLocation: https://ubc.zoom.us/j/692
41125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: The topic of this
talk is on Blackbox optimization (BBO), the study of\napplications, attri
butes, and solutions of optimization problems in\nwhich the values of one
or more of the functions defining the problem\nare provided through blackb
oxes. The Mesh Adaptive Direct Search (Mads)\nis a derivative-free algori
thm pioneered in 2006 for constrained BBO\nproblems. This talk discusses
recent advances to the Mads algorithm,\nincluding -i- the treatment of gra
nular variables\; -ii- dynamic scaling\nof variables\; -iii- escaping unkn
own discontinuities\; and -iv- revised\nconvergence results for discontinu
ous functions.
DTSTAMP:20240208T221011Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Recent Results in Computational Convex Analysis fro
m Yves Lucet
DTSTART:20240229T220000Z
DTEND:20240229T230000Z
UID:97
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Yves Lucet\
nTitle: Recent Results in Computational Convex Analysis\nLocation: https:/
/ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract:
Computational convex analysis aims at computing mathematical objects comm
only used in convex analysis with an emphasis on lower dimensions to facil
itate visualization. The latest contributions have focused on computing th
e closest convex function and the ongoing quest for explicit formulas for
the conjugate of nonconvex functions. To ease the development of a toolbox
, greater emphasis is put on simplifying intermediate computations especia
lly for piecewise functions because the overall computation time depends o
n the number of pieces.
DTSTAMP:20240226T214936Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: TBA from João Gouveia
DTSTART:20240425T210000Z
DTEND:20240425T220000Z
UID:98
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:João Gouveia\nTitle: TBA\nLocation: https://ubc
.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: TBA
DTSTAMP:20240304T185323Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: A survey on preconditioning techniques for a class
of block three-by-three linear systems from Fatemeh Beik
DTSTART:20240509T210000Z
DTEND:20240509T220000Z
UID:100
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Fatemeh Beik \nTitle: A survey on preconditionin
g techniques for a class of block three-by-three linear systems\nLocation:
https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nA
bstract: In this talk, we study the performance of some preconditioners fo
r accelerating the convergence of Krylov subspace methods for solving line
ar systems of equations with a block three-by-three structure. A brief dis
cussion is included regarding how spectral and field-of-value analyses can
be exploited to study the performance of a preconditioner in conjunction
with the Generalized Minimum Residual Method (GMRES). Numerical experiment
s show the effectiveness of inexact versions of preconditioners used with
flexible GMRES for solving linear systems of equations arising from mixed
finite element discretizations of the coupled Stokes-Darcy flow problem.
DTSTAMP:20240310T183714Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: TBA from Beny Wai
DTSTART:20240418T210000Z
DTEND:20240418T220000Z
UID:102
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Beny Wai\nTitle: TBA\nLocation: https://sfu.zoom
.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: TBA
DTSTAMP:20240327T180937Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Q-fully quadratic modeling and its application in a
random subspace derivative-free method from Yiwen Chen
DTSTART:20240404T210000Z
DTEND:20240404T220000Z
UID:99
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Yiwen Chen\nTitle: Q-fully quadratic modeling an
d its application in a random subspace derivative-free method\nLocation: h
ttps://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbs
tract: Derivative-free optimization (DFO) methods are a class of optimizat
ion methods that do not use the derivatives of the objective or constraint
functions. Model-based DFO methods are an important class of DFO methods
that are known to struggle with solving high-dimensional optimization pro
blems. Recent research has shown that incorporating random subspaces into
model-based DFO methods has the potential to improve their performance on
high-dimensional problems. However\, most of the current theoretical and
practical results are based on linear approximation models due to the comp
lexity of quadratic approximation models. In this talk\, we propose a rand
om subspace derivative-free trust-region algorithm based on quadratic appr
oximations. Unlike most of its precursors\, this algorithm does not requir
e any special form of objective function. We study the geometry of sample
sets\, the error bounds for approximations\, and the quality of subspaces.
In particular\, we provide a technique to construct Q-fully quadratic mod
els\, which is easy to analyze and implement. We present an almost-sure gl
obal convergence result of our algorithm and give an upper bound on the ex
pected number of iterations to find a sufficiently small gradient. We also
develop numerical experiments to compare the performance of our algorithm
using both linear and quadratic approximation models. The numerical resul
ts demonstrate the strengths and weaknesses of using quadratic approximati
ons.
DTSTAMP:20240327T215425Z
END:VEVENT
END:VCALENDAR