Applied & Computational Math Archives - Page 6 of 6

Seminars: Applied & Computational Math

Date
Time
Venue

Speaker
Affiliation
Title of Talk

20 Mar 2019
15:00
S17 #05-12 SR4

Jonathan Mark Scarlett
National University of Singapore
Robust Submodular Function Maximization

Robust Submodular Function Maximization

Date/Time : 20 Mar 2019 15:00
Venue : S17 #05-12 SR4
Speaker : Jonathan Mark Scarlett, National University of Singapore

Date/Time:20 Mar 2019 15:00 Venue: S17 #05-12 SR4 Speaker: Jonathan Mark Scarlett, National University of Singapore Robust Submodular Function Maximization In the first part of this talk, I will give a brief introduction to a “diminishing returns” property of set functions known as submodularity, and overview some applications and classical guarantees of greedy optimization methods. In the second part, I will turn to a robust variation of the submodular optimization problem in which some of the selected elements of the set are removed by an adversary, and we want the function value to remain as a high as possible even after these removals. We introduce an algorithm based on applying the greedy subroutine to carefully defined partitions, and provide a theoretical guarantee of attaining a constant-factor approximation of the best value Add to calendar:

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11 Mar 2019
16:00
S17 #04-06 SR1

Charles K. Chui
Menlo Park, California
Inverse Problem on Assessment of our Health and Well-being

Inverse Problem on Assessment of our Health and Well-being

Date/Time : 11 Mar 2019 16:00
Venue : S17 #04-06 SR1
Speaker : Charles K. Chui , Menlo Park, California

Date/Time:11 Mar 2019 16:00 Venue: S17 #04-06 SR1 Speaker: Charles K. Chui, Menlo Park, California Inverse Problem on Assessment of our Health and Well-being Hormone balance is critical to our health and well-being, in that its imbalances commonly lead to fatigue, sleep changes, depression, loss of bone, hair loss, memory difficulty, heart disease, and cancer. Our endocrine system is in charge of secreting hormones, mainly from the endocrine glands to the blood stream, creating pulse signals that can be measured, most conveniently from our two wrists. Normal daily periods of hormone secretion are governed by the circadian rhythms. This lecture is concerned with the important inverse problem of assessing the health of the major endocrine glands from pulse data. Motivated by the published medical literature, we introduce a mathematical representation of pulse waveforms, with three parameters that govern the pulse strength, rate of secretion signal onset, and rate of secretion signal decay, respectively. We then formulate the mathematical model of the source signal in terms of the pulse waveforms, and discuss the inverse problem of recovering the source signal from pulse data. Add to calendar:

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06 Mar 2019
15:00
S17 #04-04 SR3

Li Yunyue
National University of Singapore
Multicomponent Elastic Imaging: New Insights from the Old Equations

Multicomponent Elastic Imaging: New Insights from the Old Equations

Date/Time : 06 Mar 2019 15:00
Venue : S17 #04-04 SR3
Speaker : Li Yunyue , National University of Singapore

Date/Time:06 Mar 2019 15:00 Venue: S17 #04-04 SR3 Speaker: Li Yunyue, National University of Singapore Multicomponent Elastic Imaging: New Insights from the Old Equations Elastic wave imaging has been a significant challenge in the exploration industry due to the complexities in wave physics and numerical implementation. We have separated the governing equations for P- and S-wave propagation without the assumptions of homogeneous Lamé parameters to capture the mode conversion between the two body waves in an isotropic, constant-density medium. The resulting set of two coupled second-order equations for P- and S-potentials clearly demonstrates that mode conversion only occurs at the discontinuities of the shear modulus. Applying the Born approximation to the new equations, we derive the PP, PS, SP, and SS imaging conditions from the first gradients of waveform matching objective functions. The resulting images are consistent with the physical perturbations of the elastic parameters, and, hence, they are automatically free of the polarity reversal artifacts in the converted images. When implementing elastic reverse time migration (RTM), we find that scalar wave equations can be used to back propagate the recorded P-potential, as well as individual components in the vector field of the S-potential. Compared with conventional elastic RTM, the proposed elastic RTM implementation using acoustic propagators not only simplifies the imaging condition, it but also reduces the computational cost and the artifacts in the images. We have demonstrated the accuracy of our method using 2D and 3D numerical examples. Add to calendar:

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20 Feb 2019
15:00
S17 #04-06 SR1

Siva Athreya
ISI Bangalore
Strong existence and uniqueness for stable stochastic differential equations with distributional drift

Strong existence and uniqueness for stable stochastic differential equations with distributional drift

Date/Time : 20 Feb 2019 15:00
Venue : S17 #04-06 SR1
Speaker : Siva Athreya , ISI Bangalore

Date/Time:20 Feb 2019 15:00 Venue: S17 #04-06 SR1 Speaker: Siva Athreya, ISI Bangalore Strong existence and uniqueness for stable stochastic differential equations with distributional drift We consider the stochastic differential equation dX_t = b(X_t) dt + dL_t,where the drift b is a generalized function and L is a symmetric one dimensional alpha-stable Levy processes, alpha in (1, 2). We define the notion of solution to this equation and establish strong existence and uniqueness whenever b belongs to the Besov-Holder space C^\beta for beta > 1/2- alpha/2. This is joint work with Oleg Butkovsky and Leonid Mytnik. Add to calendar:

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30 Jan 2019
14:00
S17 #04-06 SR1

Peter Forrester
University of Melbourne
Decomposition of measure in random matrix theory and integral geometry

Decomposition of measure in random matrix theory and integral geometry

Date/Time : 30 Jan 2019 14:00
Venue : S17 #04-06 SR1
Speaker : Peter Forrester , University of Melbourne

Date/Time:30 Jan 2019 14:00 Venue: S17 #04-06 SR1 Speaker: Peter Forrester, University of Melbourne Decomposition of measure in random matrix theory and integral geometry Fundamental to random matrix theory is various factorisations of Lebesgue product measure implied by matrix change of variables. Factorisation of measure is also fundamental to integral geometry, with one of the most important results due to Blaschke and Petkantschin. Following recent work of Moghadasi, we show how the latter is related to matrix polar decomposition, and can be applied to the calculation of the moments of the volume content of the convex hull of random points in higher dimensional spaces. Applications to the computation of Lyapunov exponents will also be discussed. Add to calendar:

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