Spring Semester, 2021 †

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4月19日 / April 19 †

The first meeting of the semester.
Let us gather, greet new members to the Department, and set a plan for the semester before us.
Update:
This year it was decided to have six meetings with two presenters at each meeting. The seminars will be mainly held online by Zoom, And a local screening will be arranged.
Presenters may choose to give their presentations locally, and be broadcasted on Zoom.

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5月31日 / May 31 †

  • Speaker: Ran Wang
    Control Systems Theory Group
    Topic:
    Formation Control of Multiple Drones with Limited Local Measurements
    This talk will introduce formation control theory firstly and show you some application cases, e.g. how to make a drone show. Then a Real-time Operating System (RTOS) based mini-drone called Crazyflie will be introduced including its hardware and software. Also, the limitation of sensors will be considered and an Extended Kalman Filter is used to obtain an optimal states estimation. Finally, we will discuss some limitations in applying formation control theory into practical projects and my future work.

  • Speaker: Shunji Kakinaka
    Physical Statistics Group
    Topic:
    Asymmetric volatility dynamics in cryptocurrency markets
    Asymmetric correlation between price and volatility is a prominent feature of financial market time series. In this short presentation, the stylized facts of the relationship between price and volatility in cryptocurrency markets are introduced. In addition, the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes are investigated using the nonlinear cross-correlation coefficient measures.
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6月14日 / June 14 †

  • Speaker: Hiroki Tanaka
    Physical Statistics Group
    Topic:
    Bayes’ theorem on marked point process and its application to seismicity-like time-series
    Probabilistic forecasting of large earthquakes is based on the statistics on the time intervals of past earthquakes at a large magnitude threshold, and the information on the small events are less utilized. In this presentation, as a method to use such information for the probabilistic evaluation, the Bayes’ theorem between the inter-event times of different magnitude thresholds is considered. By applying the method to the synthetic earthquake catalog, the effectiveness for the forecasting is discussed briefly.

  • Speaker: Hiroki Tanabe
    System Optimization Group
    Topic:
    Accelerated proximal gradient methods for multiobjective optimization
    In recent years, many researchers have studied descent methods for multiobjective optimization problems. For example, Fliege and Svaiter proposed the steepest descent method for differentiable multiobjective optimization problems. Afterward, a proximal gradient method, which can solve non-differentiable problems, was also considered. However, their accelerated versions are not sufficiently studied. Recently, El Moudden and El Mouatasim proposed a natural extension of Nesterov’s accelerated method for multiobjective optimization problems. They proved the global convergence rate of the algorithm O(1/k^2) under the assumption that the sequence of the Lagrangian multipliers of the subproblems is eventually fixed. However, this assumption is restrictive because it means that the method is regarded as the Nesterov’s method for the weighting problem. In this paper, we propose new accelerated algorithms, in which we solve subproblems with terms that only appear in the multiobjective case. We also prove the proposed methods' global convergence rate O(1/k^2) under a more natural assumption, using merit functions as a way to measure the complexity.
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6月21日 / June 21 †

  • Speaker: Atsushi Hori
    System Optimization Group
    Topic:
    Distributionally Robust Expected Residual Minimization for Stochastic Variational Inequality Problems
    The stochastic variational inequality problem (SVIP) is an equilibrium model which includes random variables and has been widely used in economics, engineering, and others. The expected residual minimization (ERM) is known as one of models to get a reasonable solution to the SVIP, and its objective function is an expected value of a suitable merit function for the SVIP. However, the ERM is restricted to the case where the distribution has already been known. We extend the ERM so that robust solutions can be obtained for the SVIP under the uncertainty distribution (we call the extended one DRERM), where the worst case distribution is taken from the set of probability measures whose expected value and variance take the same sample mean and variance, respectively. Under suitable assumptions, we show that the DRERM can be reformulated as a deterministic convex nonlinear semidefinite programming to avoid numerical integration.

  • Speaker: Veronika Nguyen
    Discrete Mathematics Group
    Topic: TBA

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6月28日 / June 28 †

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7月12日 / July 12 †

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7月19日 / July 19 †

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The Research Groups in AMP †

Applied Mathematical Analysis Group
Discrete Mathematics Group
System Optimization Group
Control Systems Theory Group
Physical Statistics Group
Dynamical Systems Group
Applied Mathematical Modelling Group