Mathematical Methods of Operations Research

Friess, Marie Billaud; Macherey, Arthur; Nouy, Anthony; Prieur, Clémentine

doi: 10.1007/s00186-022-00769-xpmid: N/A

This paper considers the problem of maximizing an expectation function over a finite set, or finite-arm bandit problem. We first propose a naive stochastic bandit algorithm for obtaining a probably approximately correct (PAC) solution to this discrete optimization problem in relative precision, that is a solution which solves the optimization problem up to a relative error smaller than a prescribed tolerance, with high probability. We also propose an adaptive stochastic bandit algorithm which provides a PAC-solution with the same guarantees. The adaptive algorithm outperforms the mean complexity of the naive algorithm in terms of number of generated samples and is particularly well suited for applications with high sampling cost.

Zhang, Xiaoya; Peng, Wei; Zhang, Hui

doi: 10.1007/s00186-022-00790-0pmid: N/A

In this paper, we propose an inertial version of the Proximal Incremental Aggregated Gradient (abbreviated by iPIAG) method for minimizing the sum of smooth convex component functions and a possibly nonsmooth convex regularization function. First, we prove that iPIAG converges linearly under the gradient Lipschitz continuity and the strong convexity, along with an upper bound estimation of the inertial parameter. Then, by employing the recent Lyapunov-function-based method, we derive a weaker linear convergence guarantee, which replaces the strong convexity by the quadratic growth condition. At last, we present two numerical tests to illustrate that iPIAG outperforms the original PIAG.

Flåm, Sjur D.; Rückmann, Jan-J.

doi: 10.1007/s00186-022-00789-7pmid: N/A

Considering constrained choice, practitioners and theorists frequently invoke a Lagrangian to generate optimality conditions. Regular use of that vehicle requires, however, some constraintqualification. Yet many economists go easy on the mathematics of that issue. Conversely, few mathematicians elaborate on the economics of the context. Thereby both parties leave some lacunas as to didactics or intuition. This note attempts to shed some light on these matters.

Boxma, Onno; Perry, David; Stadje, Wolfgang

doi: 10.1007/s00186-022-00793-xpmid: N/A

We present a stochastic growth-collapse model for the capital process of a peer-to-peer lending platform. New lenders arrive according to a compound Poisson-type process with a state-dependent intensity function; the growth of the lending capital is from time to time interrupted by partial collapses whose arrival intensities and sizes are also state-dependent. In our model the capital level administered via the platform is the crucial quantity for the generated profit, because the brokerage fee is a fixed (small) fraction of it. Therefore we study its steady-state probability distribution as a key performance measure. In the case of exponentially distributed upward jumps we derive an explicit expression for its probability density, for quite general arrival rates of upward and downward jumps and for certain collapse mechanisms. In the case of generally distributed upward jumps, we derive an explicit expression for the Laplace transform of the steady-state cash level density in various special cases. An alternative model featuring up and down periods and a shot noise mechanism for the downward evolution is also analyzed in steady state.

Yuan, Yu; Liang, Zhibin; Han, Xia

doi: 10.1007/s00186-022-00794-wpmid: N/A

We consider an optimal robust investment and reinsurance problem for a general insurance company which holds shares of an insurance company and a reinsurance company. It is assumed that the decision-maker is ambiguity-averse and does not have perfect information in drift terms of the investment and insurance risks. To capture the ambiguity aversion in the objective function, the criterion of this paper is to minimize a robust value involving the probability of drawdown and a penalization of model uncertainty. By using the technique of stochastic control theory and solving the corresponding boundary-value problems, the closed-form expressions of the optimal strategies are derived explicitly, and a new verification theorem is proved to show that a non-increasing solution to the Hamilton–Jacobi–Bellman equation is indeed our value function. Moreover, we examine theoretically how the level of ambiguity aversion affects the value function and optimal drift distortion. In the end, some numerical examples are exhibited to illustrate the influence of the different investment patterns on our optimal results.

Pun, Chi Seng

doi: 10.1007/s00186-022-00795-9pmid: N/A

This paper establishes a general analytical framework for classical and impulse stochastic control problems in the presence of model uncertainty. We consider a set of dominated models, which are induced by the measures equivalent to that of a reference model. The state process under the reference model is a multidimensional Markov process with multidimensional Brownian motion, controlled by continuous and impulse control variates. We propose quasi-variational inequalities (QVI) associated with the value function of the control problem and prove a verification theorem for the solution to the QVI. With the relative entropy constraints and piecewise linear intervention penalty, we show that the QVI can be degenerated to the non-robust case and it can be solved via the solution to a free boundary problem. To illustrate the tractability of the proposed framework, we apply it to a linear-quadratic setting, which covers a broad class of problems including robust mean-reverting inventory controls.

Gurski, Frank; Komander, Dominique; Rehs, Carolin

doi: 10.1007/s00186-021-00757-7pmid: N/A