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John Moriarty

Publications and source records attributed to John Moriarty.

At least 19 recordsLinked to original sources

Branched harmonic majorants: representations for multidimensional optimal stopping

We construct the least superharmonic majorant of a continuous function $g$ on the $d$-dimensional unit ball ($d \geq 2$) via a canonical sequential scheme. While classical theory identifies this majorant with the value function of the optimal stopping problem for Brownian motion absorbed at the domain boundary, no comparable constructive approximation scheme has been available. We introduce branched harmonic majorants, obtained by arranging classical harmonic functions on smoothly bounded domains in a finite, depth-indexed branching structure, and prove two main results. First, the optimal stopping region is identified as the contact set between the gain function $g$ and the pointwise infimum of this family; the value function is recovered as the expected gain at the first exit time from the non-contact set. This yields a multidimensional generalisation of the Dynkin--Yushkevich concave-envelope theorem in which affine functions are replaced by branched harmonic majorants. Second, truncation in the branching depth produces a decreasing sequence of envelopes that converges pointwise to this infimum, yielding an explicit approximation scheme not present in classical formulations. Analytically, the branching structure relaxes the global majorisation constraint to a local constraint imposed on a decreasing sequence of non-contact sets, yielding a representation of the Perron envelope in terms of harmonic functions on smoothly bounded domains. Probabilistically, the construction corresponds to the sequential composition of stopping times and overcomes the localisation obstruction arising from the thinness of Brownian paths in dimensions $d \geq 2$.

math.PR

The one-shot problem: Solution to an open question of finite-fuel singular control with discretionary stopping

We resolve a long-standing open question posed by Karatzas, Ocone, Wang, and Zervos (Stochastics, 2000) on finite-fuel singular stochastic control with discretionary stopping. Our approach introduces a novel "one-shot" solution technique, based on reduction to an auxiliary optimal stopping problem. We prove that, despite the convexity of the objective function, the waiting region of the optimal strategy need not be connected. Furthermore, we identify a qualitative transition in the optimal policy: even with arbitrarily small positive fuel, the solution can differ from the zero-fuel (pure optimal stopping) limit. These results reveal a fundamental non-robustness in such control problems, showing that zero-fuel models need not approximate behaviour under small but positive fuel constraints.

math.OC

Optimal stopping with nonlinear expectation: geometric and algorithmic solutions

We use the geometry of suitably generalised potentials to solve risk-sensitive Markovian optimal stopping problems. As in the linear case due to Dynkin and Yushkievich (1967), the value function is the pointwise infimum of those functions which dominate the gain function. An emphasis is placed on geometric and pathwise arguments, rather than exploiting convexity, positive homogeneity or related analytical properties. An algorithm is provided to construct the value function at the computational cost of a two-dimensional search.

math.OC

Markov risk mappings and risk-sensitive optimal prediction

We formulate a probabilistic Markov property in discrete time under a dynamic risk framework with minimal assumptions. This is useful for recursive solutions to risk-sensitive versions of dynamic optimisation problems such as optimal prediction, where at each stage the recursion depends on the whole future. The property holds for standard measures of risk used in practice, and is formulated in several equivalent versions including a representation via acceptance sets, a strong version, and a dual representation.

math.OC

RangL: A Reinforcement Learning Competition Platform

The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems. This article describes the reusable code repository developed by the RangL team and deployed for the 2022 Pathways to Net Zero Challenge, supported by the UK Net Zero Technology Centre. The winning solutions to this particular Challenge seek to optimize the UK's energy transition policy to net zero carbon emissions by 2050. The RangL repository includes an OpenAI Gym reinforcement learning environment and code that supports both submission to, and evaluation in, a remote instance of the open source EvalAI platform as well as all winning learning agent strategies. The repository is an illustrative example of RangL's capability to provide a reusable structure for future challenges.

cs.LG

Discrete-time risk-aware optimal switching with non-adapted costs

We solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision maker is risk aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example application to hydropower planning is provided.

math.OC

A Metropolis-class sampler for targets with non-convex support

We aim to improve upon the exploration of the general-purpose random walk Metropolis algorithm when the target has non-convex support $A \subset \mathbb{R}^d$, by reusing proposals in $A^c$ which would otherwise be rejected. The algorithm is Metropolis-class and under standard conditions the chain satisfies a strong law of large numbers and central limit theorem. Theoretical and numerical evidence of improved performance relative to random walk Metropolis are provided. Issues of implementation are discussed and numerical examples, including applications to global optimisation and rare event sampling, are presented.

math.PR

Hopping between distant basins

We present the Basin Hopping with Skipping (BH-S) algorithm for stochastic optimisation, which replaces the perturbation step of basin hopping (BH) with a so-called skipping proposal from the rare-event sampling literature. Empirical results on benchmark optimisation surfaces demonstrate that BH-S can improve performance relative to BH by encouraging non-local exploration, that is, by hopping between distant basins.

math.OC

Large Fluctuations in Locational Marginal Prices

This paper investigates large fluctuations of Locational Marginal Prices (LMPs) in wholesale energy markets caused by volatile renewable generation profiles. Specifically, we study events of the form $\mathbb{P} \Big ( \mathbf{LMP} \notin \prod_{i=1}^n [α_i^-, α_i^+] \Big),$ where $\mathbf{LMP}$ is the vector of LMPs at the $n$ power grid nodes, and $\boldsymbolα^-,\boldsymbolα^+\in\mathbb{R}^n$ are vectors of price thresholds specifying undesirable price occurrences. By exploiting the structure of the supply-demand matching mechanism in power grids, we look at LMPs as deterministic piecewise affine, possibly discontinuous functions of the stochastic input process, modeling uncontrollable renewable generation. We utilize techniques from large deviations theory to identify the most likely ways for extreme price spikes to happen, and to rank the nodes of the power grid in terms of their likelihood of experiencing a price spike. Our results are derived in the case of Gaussian fluctuations and are validated numerically on the IEEE 14-bus test case.

math.OC

Optimal control of a commercial building's thermostatic load for off-peak demand response

This paper studies the optimal control of a commercial building's thermostatic load during off-peak hours as an ancillary service to the transmission system operator of a power grid. It provides an algorithmic framework which commercial buildings can implement to cost-effectively increase their electricity demand at night while they are unoccupied, instead of using standard inflexible setpoint control. Consequently, there is minimal or no impact on user comfort, while the building manager gains an additional income stream from providing the ancillary service, and can benefit further by pre-conditioning the building for later periods. The framework helps determine the amount of flexibility that should be offered for the service, and cost optimized profiles for electricity usage when delivering the service. Numerical results show that there can be an economic incentive to participate even if the payment rate for the ancillary service is less than the price of electricity.

math.OC

Frequency violations from random disturbances: an MCMC approach

The frequency stability of power systems is increasingly challenged by various types of disturbances. In particular, the increasing penetration of renewable energy sources is increasing the variability of power generation and at the same time reducing system inertia against disturbances. In this paper we are particularly interested in understanding how rate of change of frequency (RoCoF) violations could arise from unusually large power disturbances. We devise a novel specialization, named ghost sampling, of the Metropolis-Hastings Markov Chain Monte Carlo method that is tailored to efficiently sample rare power disturbances leading to nodal frequency violations. Generating a representative random sample addresses important statistical questions such as "which generator is most likely to be disconnected due to a RoCoF violation?" or "what is the probability of having simultaneous RoCoF violations, given that a violation occurs?" Our method can perform conditional sampling from any joint distribution of power disturbances including, for instance, correlated and non-Gaussian disturbances, features which have both been recently shown to be significant in security analyses.

eess.SY

Nonzero-sum games of optimal stopping and generalised Nash equilibrium

In the nonzero-sum setting, we establish a connection between Nash equilibria in games of optimal stopping (Dynkin games) and generalised Nash equilibrium problems (GNEP). In the Dynkin game this reveals novel equilibria of threshold type and of more complex types, and leads to novel uniqueness and stability results.

math.PR

Nash equilibria of threshold type for two-player nonzero-sum games of stopping

This paper analyses two-player nonzero-sum games of optimal stopping on a class of linear regular diffusions with not non-singular boundary behaviour (in the sense of Itô and McKean (1974), p.\ 108). We provide sufficient conditions under which Nash equilibria are realised by each player stopping the diffusion at one of the two boundary points of an interval. The boundaries of this interval solve a system of algebraic equations. We also provide conditions sufficient for the uniqueness of the equilibrium in this class.

math.PR

Bayesian calibration and number of jump components in electricity spot price models

We find empirical evidence that mean-reverting jump processes are not statistically adequate to model electricity spot price spikes but independent, signed sums of such processes are statistically adequate. Further we demonstrate a change in the composition of these sums after a major economic event. This is achieved by developing a Markov Chain Monte Carlo (MCMC) procedure for Bayesian model calibration and a Bayesian assessment of model adequacy (posterior predictive checking). In particular we determine the number of signed mean-reverting jump components required in the APXUK and EEX markets, in time periods both before and after the recent global financial crises. Statistically, consistent structural changes occur across both markets, with a reduction of the intensity and size, or the disappearance, of positive price spikes in the later period. All code and data are provided to enable replication of results.

stat.AP

Energy imbalance market call options and the valuation of storage

The use of energy storage to balance electric grids is increasing and, with it, the importance of operational optimisation from the twin viewpoints of cost and system stability. In this paper we assess the real option value of balancing reserve provided by an energy-limited storage unit. The contractual arrangement is a series of American-style call options in an energy imbalance market (EIM), physically covered and delivered by the store, and purchased by the power system operator. We take the EIM price as a general regular one-dimensional diffusion and impose natural economic conditions on the option parameters. In this framework we derive the operational strategy of the storage operator by solving two timing problems: when to purchase energy to load the store (to provide physical cover for the option) and when to sell the option to the system operator. We give necessary and sufficient conditions for the finiteness and positivity of the value function -- the total discounted cash flows generated by operation of the storage unit. We also provide a straightforward procedure for the numerical evaluation of the optimal operational strategy (EIM prices at which power should be purchased) and the value function. This is illustrated with an operational and economic analysis using data from the German Amprion EIM.

math.OC

Optimal Entry to an Irreversible Investment Plan with Non Convex Costs

A problem of optimally purchasing electricity at a real-valued spot price (that is, with potentially negative cost) has been recently addressed in De Angelis, Ferrari and Moriarty (2015) [SIAM J. Control Optim. 53(3)]. This problem can be considered one of irreversible investment with a cost functional which is non convex with respect to the control variable. In this paper we study the optimal entry into this investment plan. The optimal entry policy can have an irregular boundary arising from this non convexity, with a kinked shape.

math.OC

The Kepler Dichotomy in Planetary Disks: Linking Kepler Observables to Simulations of Late-Stage Planet Formation

NASA's Kepler Mission uncovered a wealth of planetary systems, many with planets on short-period orbits. These short-period systems reside around 50% of Sun-like stars and are similarly prevalent around M dwarfs. Their formation and subsequent evolution is the subject of active debate. In this paper, we simulate late-stage, in-situ planet formation across a grid of planetesimal disks with varying surface density profiles and total mass. We compare simulation results with observable characteristics of the Kepler sample. We identify mixture models with different primordial planetesimal disk properties that self-consistently recover the multiplicity, period ratio and duration ratio distributions of the Kepler planets. We draw three main conclusions: (1) We favor a "frozen-in" narrative for systems of short period planets, in which they are stable over long timescales, as opposed to metastable. (2) The "Kepler dichotomy", an observed phenomenon of the Kepler sample wherein the architectures of planetary systems appear to either vary significantly or have multiple modes, can naturally be explained by formation within planetesimal disks with varying surface density profiles. Finally, (3) we quantify the nature of the "Kepler dichotomy" for both GK stars and M dwarfs, and find that it varies with stellar type. While the mode of planet formation that accounts for highly multiplistic systems occurs in 24+/-7% of planetary systems orbiting GK stars, it occurs in 63+/-16% of planetary systems orbiting M dwarfs.

astro-ph.EP