A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method solves nonseparable stochastic control problems.
This research develops an evolutionary approach to discover non-Gaussian stochastic dynamical systems.
Deep learning solves complex stochastic control with jumps.
This paper studies dynamic stochastic optimization problems parametrized by a random variable. Such problems arise in many applications in operations research and mathematical finance. We give sufficient conditions for the existence of solutions and the absence of a duality gap. Our proof uses extended dynamic programm…
The paper analyzes error propagation in dynamic programming for stochastic control and option pricing.
Neural model accelerates SDDP for stochastic optimization.
New metric derived for robust optimization in stochastic control problems.
This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems. We propose formulations to represent, compare, and propagate uncertainties for fairly general stochastic dynamics in a distribution-free manner. The new tools enjoy sound theory rooted in f…
Develops new optimization techniques for decision-making under uncertainty.
New method uses dynamic programming for meta continual learning.
We study a stochastic game where one player tries to find a strategy such that the state process reaches a target of controlled-loss-type, no matter which action is chosen by the other player. We provide, in a general setup, a relaxed geometric dynamic programming principle for this problem and derive, for the case of …
We introduce a novel numerical approach for a class of stochastic dynamic programs which arise as discretizations of backward stochastic differential equations or semi-linear partial differential equations. Solving such dynamic programs numerically requires the approximation of nested conditional expectations, i.e., it…
We consider an optimal stopping problem where a constraint is placed on the distribution of the stopping time. Reformulating the problem in terms of so-called measure-valued martingales allows us to transform the marginal constraint into an initial condition and view the problem as a stochastic control problem; we esta…
The paper solves optimal control problems for stochastic delay equations.
Paper explores two methods for optimal portfolio selection in financial markets.
In this paper, we consider the problem of optimization of a portfolio consisting of securities. An investor with an initial capital, is interested in constructing a portfolio of securities. If the prices of securities change, the investor shall decide on reallocation of the portfolio. At each moment of time, the prices…
This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming. Unlike classical approximate dynamic programming approaches, we first approximate the optimal policy by means of neural networks in the spirit of deep reinforcement learning, and then the valu…
The paper shows how label noise in training can lead to solutions that solve a Lasso program.
Solves VaR-constrained portfolio optimization in markets with stochastic volatility.
Solves portfolio optimization with costs using numerical methods.
Improved machine learning for reservoir optimization problems.
We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the …
We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning effic…
Model for optimal cybersecurity investment considering clustered cyberattacks.
New algorithm solves complex stopping problems with robust optimization.
The stochastic knapsack has been used as a model in wide ranging applications from dynamic resource allocation to admission control in telecommunication. In recent years, a variation of the model has become a basic tool in studying problems that arise in revenue management and dynamic/flexible pricing; and it is in thi…
Solves Merton's investment-consumption problem with certainty equivalent approach.
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
Develops a model for bid and ask prices using stochastic control.
Efficiently selects top-m designs for various contexts using sequential sampling.
We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in them. Stochastic probabilistic programs allow straightforward specification and efficient inference …
Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decision problems. However, in multi-period decision problems, we must often compute expected downstream values corresponding to current decision…
The paper solves complex control problems using neural networks.
Improved reinforcement method for optimal control problems.
Inspired by dynamic programming, we propose Stochastic Virtual Gradient Descent (SVGD) algorithm where the Virtual Gradient is defined by computational graph and automatic differentiation. The method is computationally efficient and has little memory requirements. We also analyze the theoretical convergence properties …
The paper solves multi-period portfolio selection with constraints using a dynamic factor model.
This paper considers a non-Markov control problem arising in a financial market where asset returns depend on hidden factors. The problem is non-Markov because nonlinear filtering is required to make inference on these factors, and hence the associated dynamic program effectively takes the filtering distribution as one…
Paper tackles non-Markovian control problems with new learning methods.
Develops RL for dynamic risk assessment in stochastic optimization.
Paper analyzes convergence of dynamic policy gradient for MDPs, improving performance in finite-time problems.
Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.
This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-b…
We generalize the primal-dual methodology, which is popular in the pricing of early-exercise options, to a backward dynamic programming equation associated with time discretization schemes of (reflected) backward stochastic differential equations (BSDEs). Taking as an input some approximate solution of the backward dyn…
BCI provides calibrated prediction intervals for time series forecasts.
CEFOL uses deep learning for dynamic programming with recursive utility.
Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Stochastic acceptors or probabilistic automata are stochastic automata without output that can model components in machine learning scenarios. In this paper, we provide dynamic programming algorithms for the comput…
The paper models battery valuation in intraday electricity markets, incorporating liquidity costs.
We study utility maximization problem for general utility functions using dynamic programming approach. We consider an incomplete financial market model, where the dynamics of asset prices are described by an -valued continuous semimartingale. Under some regularity assumptions we derive backward stochastic partial…