arXiv research

A new scheme reduces global search cost by a square root factor.

We introduce and analyze a parallel sequential Monte Carlo methodology for the numerical solution of optimization problems that involve the minimization of a cost function that consists of the sum of many individual components. The proposed scheme is a stochastic zeroth order optimization algorithm which demands only t…

A model is presented in this work for simulating endogenously the evolution of the marginal costs of production of energy carriers from non-renewable resources, their consumption, depletion pathways and timescales. Such marginal costs can be used to simulate the long term average price formation of energy commodities. …

FedFMC improves federated learning on non-iid data without sharing data or increasing communication costs.

Study optimal transport on globally hyperbolic spacetimes, focusing on weak Kantorovich potentials' regularity.

New algorithms minimize regret with global costs in online learning.

A novel algorithm reduces communication costs in federated best arm identification.

Policy gradient converges to globally optimal policy in nearly linear-quadratic systems.

Paper presents an efficient algorithm for linear MDP with low switching cost.

Global geometric expressions derived for manifold embeddings.

Paper improves a method for fast global and local convergence in optimization.

This letter presents an improved version of diffusion least mean ppower (LMP) algorithm for distributed estimation. Instead of sum of mean square errors, a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors. The weight coefficients are updated b…

Optimal transport and neural networks improve trade modeling accuracy.

Less frequent retraining improves forecast accuracy in retail demand forecasting.

Cost-aware BO minimizes function evaluations with varying costs.

The paper constructs minimizers for deep learning networks and analyzes their geometric structure.

Cost overruns in transport infrastructure projects know no geographical limits, overruns are a global phenomenon. Nevertheless, the size of cost overruns varies with location. In the Netherlands, cost overruns appear to be smaller compared to the rest of the world. This paper tests whether Dutch projects perform signif…

Paper proposes quantum methods for optimizing machine learning functions.

Study compares CDS databases and finds discrepancies due to various factors.

Improved portfolio optimization method yields better risk-adjusted returns.

In the early phases of the product life cycle, the costs controls became a major decision tool in the competitiveness of the companies due to the world competition. After defining the problems related to this control difficulties, we will present an approach using a concept of cost entity related to the design and real…

This paper presents efficient sampling methods for Gaussian processes.

This paper presents a stochastic model for discrete-time trading in financial markets where trading costs are given by convex cost functions and portfolios are constrained by convex sets. The model does not assume the existence of a cash account/numeraire. In addition to classical frictionless markets and markets with …

Study uses RL to optimize global equity portfolios, finds mixed results.

Bitcoin mining costs remain stable despite increased activity.

Study on distributed nonparametric function estimation with optimal rate and cost of adaptation.

The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learnin…

Improved BAI under DP reduces gap to constant.

Modeling climate change costs with stochastic interest rates shows inequality, but funding abatement can reduce this.

A new TwinGP framework for efficient large-scale GP modeling.

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devic…

New method reduces regret and communication costs in federated Q-learning.

BOKE optimizes expensive functions with reduced computational costs.

Study creates a global living index to assess quality of life.

The paper proves convergence to global optima for a class of distributed algorithms for nonconvex optimization in network-based multi-agent settings. Agents are permitted to communicate over a time-varying undirected graph. Each agent is assumed to possess a local objective function (assumed to be smooth, but possibly …

Model liquidity premia using a risk-sharing economy with quadratic costs.

For portfolio choice problems with proportional transaction costs, we discuss whether or not there exists a "shadow price", i.e., a least favorable frictionless market extension leading to the same optimal strategy and utility. By means of an explicit counter-example, we show that shadow prices may fail to exist even i…

Optimal crypto asset routing with CFMMs, including fixed costs.

Recently, GAIL framework and various variants have shown remarkable possibilities for solving practical MDP problems. However, detailed researches of low-level, and high-dimensional state input in this framework, such as image sequences, has not been conducted. Furthermore, the cost function learned in the traditional …

New findings show different cost functions yield equivalent curvature bounds.

Effective dimensionality reduction improves accuracy and reduces costs in estimating option Greeks.

Proposes glocal hypergradient estimation for hyperparameter optimization.

In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph. Assuming that each agent has access to a stochastic first-order oracle ($\mathcal{SFO}$), we propose a novel distribut…

Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of …

We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and t…

This paper analyzes air pollution trends in Rwanda using low-cost sensors and machine learning.

Gradient descent with preconditioning finds global optima in overparameterized nonconvex factorization.