Study analyzes cost-benefit of CBM for unmanned systems.
problem Determining ROI for CBM strategies in unmanned systems.
method MDFTA with MCS for assessing maintenance requirements.
result Different CBM strategies can significantly impact ROI.
On-device research index
arXiv research
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.
169,051 papers · 148 categories
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48 results for “Cost components”
A new method for decision-focused learning reduces computational cost.
problem Efficiently solving combinatorial problems with uncertain parameters.
method Reframed as cost-sensitive multi-output regression, with novel loss components.
result Comparable downstream task quality with reduced computational cost.
Optimal Investment and Consumption Strategies with General Cost Structure under CRRA Utilityq-fin.PM
Study optimal investment and consumption strategies with various transaction costs.
problem Investment and consumption decisions under varying transaction costs.
method Dynamic programming and singular perturbation expansion for small cost-to-wealth ratio.
result Derive leading-order asymptotic formulas for no-trade regions and trading boundaries.
New method reduces mixture model evaluation cost for large models.
problem Computational infeasibility of evaluating all mixture components.
method Combining EM and Metropolis-Hastings for stochastic sampling.
result Significantly reduced computational cost for large models.
The Empirical Reality of IT Project Cost Overruns: Discovering A Power-Law Distributionphysics.soc-ph
Empirical study finds IT project costs follow a power-law distribution, exposing risk underestimation.
problem IT project cost overruns are underestimated due to normal distribution assumptions.
method Analyzed 5,392 IT projects to examine cost overruns following a power-law distribution.
result IT project cost overruns follow a power-law distribution with a fat tail of extreme overruns.
New approach estimates vehicle and component prices without teardowns.
problem Uncertainty in traditional vehicle price estimation methods.
method Data-driven machine learning and game theory.
result Shows large gaps in manufacturer pricing strategies.
Quaternion self-attention reduces computational cost and improves performance.
problem Existing quaternion self-attention increases computational cost and diverges attention distributions.
method Proposes a shared-score quaternion self-attention mechanism.
result Reduces score-computation multiplications by 75% and softmax operations from four to one.
Paper optimizes broker performance by estimating execution costs.
problem Minimizing execution costs for large trades.
method Intraday modeling of execution cost components (linear and quadratic).
result Substantial improvements in estimating execution costs.
New method uses parallel Monte Carlo for nonconvex optimization without gradients.
problem Optimizing complex functions with many components.
method Parallel sequential Monte Carlo for zeroth-order optimization.
result Converges to global minimum of cost function almost surely.
The paper identifies inconsistencies in post-crisis derivative pricing methods and derives no-arbitrage expressions.
problem Inconsistencies in post-crisis derivative pricing methods, particularly regarding cost components to a risk-free money account.
method Derives no-arbitrage expressions for default-risky derivative contracts with and without collateral.
result Avoids inconsistencies in derivative pricing methods by deriving no-arbitrage expressions.
New algorithm controls linear systems with bandit feedback, achieving optimal regret.
problem Controlling linear systems with bandit feedback under adversarial costs.
method Developed a new algorithm for linear control with memory optimization technique.
result Achieved optimal regret growth proportional to square root of time horizon.
Autoencoders reveal principal component subspaces.
problem Recovering principal component loadings from autoencoder weights.
method Using linear autoencoders with specific configurations.
result Autoencoder weights span the same subspace as principal component loadings.
A fast method for sparse PCA reduces computation time.
problem Time-consuming implementation of SPCA on high-dimensional data.
method Subspace projections using Household QR factorization for efficient deflation.
result Developed SPCA-SP method maintains good tradeoffs between various criteria.
Paper uses neural networks to speed up simulations of complex systems.
problem Rapid simulations of advection-dominated problems in engineering and geophysics.
method Recurrent neural network for approximating nonlinear component of ROM.
result The proposed framework accurately recovers transient dynamics without full nonlinear computations.
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
It was shown recently that the L1-norm principal components (L1-PCs) of a real-valued data matrix ( data samples of dimensions) can be exactly calculated with cost or, when advantageous, where $d=\mathrm{rank}(\mathbf …
New method for decomposing vectors into independent components over finite alphabets.
problem Decomposing vectors into independent components without prior generation assumptions.
method Branch and bound search tree algorithm, linear approximations, order permutation.
result Efficiently decomposes the majority of vectors into independent components.
Ownership Cost Calculations for Distributed Energy Resources Using Uncertainty and Risk Analysesecon.GN
Ownership cost calculation plays an important role in optimal operation of distributed energy resources (DERs) and microgrids (MGs) in the future power system, known as smart grid. In this paper, a general framework for ownership cost calculation is proposed using uncertainty and risk analyses. Four ownership cost calc…
Enhances Random Forest for imbalanced functional data classification.
problem Challenges in classifying imbalanced functional data.
method Functional Random Forest with Adaptive Cost-Sensitive Splitting (FRF-ACS).
result Significantly improves minority class recall and predictive performance.
Proposes a balanced multi-component and multi-layer neural network for efficient function approximation.
problem Accurately and efficiently approximating complex functions with high degrees of freedom and computational cost.
method Inspired by a multi-component approach, MMNN combines single-layer networks with a multi-layer decomposition strategy.
result Significant reduction in training parameters, more efficient training process, and improved accuracy compared to FCNNs or MLPs.
Develops a framework to assess infrastructure reliability under natural and malicious events.
problem Assessing reliability and costs of infrastructure under various hazards.
method Coupling mechanical reliability analyses with economical reliability analyses using probabilistic considerations.
result Indicators of probable cost of failure for infrastructure, aiding safety investments.
New algorithm reduces communication costs in distributed deep learning.
problem High communication costs in distributed deep learning.
method Sparse-SignSGD with Majority Vote (S3GD-MV).
result Significantly reduces communication costs while maintaining accuracy.
Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learning. In theory, under certain conditions, this problem can be solved in polynomial time via a natural convex relaxation, known as Compressive …
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
Study examines fraud detection methods for credit cards with limited data.
problem Data imbalance in credit card fraud detection.
method Assesses different sampling methods and machine learning algorithms.
result Monte Carlo analysis shows random undersampling outperforms SMOTE in fraud cost reduction.
New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
We propose an inference method to estimate sparse interactions and biases according to Boltzmann machine learning. The basis of this method is regularization, which is often used in compressed sensing, a technique for reconstructing sparse input signals from undersampled outputs. regularization impedes the …
A new approach solves optimal trading with linear costs, simplifying multi-asset problems.
problem Optimal trading with linear transaction costs in multi-asset markets.
method Mean-field approach reducing multi-asset to single-asset problem, incorporating risk aversion.
result Simple approximate solution for Ornstein-Uhlenbeck predictors with maximum position constraints.
We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an …
Optimal tuning of Tikhonov regularizers achieves best performance without additional cost.
problem Selecting the best estimator among Tikhonov regularized estimators or their linear combinations.
method Convex aggregation procedure based on -aggregation.
result Error term does not depend on penalty matrix or number of estimators.
New method optimizes submanifolds without explicit manifold details.
problem Optimization constrained to unknown or hard-to-access manifolds.
method Approximates missing manifold components using samples and intrinsic dimension.
result Global convergence of Riemannian optimization methods proven.
Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Diceq-fin.GN
The study optimizes supply chain management through a dice-based model to predict cleaner production.
problem Uncertainty in supply chain management and economic predictions.
method A 4-component SC module (environmental, demand, economic, social uncertainties) ranked by weight, using Analytical Hierarchical Process and optimization of a weighted cost function.
result Identifies conditions validating the sustainability of a business venture and optimizes market uncertainty.
UCRL-CMDP algorithm optimizes RL with constraints on average costs.
problem Optimizing RL in MDPs with average cost constraints.
method Model-based RL algorithms maximizing reward while keeping costs within bounds.
result UCRL-CMDP algorithm's expected regret is upper-bounded by $T^{2\slash 3}$.
Continuous-time interpolation of volatility surfaces preserving mixtures and arbitrage-free.
problem Interpolation of volatility surfaces
method Constructing a mixture-preserving, arbitrage-free interpolation
result Lifts Brigo-Mercurio to time-varying weights with additive cost
Study examines trading costs on Uniswap, finding adversarial slippage is significant for large trades and certain assets.
problem Analyzing costs and slippage in decentralized exchanges (DEXs).
method Empirical evaluation of Uniswap's USDC-ETH and PEPE-ETH pools, calculating slippage and reordering slippage.
result Adversarial slippage is significant for large trades and certain assets like PEPE.
Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Karda…
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulatorscs.LG
Policy gradient converges to globally optimal policy in nearly linear-quadratic systems.
problem Finding optimal policies in nonlinear control systems with partial information.
method Policy gradient algorithm designed for nearly linear-quadratic regulators with small Lipschitz nonlinear components.
result Policy gradient algorithm converges to globally optimal policy with linear rate.
Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algorithm and its variants, extract the independent components one after another. A novel method for deflationary ICA, referr…
Rotates MFVI for better Gaussian approximations.
problem Improving variational approximations for complex distributions.
method Rotated coordinate system, PCA-based rotation, iterative Gaussianization.
result Significantly more accurate approximations with lower computational cost.
Graph filtering improves data reconstruction performance.
problem Data reconstruction and dimensionality reduction.
method Formulate data tasks as graph filtering operations, optimize mean-square error cost involving adjacency matrix, update filters via gradient descent.
result Better reconstruction performance of novel method compared to PCA.
RL optimizes trading algorithms to reduce market impact and costs.
problem Optimizing sophisticated trading algorithms to minimize market impact and costs.
method Reinforcement learning framework within a market simulator.
result RL-derived strategies consistently outperform baselines and operate near the efficient frontier.
No arbitrage holds if a Pareto solution exists for vector-valued utility maximization.
problem Existence of no arbitrage in markets with transaction costs and multiple assets.
method Prove no arbitrage condition equivalent to Pareto solution for vector-valued utility maximization.
result A consistent price process can be constructed from the Pareto maximizer.
Bayesian-TPNN improves ANOVA-TPNN for detecting higher-order components.
problem Difficulty in incorporating higher-order components in ANOVA-TPNN due to computational and memory constraints.
method Bayesian inference procedure for functional ANOVA model with TPNN basis functions.
result Bayesian-TPNN detects higher-order components with reduced computational cost.
CAMS selects best pre-trained model for unlabeled data points.
problem Efficiently utilizing pre-trained models and unlabeled data.
method Contextual active model selection algorithm with two components: contextual model selection and active query.
result CAMS requires less than 10% labeling effort compared to existing methods, achieving similar or better accuracy.
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost…
Optimizes neural networks with blackbox solvers using Time-cost Regularization.
problem Improving neural network performance by integrating efficient solvers for complex problems.
method Optimizes both the primary loss function and the performance of the blackbox solver using Time-cost Regularization. Introduces a hyper-blackbox concept to learn blackbox parameters.
result Significant improvement in neural network performance through optimization of blackbox solvers.
Mixture models with Gamma and or inverse-Gamma distributed mixture components are useful for medical image tissue segmentation or as post-hoc models for regression coefficients obtained from linear regression within a Generalised Linear Modeling framework (GLM), used in this case to separate stochastic (Gaussian) noise…
Gradient Boosted Normalizing Flows improve flexibility of NFs without increasing complexity.
problem Improving flexibility of normalizing flows without increasing complexity.
method Gradient Boosting applied to normalizing flows to create a mixture model structure.
result GBNFs outperform non-boosted NFs and produce better results with simpler components.