cost modeling

Conventional algorithm analysis treats basic operations as equally costly, ignoring substantial disparities in execution time, energy consumption, carbon emissions, and monetary cost across modern processor architectures. Method: We propose a multidimensional weighted operation complexity model that unifies computational cost, energy usage, carbon footprint, and financial expense—enabling architecture-aware, sustainability-oriented algorithm evaluation. Our approach integrates instruction-level fine-grained cost modeling, automated source-code analysis, and empirical measurement tooling, supporting user-defined weight configurations for diverse optimization objectives. Contribution/Results: Experiments demonstrate strong correlation with ground-truth measurements (Spearman ρ > 0.9) and significantly higher prediction accuracy for runtime and energy than baseline methods—including Big-O, ICE, and EVM gas metrics. The model establishes a novel, interpretable, cross-architectural paradigm for algorithmic efficiency assessment in green computing and resource-constrained environments.

This paper addresses the generation of algorithmic recourse for individuals seeking actionable counterfactual recommendations in decision-making systems, under multi-criteria, non-differentiable, and discrete cost functions—settings where gradient-based methods fail due to their reliance on differentiability, compromising solution optimality, interpretability, and theoretical guarantees. Method: We formulate recourse search as a weighted multi-objective optimization problem and, for the first time, leverage ε-net theory to rigorously establish the existence and computability of approximate Pareto-optimal solutions. Our approach eliminates gradient dependence, enabling direct modeling of non-differentiable and discrete costs, and introduces a graph-structured, scalable solution framework. Results: Experiments demonstrate efficiency and scalability on large graphs; quantitative analysis reveals multidimensional cost trade-offs; and the method significantly improves solution quality, theoretical rigor, and real-world applicability—thereby strengthening interpretability and fairness guarantees essential for XAI.

This study addresses the challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

Optimization model developers face significant adoption barriers, low stakeholder trust, and poor communication in real-world domains such as healthcare and logistics. Method: We conducted a qualitative empirical study involving semi-structured interviews with 15 cross-domain practitioners to investigate optimization practices in situ. Contribution/Results: Our analysis reveals a highly iterative, six-stage optimization practice pattern, identifying continuous data processing and sustained stakeholder dialogue as the core mechanisms driving optimization decisions. Moving beyond the traditional “algorithm-centric” paradigm, we propose a novel “data-and-dialogue co-driven” framework that reconceptualizes optimization as a sociotechnical process—not merely a technical one. This framework provides empirically grounded design principles for developing more transparent, interpretable, and human-centered optimization support tools, thereby bridging critical gaps between technical modeling and organizational implementation.

This study addresses a critical gap in current AI efficiency evaluations, which typically focus only on isolated training or inference phases and fail to capture the full lifecycle resource consumption and environmental impact of AI systems. To overcome this limitation, the work introduces, for the first time, a comprehensive Life Cycle Assessment (LCA) framework tailored to machine learning. This approach systematically integrates energy use and embedded environmental costs across all stages—including hardware manufacturing, model training, and deployment—thereby transcending the narrow scope of conventional assessments. By providing a holistic and accurate methodology for evaluating sustainability, the proposed framework offers researchers, developers, and policymakers a robust tool to guide more environmentally responsible design, deployment, and regulation of AI technologies.

Traditional quantitative investment systems typically optimize a single metric—such as the information ratio—and thus struggle to meet professional investors’ multifaceted objectives, including pure alpha generation, style control, drawdown resilience, and turnover and capacity constraints. This work proposes an Objective-Oriented Quantitative Investment (OOQI) framework that formally encodes investment intent as strategy specifications and compiles them into composable, constraint-satisfying strategy assemblies. Key innovations include establishing a dual lattice structure between specifications and assemblies, designing a satisfaction-driven synthesis mechanism, and introducing rolling recertification via e-process-based validation. Empirical results demonstrate that the specification-driven approach satisfies 100% of target constraints across 32 strategies, at the cost of only a 5.5% reduction in information ratio, whereas conventional outcome-oriented methods—despite higher in-sample information ratios—fulfill merely 25% of the specified requirements.

This work introduces, for the first time, fundamental thermodynamic limits into basic machine learning algorithms by leveraging Landauer’s principle and information thermodynamics to quantify the minimum irreversible energy dissipation incurred by floating-point implementations of simple linear regression. By constructing an entropy production model for continuous inputs, the study analyzes the thermodynamic costs associated with both exact solutions and stochastic gradient descent. Furthermore, it derives the optimal scaling law between training set size and energy consumption under a prescribed generalization error constraint. This paper establishes the first theoretical framework characterizing the trade-off between energy efficiency and generalization in linear regression, thereby providing a physical foundation for the design of energy-aware machine learning systems.

This work addresses the lack of systematic methodologies in model optimization, which often relies on heuristic choices and struggles to accommodate diverse deployment constraints. It formalizes model compression and acceleration as a constraint-aware multi-objective engineering decision problem, establishing a unified and actionable framework grounded in five key dimensions: data availability, latency, memory footprint, accuracy tolerance, and retraining budget. By integrating techniques such as quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference optimization, the study proposes tailored optimization pipelines for four representative industrial scenarios, delivering a reproducible and quantifiable guide for technology selection.

This work addresses the challenge of selecting and optimizing recovery pathways under heterogeneous experimental cost constraints in autonomous materials discovery. The authors propose a two-stage sequential decision framework: first, a cost-aware active hypothesis discrimination method—based on EC2—is employed to identify high-potential pathways; subsequently, Gaussian process Bayesian optimization refines performance within the selected pathways. This approach uniquely integrates cost-sensitive hypothesis testing with Bayesian optimization, enabling efficient exploration without requiring prior labels of correct pathways and providing theoretical bounds on expected cost. Experiments on a CICERO-inspired synthetic benchmark demonstrate that the method matches the performance of an oracle optimizer, significantly outperforms split-plate baselines, and effectively avoids performance degradation caused by erroneously selecting hydroxide pathways.