In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum their local objective functions over common decision variable, where information exchange is restricted between neighbors. To this end, one can first obtain problem reformulation and then apply alternating direction method multipliers (ADMM). The applies iterative computation at individual This approach has been observed to converge quickly deemed powerful. paper establishes its linear...

Consider the consensus problem of minimizing $f(x)=\sum_{i=1}^n f_i(x)$, where $x\in{\mathbb{R}}^p$ and each $f_i$ is only known to individual agent $i$ in a connected network $n$ agents. To solve this obtain solution, all agents collaborate with their neighbors through information exchange. This type decentralized computation does not need fusion center, offers better load balance, improves data privacy. paper studies gradient descent method [A. Nedic A. Ozdaglar, IEEE Trans. Automat....

This paper develops a distributed optimization strategy with guaranteed exact convergence for broad class of left-stochastic combination policies. The resulting diffusion is shown in Part II this to have wider stability range and superior performance than the EXTRA strategy. method applicable locally balanced matrices which, compared conventional doubly stochastic matrix, are more general able endow algorithm faster rates, flexible step-size choices, improved privacy-preserving properties....

We propose an asynchronous, decentralized algorithm for consensus optimization. The runs over a network in which the agents communicate with their neighbors and perform local computation. In proposed algorithm, each agent can compute independently at different times, durations, information it has even if latest from its is not yet available. Such asynchronous reduces time that would otherwise waste idle because of communication delays or are slower. It also eliminates need global clock...

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing sum local strongly-convex smooth components plus common term. We propose general primal-dual algorithmic framework that unifies many existing state-of-the-art algorithms. establish linear convergence proposed method to exact minimizer in presence Moreover, for more with agent specific terms, we show cannot be achieved (in worst case) algorithms uses gradients and...

This study drew on teacher survey responses from randomized experiments exploring three different pay-for-performance programs to examine the extent which these motivated teachers improve student achievement and impact of such teachers' instruction, number hours worked, job stress, collegiality. Results showed that most did not report their program as motivating. Moreover, suggest none changed increased worked or damaged Future research needs further logic model test alternative incentive...

Part I of this paper developed the exact diffusion algorithm to remove bias that is characteristic distributed solutions for deterministic optimization problems. The was shown be applicable larger set locally balanced left-stochastic combination policies than doubly-stochastic policies. These endow with faster convergence rate, more flexible step-size choices and better privacy-preserving properties. In II, we examine stability properties in some detail establish its linear rate. We also...

While light-emitting diodes (LEDs) are a very efficient lighting option, whether phosphor-coated LEDs (PC-LEDs) suitable for street remains to be tested. Correlated color temperature (CCT), mesopic vision illuminance, dark adaption, perception, fog penetration, and skyglow pollution important factors that determine light's suitability lighting. In this paper, we have closely examined the performance of LED lights with different temperatures found low-color-temperature (around 3000 K) PC-LEDs more

This paper develops a distributed variance-reduced strategy for collection of interacting agents that are connected by graph topology. The resulting diffusion-AVRG (where AVRG stands "amortized gradient") algorithm is shown to have linear convergence the exact solution, and more memory efficient than other alternative algorithms. When batch implementation employed, it observed in simulations computationally diffusion or EXTRA, while maintaining almost same communication efficiency.

Consider the consensus problem of minimizing $f(x)=\sum_{i=1}^n f_i(x)$ where each $f_i$ is only known to one individual agent $i$ out a connected network $n$ agents. All agents shall collaboratively solve this and obtain solution subject data exchanges restricted between neighboring Such algorithms avoid need fusion center, offer better load balance, improve privacy. We study decentralized gradient descent method in which updates its variable $x_{(i)}$, local approximate unknown $x$, by...

This work studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The information is assumed to be spread across agents in a network, where each agent observes some features. Through local cooperation, are supposed interact with other solve an inference converge towards global minimizer empirical risk. We study this exclusively primal domain, propose new effective distributed solutions guaranteed convergence linear rate strong convexity....

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These employ constant step-sizes converge linearly the solution under proper conditions. However, their performance stochastic adaptive settings is less explored. It still unknown whether, when why these can outperform traditional counterparts with noisy step-sizes. This work studies of setting, provides conditions...

This paper introduces a new algorithm for consensus optimization in multi-agent network, where all agents collaboratively find minimizer the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. One class use some or pairs at each iteration. Another uses random walk incremental strategy, which sequentially activates succession agents. Existing require diminishing step sizes to converge solution, and convergence is slow. In this work, we...

We study the consensus decentralized optimization problem where objective function is average of $n$ agents private non-convex cost functions; moreover, can only communicate to their neighbors on a given network topology. The stochastic learning setting considered in this paper each agent access noisy estimate its gradient. Many methods solve such including EXTRA, Exact-Diffusion/D$^2$, and gradient-tracking. Unlike famed DSGD algorithm, these have been shown be robust heterogeneity across...

Purpose: Surgical workflow analysis is crucial for improving surgical efficiency and safety. However, previous studies rely heavily on large-scale annotated datasets, posing challenges in cost, scalability, reliance expert annotations. To address this, we propose Surg-FTDA (Few-shot Text-driven Adaptation), designed to handle various tasks with minimal paired image-label data. Methods: Our approach has two key components. First, Few-shot selection-based modality alignment selects a small...

Large Language Models (LLMs) demonstrate exceptional performance across various tasks but demand substantial computational resources even for fine-tuning computation. Although Low-Rank Adaptation (LoRA) significantly alleviates memory consumption during fine-tuning, its impact on cost reduction is limited. This paper identifies the computation of activation gradients as primary bottleneck in LoRA's backward propagation and introduces Computation-Efficient LoRA (CE-LoRA) algorithm, which...

As double patterning lithography(DPL) becomes the leading candidate for sub-30 nm lithography process, we need a fast and friendly decomposition framework. In this paper, propose multi-objective min-cut based framework stitch minimization, balanced density, overlay compensation, simultaneously. The key challenge of DPL is to accomplish high quality large-scale layouts under reasonable runtime with following objectives: a) number stitches minimized, b) balance between two decomposed layers...

Standards-based accountability (SBA) has been a primary driver of education policy in the United States for several decades. Although definitions SBA vary, it typically includes standards that indicate what students are expected to know and be able do, measures student attainment standards, targets performance on those measures, set consequences schools or educations based performance. Research indicates these policies have led some its advocates had hoped achieve, such as an emphasis equity...

We examine the factor structure of scores from CLASS‐S protocol obtained observations middle school classroom teaching. Factor analysis has been used to support both interpretations observation protocols, like CLASS‐S, and theories about teaching that underlie them. However, contain multiple sources error, most predominantly rater errors. demonstrate errors in made by two raters on same lesson have a is distinct at teacher level. Consequently, “standard” approach analyzing teacher‐level...