Yusuke MIYAKE / Pepabo R&D Institute, GMO Pepabo, Inc. 2019.07.25 GopherCon 2019 Optimization for number of goroutines using feedback control Principal engineer Yusuke MIYAKE @monochromegane Pepabo

this using the earlier example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering hyperparameters. Some of the commonly tuned hyperparameters are the learning rate and the momentum of the optimization algorithm and the training batch size. Other aspects of the training pipeline like data augmentation quality and footprint) that each string produces. Unlike the guitar which has a few knobs, the hyperparameter search space can be quite large. Moreover, unlike a guitar knob which is associated with a single

Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting 人工智能服务 视觉 语音 语言 聊天机器人 预测 推荐 Personalize Forecast Lex Translate Comprehend Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting 人工智能服务 视觉 语音 语言 聊天机器人 预测 推荐 Personalize Forecast Lex Translate Comprehend Direct Marketing with Amazon SageMaker XGBoost and Hyperparameter Tuning https://github.com/awslabs/amazon-sagemaker- examples/blob/master/hyperparameter_tuning/xgboost_direct_marketing/hpo_xgboost _dir

001之间，就会有更多的搜索资源可用，还有在0.001到0.01之间等等。 20 超参数调整的方法 Hyperparameter 1 Hyperparameter 2 Hyperparameter 1 Hyperparameter 2 21 由粗到细调整超参数 Hyperparameter 1 Hyperparameter 2 22 熊猫方式与鱼子酱方式 由计算资源决定 23 Batch Norm

rvice/ Pipeline Images: https://github.com/pachyderm/pachyderm/tree/master/doc/examples/ml/hyperparameter 谢谢！

最终，我们真正关心的是生成一个模型，它能够在从未见过的数据上表现良好。但“训练”模型只能将模型 与我们实际能看到的数据相拟合。因此，我们可以将拟合模型的任务分解为两个关键问题： • 优化（optimization）：用模型拟合观测数据的过程； 39 https://discuss.d2l.ai/t/1751 2.4. 微积分 63 • 泛化（generalization）：数学原理和实践者的 size）。η表示学习率（learning rate）。批量 大小和学习率的值通常是手动预先指定，而不是通过模型训练得到的。这些可以调整但不在训练过程中更新 的参数称为超参数（hyperparameter）。调参（hyperparameter tuning）是选择超参数的过程。超参数通常 88 3. 线性神经网络 是我们根据训练迭代结果来调整的，而训练迭代结果是在独立的验证数据集（validation dataset）上评估得 ∇2f）。这相当于根据半正定矩阵的定义，H ⪰ 0。 11.2.3 约束 凸优化的一个很好的特性是能够让我们有效地处理约束（constraints）。即它使我们能够解决以下形式的约 束优化（constrained optimization）问题： minimize x f(x) subject to ci(x) ≤ 0 for all i ∈ {1, . . . , N}. (11.2.16) 这里f是目标函数，ci是约束函数。例如第一个约束c1(x)

downloads. The process of building an open source OS relies on supply chain aggregation and optimization. A reliable open source software supply chain is fundamental to a large-scale commercial OS an HPC deployment tuning assistant that significantly reduces deployment costs and improves optimization efficiency. Project Introduction HPCRunner is composed of two parts: HPC dependency management Scheduler offers several features, including topology discovery and export, scheduling support and optimization, and user-mode topology support. • The Linux ACPI and topology driver can enumerate and create

+ ? ≥ 1 Negative class: ?!? + ? ≤ −1 ? = 1 ? Figure 2: Hard-margin SVM. aim of the above optimization problem is to find a hyperplane (parameterized by ω and b) with margin γ = 1/∥ω∥ maximized, while training set. 2.2 Preliminary Knowledge of Convex Optimization 2.2.1 Optimization Problems and Lagrangian Duality We now consider the following optimization problem min ω f(ω) (9) s.t. gi(ω) ≤ 0, i = 1 gk(ω) and the equality constraints h1(ω), · · · , hl(ω). We construct the Lagrangian of the above optimization problem as L(ω, α, β ) = f(ω) + k � i=1 αigi(ω) + l � j=1 β jhj(ω) (12) In fact, L(ω, α

reserved. © 2024 Bloomberg Finance L.P. All rights reserved. Can you RVO? Using Return Value Optimization for Performance in Bloomberg’s C++ Codebases CppCon 2024 September 16, 2024 Michelle D’Souza RVO? How many people here have heard about “Return Value Optimization”? 2 How many people here are experts on “Return Value Optimization”?© 2018 Bloomberg Finance L.P. All rights reserved. © 2024 Finance L.P. All rights reserved. © 2024 Bloomberg Finance L.P. All rights reserved. Return Value Optimization (RVO) 5© 2018 Bloomberg Finance L.P. All rights reserved. Agenda Questions at the end Source:

Outline 1 SVM: A Primal Form 2 Convex Optimization Review 3 The Lagrange Dual Problem of SVM 4 SVM with Kernels 5 Soft-Margin SVM 6 Sequential Minimal Optimization (SMO) Algorithm Feng Li (SDU) SVM December 28, 2021 15 / 82 Convex Optimization Review Optimization Problem Lagrangian Duality KKT Conditions Convex Optimization S. Boyd and L. Vandenberghe, 2004. Convex Optimization. Cambridge university press press. Feng Li (SDU) SVM December 28, 2021 16 / 82 Optimization Problems Considering the following optimization problem min ω f (ω) s.t. gi(ω) ≤ 0, i = 1, · · · , k hj(ω) = 0, j = 1, · · · , l with