Xbox Official Site: Consoles, Games and Community | Xbox
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Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, an…
The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations. This algorithm runs in quadratic time as it requires the full pairwise cost matrix, which is prohibitively expensive for large sets...
What Is On-Page SEO? How to Do On-Page Optimization in 2026
On-page SEO refers to optimizations on your website, while off-page SEO refers to optimization actions outside your website. Examples of on-site SEO efforts are creating and optimizing content according …
On-Page SEO: How to Optimize for Robots and Readers - Ahrefs
Aug 26, 2025 · Discover how to effectively optimize your website for both search engines and readers with our comprehensive guide on on-page SEO strategies and best practices.
Practical Batch Bayesian Optimization for Less Expensive Functions
Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions. However, these traditional BO approaches are not yet ideal for optimizing less expensive functions when the computational cost of BO can...
How to see which flags -march=native will activate?
Mar 29, 2011 · I'm compiling my C++ app using GCC 4.3. Instead of manually selecting the optimization flags I'm using -march=native, which in theory should add all optimization flags applicable to the …
COBRA++: Enhanced COBRA Optimizer with Augmented Surrogate Pool and Reinforced Surrogate S…
The optimization problems in realistic world present significant challenges onto optimization algorithms, such as the expensive evaluation issue and complex constraint conditions. COBRA optimizer (including its up-to-date variants) is a representativ...
Real-time generative design of diverse, "truly" optimized structures with controllable str…
Compared with traditional design methods, generative design significantly attracts engineers in various disciplines. In thiswork, howto achieve the real-time generative design of optimized structures with various diversities and controllable structur...
Timing and Code Size Optimization on Achieving Full Parallelism in Uniform Nested Loops
Multidimensional Retiming is one of the most important optimization techniques to improve timing parameters of nested loops. It consists in exploring the iterative and recursive structures of loops to redistribute computation nodes on cycle periods,...
PyTorch Hyperparameter Tuning - A Tutorial for spotPython
The goal of hyperparameter tuning (or hyperparameter optimization) is to optimize the hyperparameters to improve the performance of the machine or deep learning model. spotPython (``Sequential Parameter Optimization Toolbox in Python'') is the Python...
Xbox Official Site: Consoles, Games and Community | Xbox
Shop all console games Shop all PC games Cloud games Xbox Play Anywhere Free-to-Play games Optimized for Xbox Series X|S Optimized for handhelds
The Implications of Tolerance Optimization on Compressor Blade Design
Geometric variability increases performance variability and degrades the mean performance of turbomachinery compressor blades. These detrimental effects can be reduced by using robust optimization to design the blade geometry or by imposing stricter...
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training
Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks. The efficacy of software-level optimizations can vary significantly when used in different deployment configurations. It is onerous and error-prone...
Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems
In this paper, we introduce a new heuristics for global optimization in scenarios where extensive evaluations of the cost function are expensive, inaccessible, or even prohibitive. The method, which we call Landscape-Sketch-and-Step (LSS), combines M...
Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization
Performance optimization is an increasingly challenging but often repetitive task. While each platform has its quirks, the underlying code transformations rely on data movement and computational characteristics that recur across applications. This pa...
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censori…
We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the trea...
Optimizing Supercell Structures for Heisenberg Exchange Interaction Calculations
In this paper, we introduce an efficient, linear algebra-based method for optimizing supercell selection to determine Heisenberg exchange parameters from DFT calculations. A widely used approach for deriving these parameters involves mapping DFT ener...
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Browse cars and read independent reviews from Optimus Auto Care in Golden, CO. Click here to find the car you’ll love near you.
Shortermism and excessive risk taking in optimal execution with a target performance
We deal with the optimal execution problem when the broker's goal is to reach a performance barrier avoiding a downside barrier. The performance is provided by the wealth accumulated by trading in the market, the shares detained by the broker evaluat...
Kira 3: integral reduction with efficient seeding and optimized equation selection
We present version 3 of Kira, a Feynman integral reduction program for high-precision calculations in quantum field theory and gravitational-wave physics. Building on previous versions, Kira 3 introduces optimized seeding and equation selection algor...
Optimization-Based Trajectory Planning for Tractor-Trailer Vehicles on Curvy Roads: A Prog…
In this work, we propose an optimization-based trajectory planner for tractor-trailer vehicles on curvy roads. The lack of analytical expression for the trailer's errors to the center line pose a great challenge to the trajectory planning for tractor...
AI powered business process optimization | Flowable Platform
Automate complex business with Flowable's intuitive AI-powered business process optimization platform. Start your free trial!
Minimal Investment Risk of Portfolio Optimization Problem with Budget and Investment Conce…
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investmen...
AI powered business process optimization | Flowable Platform
Automate complex business with Flowable's intuitive AI-powered business process optimization platform. Start your free trial!
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linea…
We study a localized notion of uniform convergence known as an "optimistic rate" (Panchenko 2002; Srebro et al. 2010) for linear regression with Gaussian data. Our refined analysis avoids the hidden constant and logarithmic factor in existing results...
Optimizing spatial throughput in device-to-device networks
Results are presented for optimizing device-to-device communications in cellular networks, while maintaining spectral efficiency of the base-station-to-device downlink channel. We build upon established and tested stochastic geometry models of signal...
Relaxations for multi-period optimal power flow problems with discrete decision variables
We consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as multi-period formulations of the OPF with discrete variables that define...
Cryptocurrency Equilibria Through Game Theoretic Optimization
Optimization methods are used to determine equilibria of investment in cryptocurrencies. The basic assumptions involve existence of a core group (the "wealthy") that fears the loss of substantial assets through government seizure. Speculators constit...
Optimal Self-Dual Z4-Codes and a Unimodular Lattice in Dimension 41
For lengths up to 47 except 37, we determine the largest minimum Euclidean weight among all Type I Z4-codes of that length. We also give the first example of an optimal odd unimodular lattice in dimension 41 explicitly, which is constructed from some...
Ensuring Network Connectedness in Optimal Transmission Switching Problems
Network connectedness is indispensable for the normal operation of transmission networks. However, there still remains a lack of efficient constraints that can be directly added to the problem formulation of optimal transmission switching (OTS) to en...
Implicit differentiation of Lasso-type models for hyperparameter optimization
Setting regularization parameters for Lasso-type estimators is notoriously difficult, though crucial in practice. The most popular hyperparameter optimization approach is grid-search using held-out validation data. Grid-search however requires to cho...
AI powered business process optimization | Flowable Platform
Automate complex business with Flowable's intuitive AI-powered business process optimization platform. Start your free trial!
First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or wi…
Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we show for any Lipschitz, weakly convex objectives and constraints, a simp...
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchica…
Designing the optimal linear quadratic regulator (LQR) for a large-scale multi-agent system (MAS) is time-consuming since it involves solving a large-size matrix Riccati equation. The situation is further exasperated when the design needs to be done...
Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
We address the problem of Gaussian Process (GP) optimization in the presence of unknown and potentially varying adversarial perturbations. Unlike traditional robust optimization approaches that focus on maximizing performance under worst-case scenari...
Satisficing in Time-Sensitive Bandit Learning
Much of the recent literature on bandit learning focuses on algorithms that aim to converge on an optimal action. One shortcoming is that this orientation does not account for time sensitivity, which can play a crucial role when learning an optimal a...
On Satisficing in Quantitative Games
Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games. {\em Optimization} is one form of analysis. We argue that in many cases it may be better to replace the optimization problem wi...
No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Dif…
This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output black-box function subject to noisy observations, which often arises...
GZR-Kernels/Optimus_Drunk_Beryllium
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D-optimal designs via a cocktail algorithm
A fast new algorithm is proposed for numerical computation of (approximate) D-optimal designs. This "cocktail algorithm" extends the well-known vertex direction method (VDM; Fedorov 1972) and the multiplicative algorithm (Silvey, Titterington and Tor...