The cloud revolution has brought about new business agility and scalability era. This revolution has enabled companies to access vast computing resources on demand, giving them the power to 8230 The post Maximizing efficiency with modern cloud pl...

A method is created to automatically increase the threshold projection parameter in three-field density-based topology optimization to achieve a near binary design. The parameter increase each iteration is based on an exponential growth function, where the growth rate is dynamically changed during optimization by linking it to the change in objective function. This results in a method that does not need to be tuned for specific problems, or optimizers, and the same set of hyper-parameters can be...

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. In this work, we study transfer learning (TL), a methodology to reuse pre-trained QAOA parameters of one problem instance into different COP instances. To this end, we select smal...

How does Dashloc India leverage hyperlocal MarTech to provide tailored solutions for retail businesses At DashLoc, we are revolutionizing retail management in India by harnessing hyperlocal MarTech. Through our innovative Global Business Platform, w...

I 39 m not developer, but managed to set up website. It 39 s not published yet, and haven 39 t added any optimization plugins or tools, but I 39 m looking for one to solve some issues. Whether paid or free, I 39 m open to suggestions for one or ...

We study a general scalarization approach via utility functions in multi-objective optimization. It consists of maximizing utility which is obtained from the objectives' bargaining with regard to a disagreement reference point. The theoretical framework for a broad class of utility functions from microeconomics is developed. For that, we associate a utility-dependent single-objective optimization problem with the given multi-objective optimization problem. We show that Pareto optimal points of t...

Egor Bogatov is an engineer working on C compiler technology at Microsoft. He had an intriguing remark about performance regression on Apple hardware following what appears to be an optimization. The .NET 9.0 runtime introduced the optimization wher...

Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively balances the risk and returns associated with investments. However, it is important to note that as the number of involved assets and constraints increases, the portfolio optimization problem can become increasingly difficult to solve, falling into the category of NP-hard problems. In such scenarios, classical algorithms,...

The benefit of quantum computing for solving combinatorial optimization problems (COPs) constitutes an open research question. In this work, we study the performance of a shrinking algorithm for COPs. The algorithm leverages correlations extracted from quantum or classical subroutines to recursively simplify the problem. We compare the performance of the algorithm equipped with correlations from the quantum approximate optimization algorithm (QAOA) as well as the classical linear programming (LP...

Optimizing performance is crucial for ensuring that Flutter applications run smoothly and provide responsive user experience. Here are x2026 Continue reading on Medium...

Tow steering technologies, such as Automated fiber placement, enable the fabrication of composite laminates with curvilinear fiber, tow, or tape paths. Designers may therefore tailor tow orientations locally according to the expected local stress state within a structure, such that strong and stiff orientations of the tow are (for example) optimized to provide maximal mechanical benefit. Tow path optimization can be an effective tool in automating this design process, yet has a tendency to creat...

Hello My name is Austin and Im software engineer and soloprenuer passionate about Artificial Intelligence and Finance. implemented an algorithm thats capable of generating population of algorithmic trading strategies that are better than the origina...

Optimizing the mRNA codon has an essential impact on gene expression for a specific target protein. It is an NP-hard problem; thus, exact solutions to such optimization problems become computationally intractable for realistic problem sizes on both classical and quantum computers. However, approximate solutions via heuristics can substantially impact the application they enable. Quantum approximate optimization is an alternative computation paradigm promising for tackling such problems. Recently...

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Linear Size Sparsifier and the Geometry of the Operator Norm Ball arXiv Continue reading on Medium...

Topology optimization is an important basis for the design of components. Here, the optimal structure is found within a design space subject to boundary conditions. Thereby, the specific material law has a strong impact on the final design. An important kind of material behavior is hardening: then a, for instance, linear-elastic structure is not optimal if plastic deformation will be induced by the loads. Since hardening behavior has a remarkable impact on the resultant stress field, it needs to...

Mutations and faces of the Thurston norm ball dynamically represented by multiple distinct flows arXiv Continue reading on Medium...

In this work, a family of symmetric interpolation points are generated on the four-dimensional simplex (i.e. the pentatope). These points are optimized in order to minimize the Lebesgue constant. The process of generating these points closely follows that outlined by Warburton in "An explicit construction of interpolation nodes on the simplex," Journal of Engineering Mathematics, 2006. Here, Warburton generated optimal interpolation points on the triangle and tetrahedron by formulating explicit ...

This is my SVR model that I m using. just need to improve the accuracy of my model by little and want to try GWO. What code do need to include import numpy as npimport pandas as pdfrom sklearn.model selection import train test splitfrom sklearn.prepr...