New "Prism" workspace launches just as studies show AI-assisted papers are flooding journals with diminished quality.

We walk through an optimization problem step by step, clearly explaining how to identify variables, set up the correct function, apply derivatives, and find maximum or minimum values. Each step is ...

Abstract: For the conjugate gradient method to solve the unconstrained optimization problem, given a new interval method to obtain the direction parameters, and a new conjugate gradient algorithm is ...

The HVAC system of public buildings, as a thermostatically controlled load, accounting for a relatively significant proportion of building energy consumption. Therefore, it is necessary to optimize ...

Abstract: This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) ...

The original version of this story appeared in Quanta Magazine. For computer scientists, solving problems is a bit like mountaineering. First they must choose a problem to solve—akin to identifying a ...

Heidi S. Enger ’27, an Associate Editorial Editor, is a Social Studies Concentrator in Eliot House. She’s enrolled in Ec10b this semester (don’t ask). Harvard students have to stop treating life like ...

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of ...