Fruchter, G. (2026) Opportunism in Supply Chain Recommendations: A Dynamic Optimization Approach. Modern Economy, 17, 26-38.

Primal-dual methods in online optimization give several of the state-of-the art results in both of the most common models: adversarial and stochastic/random order. Here we try to provide a more ...

What if the next new mathematical discovery didn’t come from a human mind, but from an AI? Imagine a machine not just crunching numbers but proposing original solutions to problems that have baffled ...

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) ...

Current AI models struggle to solve research-level math problems, with the most advanced AI systems we have today solving just 2% of the hundreds of challenges faced. When you purchase through links ...

Abstract: This paper presents a decentralized algorithm for solving distributed convex optimization problems in dynamic networks with time-varying objectives. The unique feature of the algorithm lies ...

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In this talk we introduce a novel modeling and computational framework for joint discrete and continuous decision making. We consider graphs where each vertex is associated with a convex optimization ...

1 School of Mathematical Science, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana. 2 Department of Mathematics, University for Development Studies, Tamale, Ghana. In this ...