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Fast, accurate and arbitrage-free volatility surface fitting remains a core challenge for options desks. Fabrice Deschâtres presents convex volatility interpolation (CVI), a framework that casts the ...

PDHCG is a high-performance, GPU-accelerated implementation of the Primal-Dual Hybrid Gradient (PDHG) algorithm designed for solving large-scale Convex Quadratic Programming (QP) problems. For a ...

Note: The official name for the course is “Convex optimization for electrical engineering”. However, the course is suitable for any student within SEAS (or beyond) provided you satisfy the math ...

OpenAI and Google DeepMind demonstrated that their foundation models could outperform human coders — and win — showing that large language models (LLMs) can solve complex, previously unsolved ...

Abstract: Mathematical programs with complementarity constraints are notoriously difficult to solve due to their nonconvexity and lack of constraint qualifications in every feasible point. This letter ...

This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; optimality conditions ...

Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China School of Chemical Engineering, University of Chinese Academy of ...

Abstract: This letter proposes an algorithm for solving finite-time nonlinear optimal control problems. The proposed method employs the Gauss pseudospectral method to transform the optimal control ...

ABSTRACT: A framework for the optimal sparse-control of the probability density function of a jump-diffusion process is presented. This framework is based on the partial integro-differential ...