Alex L. Wang
Mathematics · Purdue University West Lafayette
Publications
36
Citations
42
Est. group size
—
Recurring co-author estimate
Active years
27
Publishing since 2000
Alex L. Wang works in mathematical optimization, focusing on designing and analyzing algorithms for convex and nonsmooth optimization problems, including gradient descent methods and semidefinite programming (a type of optimization involving matrix constraints). Much of the work studies theoretical questions such as how fast algorithms converge, how to characterize solvable problem classes (like quadratically constrained quadratic programs), and structural properties like convexity hidden within seemingly non-convex problems. This research is largely theoretical, aimed at improving the mathematical foundations and efficiency guarantees of optimization methods used across statistics, engineering, and data science.
Publication output has grown over the past decade, rising from little to no output around 2017-2019 to a peak of nine publications in 2024, with continued activity in 2025-2026.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Beyond minimax optimality: A subgame perfect gradient method
Mathematical Programming · 2026
- Sharpness and Conditioning of Nonsmooth Convex Formulations in Statistical Signal Recovery
SIAM Journal on Optimization · 2026
- Accelerated Objective Gap and Gradient Norm Convergence for Gradient Descent via Long Steps
INFORMS Journal on Optimization · 2025
- Kirigami analogies for parallelogram-based remote-center-of-motion mechanisms
Biomimetic Intelligence and Robotics · 2025
- Subgame Perfect Methods in Nonsmooth Convex Optimization
arXiv (Cornell University) · 2025
- Optimal Subgradient Methods for Lipschitz Convex Optimization with Error Bounds
arXiv (Cornell University) · 2025
- New notions of simultaneous diagonalizability of quadratic forms with applications to QCQPs
Mathematical Programming · 2024
- Hidden Convexity, Optimization, and Algorithms on Rotation Matrices
Mathematics of Operations Research · 2024
- On semidefinite descriptions for convex hulls of quadratic programs
Operations Research Letters · 2024
- Accelerated first-order methods for a class of semidefinite programs
Mathematical Programming · 2024
- On semidefinite descriptions for convex hulls of quadratic programs
arXiv (Cornell University) · 2024
- Accelerated Objective Gap and Gradient Norm Convergence for Gradient Descent via Long Steps
arXiv (Cornell University) · 2024
- A Strengthened Conjecture on the Minimax Optimal Constant Stepsize for Gradient Descent
arXiv (Cornell University) · 2024
- Composing Optimized Stepsize Schedules for Gradient Descent
arXiv (Cornell University) · 2024
- Introduction: Symposium: China & the Environment – Taking Stock of Domestic and Global Developments in Law & Governance
UCLA Pacific Basin Law Journal · 2024
- arXiv (Cornell University)×18
- Mathematical Programming×4
- Mathematics of Operations Research×2
- SIAM Journal on Optimization×2
- INFORMS Journal on Optimization×1
- Shixin Zheng
Mathematics · Purdue University West Lafayette
- Jose S. Rodriguez
Mathematics · Purdue University West Lafayette
- Chen Chen
Mathematics · The Ohio State University
- Yongzheng Dai
Mathematics · The Ohio State University
- Mohit Tawarmalani
Mathematics · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile