arXiv Machine Learning
Aug 13
Basin: Efficient and Extensible Numerical Optimization in Rust
arXiv:2608. 11279v1 Announce Type: new Abstract: Basin is a numerical optimization library for the Rust programming language.
Generation of Custom Solvers in Rust for Convex Optimization
The paper presents cvxgenrust, an open‑source tool that generates custom Rust code for solving families of parameterized convex optimization problems defined in CVXPY. It canonicalizes problem families, extracts affine maps to Clarabel cone‑program data, and produces a specialized Rust crate that updates parameters and calls Clarabel natively at runtime. The generated solver can also be exposed to Python and registered as a custom CVXPY solver, supporting a wide range of convex problems up to semidefinite and exponential‑cone programs, and demonstrates reduced runtime compared to direct CVXPY solves and performance comparable to CVXPYgen.
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Aug 11
Basin: Efficient and Extensible Numerical Optimization in Rust
Basin is a numerical optimization library for the Rust programming language. Numerical optimization is the task of finding the inputs that minimize a function, and it is a fundamental element across the sciences: fitting a model to data, calibrating a simulation, training a machine learning model, or choosing engineering parameters that minimize cost.
arXiv Machine Learning
Sep 10
GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
arXiv:2609. 08136v1 Announce Type: new Abstract: This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA).
arXiv Machine Learning
Sep 10
Accelerated Frank-Wolfe Algorithms: Complementarity Conditions and Sparsity
arXiv:2511.02821v2 Announce Type: replace-cross Abstract: We develop new accelerated first-order algorithms in the Frank-Wolfe (FW) family for minimizing smooth convex functions over compact convex s...
arXiv Machine Learning
Jun 16
A Penalty Approach for Differentiation Through Black-Box Quadratic Programming Solvers
arXiv:2602. 14154v3 Announce Type: replace Abstract: Differentiating through the solution of a quadratic program (QP) is a central problem in differentiable optimization.
arXiv Machine Learning
Sep 2
Disciplined Bilevel Programming
arXiv:2609.00644v1 Announce Type: cross Abstract: Bilevel optimization provides a natural modeling language for hierarchical decision problems. However, applying existing numerical solvers usually re...
arXiv Machine Learning
Sep 4
Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming
The paper introduces a GPU-resident, batched Levenberg–Marquardt solver that efficiently optimizes constants in tree-based genetic programming for symbolic regression. By using reverse-mode automatic differentiation to assemble per-tree Jacobians in a single backward sweep, the solver’s per-iteration cost becomes independent of the number of constants per tree, achieving up to 510,000 trees per second on an NVIDIA A100. Integrated into EvoGP, the solver enables end-to-end search that recovers governing equations on 10 of 18 constructed problems, a significant improvement over stock EvoGP.
arXiv Machine Learning
Jun 15
Scalable Deep Unfolding of Conic Optimizers
arXiv:2606. 13825v1 Announce Type: cross Abstract: Deep unfolding (DU) accelerates iterative optimizers by introducing learnable components and training them through unrolled iterations, but extending DU to the large-scale semidefinite programs (SDPs) common in robotics has remained limited.
arXiv Machine Learning
Jun 16
A Fully First-Order Layer for Differentiable Optimization
arXiv:2512. 02494v2 Announce Type: replace Abstract: Differentiable optimization layers enable learning systems to make decisions by solving embedded optimization problems.
arXiv AI
Jun 9
Accelerating Birkhoff Projection for Manifold-Constrained Hyper-Connections
arXiv:2606. 07574v1 Announce Type: cross Abstract: Manifold-constrained hyper-connections (mHCs) have recently been proposed as a principled extension of hyper-connections, where the residual mixing matrices are constrained to be doubly stochastic via projection onto the Birkhoff polytope.
arXiv Machine Learning
Jul 27
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.
arXiv Machine Learning
Jun 5
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
arXiv:2508. 00775v2 Announce Type: replace-cross Abstract: The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems.