Optimization

Randomized strategies improve truthful learning-augmented mechanisms for strategic facility location, achieving better approximations than deterministic methods.

Random Function Descent (RFD) replaces the classical convex function framework with a random function approach, providing a scalable gradient descent method with inherent scale invariance and a theore…

Random Cycle Coding (RCC) optimally compresses cluster assignments in large datasets, saving up to 70% storage in vector databases by eliminating the need for integer IDs.

Novel algorithms stabilize multi-server queueing systems with unknown service rates, achieving sublinear regret by learning server preferences via preference feedback.

Novel algorithms for query-efficient correlation clustering with noisy oracles achieve a balance between query complexity and solution quality, offering theoretical guarantees and outperforming baseli…

Quantum algorithms achieve speedups in non-smooth, non-convex optimization, outperforming classical methods by a factor of ε⁻²/³ in query complexity for finding (δ,ε)-Goldstein stationary points.

Quantum speedup achieved for large-scale market equilibrium computation!

Accelerated Lie group optimization achieved via a novel momentum algorithm (Lie NAG-SC) with proven convergence rates, surpassing existing methods in efficiency.

Q2VMC, a novel quantum chemistry algorithm, drastically boosts the efficiency and accuracy of solving the Schrödinger equation using a quadratic update mechanism and neural network ansatzes.

Researchers improve two-sided matching market algorithms by prioritizing stability through novel bandit-learning algorithms, providing theoretical bounds on sample complexity and demonstrating intrigu…

POETRY: a recursive neural theorem prover achieving 5.1% higher success rate and solving substantially longer proofs.

Faster bilevel optimization is achieved via without-replacement sampling, improving convergence rates compared to independent sampling methods.

Complex numbers boost neural network performance! This study proves that complex parameterizations in structured state space models (SSMs) enable more efficient and practical learning of complex mappi…

This paper proves Nesterov’s Accelerated Gradient achieves faster convergence for rectangular matrix factorization and linear neural networks, using a novel unbalanced initialization.

Progressive Entropic Optimal Transport (PROGOT) solvers efficiently and robustly compute optimal transport plans and maps, even at large scales, by progressively scheduling parameters.

Prod-family algorithms achieve optimal regret in adversarial multi-armed bandits, disproving prior suboptimality conjectures.

This paper presents novel, privacy-preserving algorithms achieving near-optimal rates for solving stochastic saddle point problems and variational inequalities in non-Euclidean geometries.

COBOL: a novel Bayesian Optimization algorithm leverages human expert advice via binary labels, achieving both fast convergence and robustness to noisy input, while guaranteeing minimal expert effort.

Pretrained Optimization Model (POM) excels at zero-shot black-box optimization, outperforming existing methods, especially in high dimensions, through direct application or few-shot fine-tuning.

New analysis reveals how reweighted least-squares algorithms for linear diagonal networks achieve favorable performance in high-dimensional settings, improving upon existing theoretical guarantees and…