ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables
Stochastic optimization is an important task in many optimization problems where the tasks are not expressible as convex optimization problems. In the case of non-convex optimization problems, various different stochastic algorithms like simulated annealing, evolutionary algorithms, and tabu search are available. Most of these algorithms require user-defined parameters specific to the problem in order to find out the optimal solution. Moreover, in many situations, iterative fine-tunings are required for the user-defined parameters, and therefore these algorithms cannot adapt if the search space and the optima changes over time. In this paper we propose an \underline{a}daptive parameter-free \underline{s}tochastic \underline{o}ptimization technique for \underline{c}ontinuous random variables called ASOC.
Code (0)
등록된 구현이 없습니다.
Tasks
Evolutionary AlgorithmsStochastic OptimizationSimilar Papers 제목 키워드 기반
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making
Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent actions, enhancing task diversity. We consi…
Decision MakingDiversityMulti-agent Reinforcement LearningAtlasOCR: Building the First Open-Source Darija OCR Model with Vision Language Models
Darija, the Moroccan Arabic dialect, is rich in visual content yet lacks specialized Optical Character Recognition (OCR) tools. This paper introduces AtlasOCR, the first open-source Darija OCR model built by fine-tuning …
Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
Achieving optimal rates for stochastic composite convex optimization without prior knowledge of problem parameters remains a central challenge. In the deterministic setting, the auto-conditioned fast gradient method has …
Parameter-free Algorithms for the Stochastically Extended Adversarial Model
We develop the first parameter-free algorithms for the Stochastically Extended Adversarial (SEA) model, a framework that bridges adversarial and stochastic online convex optimization. Existing approaches for the SEA mode…
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
This paper proposes a novel approach to adaptive step sizes in stochastic gradient descent (SGD) by utilizing quantities that we have identified as numerically traceable -- the Lipschitz constant for gradients and a conc…
image-classificationImage ClassificationStochastic Optimization