Stochastic optimization methods. Medical search. Frequent questions

I've never seen any optimization algorithms do this, so why not? (reddit.com)
In fact, now people are also using those methods to design such algorithms (e.g. assigning 'good' values to its parameters) with certain guaranteed properties such as a guaranteed rate of convergence or so. (reddit.com)
The way in which results of stochastic optimization algorithms are usually presented (e.g., presenting only the average, or even the best, out of N runs without any mention of the spread), may also result in a positive bias towards randomness. (wikipedia.org)
Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. (nsf.gov)
DenoiseLab: a standard test set and evaluation method to compare denoising algorithms. (crossref.org)
This paper continues the line of work on stochastic adaptive algorithms studied in (Berahas et. (ibm.com)
We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth finite-sum problems. (deepai.org)
We propose and analyze several stochastic gradient algorithms for findin. (deepai.org)
Two types of zeroth-order stochastic algorithms have recently been desig. (deepai.org)
Inspired by the geometrical insights of these two emerging fields of optimization theory, this thesis studies Stochastic Derivative-Free Optimization (SDFO) algorithms over Riemannian manifolds from a geometrical perspective. (bham.ac.uk)
We propose a generalized framework for adapting SDFO algorithms on Euclidean spaces to Riemannian manifolds, which encompasses known methods such as Riemannian Covariance Matrix Adaptation Evolutionary Strategies (Riemannian CMA-ES). (bham.ac.uk)
I am a member of the research group Stochastic Algorithms and Nonparametric Statistics of the Weierstrass Institute for Applied Analysis and Stochastics . (wias-berlin.de)
An overview of how stochastic gradient Markov chain Monte Carlo algorithms can be used for computationally efficient Bayesian inference. (lancaster.ac.uk)
We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. (nips.cc)
Our algorithms build upon the inverse sensitivity mechanism, which adapts to instance difficulty [2], and recent localization techniques in private optimization [25]. (nips.cc)
Sierra's work now focuses on guaranteeing the future performance of the algorithms, with the goal of developing rapid learning methods liable to cope with major changes of scale without a significant loss of efficacy, Francis Bach concludes. (inria.fr)

2014) with stochastic step search analysis in (Paquette and Scheinberg, 2020). (ibm.com)

Stochastic optimization methods generalize deterministic methods for deterministic problems. (wikipedia.org)
Artificial neural network (ANN) methods in general fall within this category, and par- ticularly interesting in the context of optimization are recurrent network methods based on deterministic annealing. (lu.se)

Approximate posterior sampling via stochastic optimisation (2019). (lancaster.ac.uk)

Nonsmooth nonconvex optimization problems broadly emerge in machine learning and business decision making, whereas two core challenges impede the development of efficient solution methods with finite-time convergence guarantee: the lack of computationally tractable optimality criterion and the lack of computationally powerful oracles. (deepai.org)
Second, we propose the gradient-free method (GFM) and stochastic GFM for solving a class of nonsmooth nonconvex optimization problems and prove that both of them can return a (δ,ϵ)-Goldstein stationary point of a Lipschitz function f at an expected convergence rate at O(d^3/2δ^-1ϵ^-4) where d is the problem dimension. (deepai.org)

When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required. (optimization-online.org)

Instead, various kinds of heuristic methods have been developed to yield reasonably good approximate solutions. (lu.se)

In the context of optimization under uncertainty, we consider various combinations of distribution estimation and resampling (bootstrap and bagging) for obtaining samples used to acquire a solution and for computing a confidence interval for an optimality gap. (optimization-online.org)
Partly random input data arise in such areas as real-time estimation and control, simulation-based optimization where Monte Carlo simulations are run as estimates of an actual system, and problems where there is experimental (random) error in the measurements of the criterion. (wikipedia.org)
A first Monte Carlo exercise illustrates the accuracy of the method for estimation and inference in a probit IV regression. (aeaweb.org)
COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques. (harvard.edu)
The first method is the spheric radial decomposition and the second method is a kernel density estimation. (springer.com)
The course deals with model building and estimation in non-linear dynamic stochastic models for financial systems. (lu.se)
The course participants will also meet statistical methods, such as Maximum-likelihood and (generalized) moment methods for parameter estimation, kernel estimation techniques, non-linear filters for filtering and prediction, and particle filter methods. (lu.se)
The point estimation method for the probability of sliding is efficient and expedites slope stability simulation routines in NIOSH software to stochastically describe rock slope behavior and assist engineers in catch bench design for large slopes. (cdc.gov)

We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. (optimization-online.org)
why not use updates similar to the ones above for numerical approximations of solutions to initial value problems, like an 'extended' Euler's method? (reddit.com)
International Journal of Mathematical Modelling and Numerical Optimisation. (wikipedia.org)
Numerical results demonstrate the merits of our algorithm over existing methods. (nsf.gov)
In this case, mathematical models contain sets of numerical parameters and the search for exact values for them presents a complex optimization problem. (sciencepubco.com)
The purpose of this paper is to investigate the possibility of using stochastic optimization methods to determine the exact numerical values of the calculated parameters of mathematical models that mimic the behavior of a structured composite material with given physico-mechanical characteristics under operating conditions. (sciencepubco.com)
The primary aim of these works is to design numerical methods that attain global and fast local convergence guarantees. (wikicfp.com)
Scientific Computing is a branch within computational science where analytical, numerical and statistical methods are used to analyse and draw conclusions from physical models, as well as huge datasets from physics experiments. (lu.se)

One of the chief attractions of stochastic mixed-integer second-order cone programming is its diverse applications, especially in engineering (Alzalg and Alioui, {\em IEEE Access}, 10:3522-3547, 2022). (optimization-online.org)

We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of the optimal solution. (optimization-online.org)
For strongly convex problems, we establish linear convergence for the SUCAG method. (nsf.gov)
When the initialization point is sufficiently close to the optimal solution, the established convergence rate is only dependent on the condition number of the problem, making it strictly faster than the known rate for the SAGA method. (nsf.gov)
The classical analysis of convergence of SGD is carried out under the assumption that the norm of the stochastic gradient is uniformly bounded. (nsf.gov)
2016) a new analysis of convergence of SGD is performed under the assumption that stochastic gradients are bounded with respect to the true gradient norm. (nsf.gov)
algorithm in the same regime, obtaining the first convergence results for this method in the case of diminished learning rate. (nsf.gov)
It recovers several existing convergence results (in terms of the number of stochastic gradient oracle calls and proximal operations), and improves/generalizes some others. (deepai.org)
In this paper we address the convergence of stochastic approximation whe. (deepai.org)
and uniform smoothing, thereby providing the basis and intuition for the design of gradient-free methods that guarantee the finite-time convergence to a set of Goldstein stationary points. (deepai.org)

We present a gradient-based algorithm for solving a class of simulation optimization problems in which the objective function is the quantile of a simulation output random variable. (ssrn.com)
We consider unconstrained stochastic optimization problems with no available gradient information. (optimization-online.org)
Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. (optimization-online.org)
We describe the applications to some of discrete programming problems, such as optimization of mixed Boolean bilinear functions including the scheduling of batch operations and the optimization of neural networks. (iospress.com)
The linear and nonlinear versions of this class of optimization problems are still unsolved yet. (optimization-online.org)
Stochastic programming (SP) is a well-studied framework for modeling optimization problems under uncertainty. (optimization-online.org)
For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints. (wikipedia.org)
Some stochastic optimization methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization. (wikipedia.org)
The paper analyzes stochastic optimization problems involving random fields on infinite directed graphs. (manchester.ac.uk)
Besides, to handle the large-scale kernelized learning problems, we propose a scalable algorithm called QS 3 ORAO using the doubly stochastic gradients (DSG) framework for functional optimization. (aaai.org)
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems. (nsf.gov)
Here we show that for stochastic problems arising in machine learning such bound always holds. (nsf.gov)
In this paper, we propose a stochastic gradient-based method for solving graph-structured sparsity constraint problems, not restricted to the least square loss. (nsf.gov)
In this paper, we propose an accelerated stochastic step search algorithm which combines an accelerated method with a fully adaptive step size parameter for convex problems in (Scheinberg et. (ibm.com)
Nonconvex and nonsmooth optimization problems are important and challeng. (deepai.org)
In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale optimization problems. (deepai.org)
We study conditional stochastic optimization problems, where we leverage. (deepai.org)
Julia Dynamic Generation Expansion (JuDGE) is a Julia package for solving stochastic capacity expansion problems formulated in a "coarse-grained" scenario tree that models long-term uncertainties. (birs.ca)
The Extended RSDFO is compared to Riemannian Trust-Region method, Riemannian CMA-ES and Riemannian Particle Swarm Optimization in a set of multi-modal optimization problems over a variety of Riemannian manifolds. (bham.ac.uk)
We compare the performances of four different stochastic optimisation methods using four analytic objective functions and two highly non-linear geophysical optimisation problems: 1D elastic full-waveform inversion (FWI) and residual static computation. (unipi.it)
The four methods we consider, namely, adaptive simulated annealing (ASA), genetic algorithm (GA), neighbourhood algorithm (NA), and particle swarm optimisation (PSO), are frequently employed for solving geophysical inverse problems. (unipi.it)
Similar to the analytic tests, the two seismic optimisation problems we analyse are characterized by very different objective functions. (unipi.it)
While solving difficult stochastic engineering problems, it is often desirable to generate several quantifiably good options that provide contrasting perspectives. (scholink.org)
Simulation-optimization has frequently been used to solve computationally difficult, stochastic problems. (scholink.org)
In both settings, we consider certain optimization problems and we compute derivatives of the probabilistic constraint using the kernel density estimator. (springer.com)
The aim of this paper is to solve probabilistic constrained optimization problems and to derive necessary optimality conditions for them in the context of flow networks. (springer.com)
This leads to optimization problems with probabilistic constraints (see e.g. (springer.com)
we analyze some formal properties of such variants, compare them with other methods proposed in the literature and describe an effective randomized heuristic algorithm that has been developed and successfully applied for the solution of large scale feature selection problems. (rutgers.edu)
The proposed special session aims to bring together new theories and applications of global and constrained optimization techniques to the data mining, Internet /telecommunication, network utility maximization, medical applications, multimedia, computational finance, social network analysis, predictive control (industry 4.0), image, and signal processing problems. (wikicfp.com)
In fact, it also allows for bigger problems to be tackled and the testing of more methods. (inria.fr)
One then needs to solve discrete optimization problems, which, despite the simplicity of the models, become computationally challenging for large proteins. (lu.se)
Quantum computing offers a potentially fast approach to difficult optimization problems. (lu.se)
The aim of this project is to explore quantum computing based methods for solving lattice protein problems. (lu.se)
Many combinatorial optimization problems require a more or less exhaustive search to achieve exact solutions, with a computational effort growing exponentially or worse with system size. (lu.se)
While early versions were confined to problems encodable with a quadratic energy in terms of a set of binary variables, the method has in the last decade been extended to deal with more general problem types, both in terms of variable types and energy functions, and has evolved to a general-purpose heuristic for combinatorial optimization. (lu.se)

You can also look at the paper 'A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints' by Hu, Seiler and Rantzer, and check the references therein. (reddit.com)
The latter are specified in terms of linear operators acting in the space L∞. We examine conditions under which these constraints can be relaxed by using dual variables in L1 - stochastic Lagrange multipliers. (manchester.ac.uk)
The method proposed for Feature Selection is based on integer formulations that represent the retained information using linear constraints associated with the observed data. (rutgers.edu)
INTRODUCTION: A beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans. (bvsalud.org)
For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. (bvsalud.org)

We start by describing the Bayesian approach to the continuous global optimization. (iospress.com)
We solve the auxiliary problem by the Bayesian methods of global optimization. (iospress.com)
Bayesian Methods (STAE02) Autumn - 7,5 credits. (lu.se)

We propose a method combining stochastic dynamic programming and Tabu Search approaches to solve the long-term energy-planning problem without the need to assume a prior form for the long-term persistence of future energy inflows. (birs.ca)

We propose a proximal stochastic gradient algorithm based on variance reduction, called ProxSVRG+. (deepai.org)
In this paper we propose a proximal subgradient method (Prox-SubGrad) fo. (deepai.org)

Global optimization Machine learning Scenario optimization Gaussian process State Space Model Model predictive control Nonlinear programming Entropic value at risk Spall, J. C. (2003). (wikipedia.org)
With the emergence of large-scale networks and complex systems, significant research activity has occurred in the area of global optimization in recent years. (wikicfp.com)

In this review, we present an overview over the current state of the art regarding the prediction and clarification of structures of biomolecules on surfaces using theoretical and computational methods. (degruyter.com)
In this project you will develop computational methods for assembling fragmented (noisy) optical DNA maps into full chromosomal maps for bacteria. (lu.se)
You will also get insight into the interplay between computational methods and the underlying physical phenomena and models that are studied. (lu.se)

GARCH models with discrete time or models based on stochastic differential equations in continuous time. (lu.se)

We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic function using finite differences within a common random number framework. (optimization-online.org)
However, these forms can be challenging to include in the framework required by state-of-the-art methods. (birs.ca)
Equipped with the geometrical framework, we return to optimization methods over Riemannian manifolds. (bham.ac.uk)

The widely used stochastic gradient methods for minimizing nonconvex com. (deepai.org)

To facilitate application and improve mine safety, NIOSH developed the Support Technology Optimization Program (STOP). (cdc.gov)

We show that these estimators, when coupled with the standard gradient descent method, lead to a multi-time-scale stochastic approximation type of algorithm that converges to an optimal quantile value with probability one. (ssrn.com)
Theoretically, we prove that our method can converge to the optimal solution at the rate of O (1/ t ), where t is the number of iterations for stochastic data sampling. (aaai.org)
Even though the optimal parameter values depend on the features of each image and the microscopy system, these values are arbitrarily set by the analyst, and further optimisation tends to be neglected. (nature.com)
In this study, the establishment of an LTA-induced MH-S inflammation model was determined, the CCK-8 method was used to determine the safe concentration range for a drug for COR and CME, the optimal concentration of COR and CME to exert anti-inflammatory effects was further selected, and the expression of inflammatory factors of TNF-α, IL-1ß, IL-18, and IL-6 was detected using ELISA. (bvsalud.org)

6] Gribanova E.B, (2017), Stochastic search algorithm. (sciencepubco.com)

Awarded every three years by the Society for Industrial and Applied Mathematics (SIAM) and the Mathematical Optimization Society, the Lagrange Prize in Continuous Optimization recognises research work in the field of mathematical optimisation. (inria.fr)
It was a paper entitled "Minimizing Finite Sums with the Stochastic Average Gradient" that earned the three researchers this prize, which is a benchmark in the world of applied mathematics. (inria.fr)

An introduction to the properties, solution methods, and applications of Markov decision processes and stochastic games. (lancaster.ac.uk)

optimization model, utilizing convexity analysis and measure theory. (scirp.org)

The obtained data allowed to draw conclusions about the advantages and disadvantages of each modification of the stochastic search algorithm. (sciencepubco.com)

Furthermore, we describe a Markov-driven approach of implementing the SUCAG method in a distributed asynchronous multi-agent setting, via gossiping along a random walk on an undirected communication graph. (nsf.gov)

It focuses on an optimisation algorithm called SAG (for Stochastic Average Gradient). (inria.fr)

A new method for varying adaptive bandwidth selection. (crossref.org)

A community for Mathematical Optimization and any topic directly related to it. (reddit.com)

Methods of this class include: stochastic approximation (SA), by Robbins and Monro (1951) stochastic gradient descent finite-difference SA by Kiefer and Wolfowitz (1952) simultaneous perturbation SA by Spall (1992) scenario optimization On the other hand, even when the data set consists of precise measurements, some methods introduce randomness into the search-process to accelerate progress. (wikipedia.org)
This project aims to develop methods for automating scenario generation, both by reconstructing conditions from real life driving datasets, and by creating completely new scenarios using generative modelling. (lancaster.ac.uk)

In this paper, we propose an unbiased objective function for S 2 OR AUC optimization based on ordinal binary decomposition approach. (aaai.org)

Extensive experimental results on various benchmark and real-world datasets also demonstrate that our method is efficient and effective while retaining similar generalization performance. (aaai.org)
The experimental method utilises fluorescence microscopy and nano-scale devices. (lu.se)

We develop a principled approach to end-to-end learning in stochastic optimization. (optimization-online.org)
Further, the injected randomness may enable the method to escape a local optimum and eventually to approach a global optimum. (wikipedia.org)
This paper applies an MGA method that can create sets of maximally different alternatives for any simulation-optimization approach that employs a population-based algorithm. (scholink.org)

The efficacy of this stochastic MGA method is demonstrated on a waste management facility expansion case. (scholink.org)

Why not use time-series models in stochastic gradient descent? (reddit.com)
P] Stochastic Differentiable Programming: Unbiased Automatic Differentiation for Discrete Stochastic Programs (such as particle filters, agent-based models, and more! (reddit.com)
Jay Rosenberger, Ph.D. Optimization of Statistical Models, Design and Analysis of Computer Experiments. (uta.edu)

A novel anisotropic local polynomial estimator based on directional multiscale optimizations. (crossref.org)

Anatomy1

Organisms1

Chemicals and Drugs12

Analytical, Diagnostic and Therapeutic Techniques and Equipment54

Phenomena and Processes27

Disciplines and Occupations10

Technology, Industry, Agriculture12

Information Science24

Health Care11

Algorithms16

20201

Deterministic2

20191

Nonconvex Optimization2

Zeroth-order stochastic1

Heuristic methods1

Estimation8

Numerical8

20221

Convergence9

Problems35

Constraints5

Bayesian3

Solve1

Proximal2

Global optimization2

Computational methods3

Differential equations1

Framework3

Gradient methods1

Support Technology Optimization Program1

Optimal4

20171

Mathematics2

Processes1

Theory1

Search algorithm1

Markov1

Algorithm called1

Adaptive1

Mathematical Optimization1

Scenario2

Objective1

Experimental2

Approach3

Efficacy1

Models3

Directional1