A5033833921- Advanced Optimization Algorithms Research
- Optimization and Variational Analysis
- Sparse and Compressive Sensing Techniques
- Gene expression and cancer classification
- Numerical methods in inverse problems
- COVID-19 epidemiological studies
- Bioinformatics and Genomic Networks
- Breast Cancer Treatment Studies
- COVID-19 and Mental Health
- Iterative Methods for Nonlinear Equations
- Data-Driven Disease Surveillance
- Image and Signal Denoising Methods
- Risk and Portfolio Optimization
- Metaheuristic Optimization Algorithms Research
- Software Engineering Techniques and Practices
- Software Engineering Research
- Advanced Vision and Imaging
- Topological and Geometric Data Analysis
- Matrix Theory and Algorithms
- Constraint Satisfaction and Optimization
- Optimization and Packing Problems
- HER2/EGFR in Cancer Research
- Blind Source Separation Techniques
- Optimization and Mathematical Programming
- Electric Power System Optimization
Universidade Estadual de Campinas (UNICAMP)
2014-2023
Hospital de Clínicas da Unicamp
2022
Universidade de São Paulo
2001-2016
Brazilian Society of Computational and Applied Mathematics
2004
Abstract Purpose: This study was designed to identify genes that could predict response doxorubicin-based primary chemotherapy in breast cancer patients. Experimental Design: Biopsy samples were obtained before treatment with doxorubicin and cyclophosphamide. RNA extracted amplified gene expression analyzed using cDNA microarrays. Results: Response evaluated 51 patients, based on Evaluation Criteria Solid Tumors guidelines, 42 who presented at least a partial (≥30% reduction tumor...
Every local minimizer of a smooth constrained optimization problem satisfies the sequential approximate Karush--Kuhn--Tucker (AKKT) condition. This optimality condition is used to define stopping criteria many practical nonlinear programming algorithms. It natural ask for conditions on constraints under which AKKT implies KKT. These will be called strict constraint qualifications (SCQs). In this paper we cone-continuity property (CCP) that shown weakest possible SCQ. Its relation other also...
We present two new constraint qualifications (CQs) that are weaker than the recently introduced relaxed constant positive linear dependence (RCPLD) CQ. RCPLD is based on assumption many subsets of gradients active constraints preserve locally. A major open question was to identify exact set whose properties had be preserved locally and would still work as a This done in first CQ, which we call rank subspace component (CRSC) CQ also preserves good RCPLD, such local stability validity an error...
Sequential optimality conditions for constrained optimization are necessarily satisfied by local minimizers, independently of the fulfillment constraint qualifications. These support employment different stopping criteria practical algorithms. On other hand, when an appropriate property on constraints holds at a point that satisfies sequential condition, such also Karush-Kuhn-Tucker conditions. Those properties will be called strict qualifications in this paper. As consequence, each it is...
In recent years, the theoretical convergence of iterative methods for solving nonlinear constrained optimization problems has been addressed using sequential optimality conditions, which are satisfied by minimizers independently constraint qualifications (CQs). Even though there is a considerable literature devoted to conditions standard optimization, same not true mathematical programs with complementarity constraints (MPCCs). this paper, we show that established suitable analysis...
Significance Shortages of COVID-19 vaccines hampered efforts to fight the current pandemic, leading experts argue for delaying second dose provide earlier first-dose protection twice as many people. We designed a model-based strategy identifying optimal second-dose delay using hospitalization rate key metric. While epistemic uncertainties apply our modeling, we found that was dependent on efficacy and vaccine mechanism action. For infection-blocking vaccines, could be delayed <mml:math...
Alzheimer's disease (AD) is the most common cause of dementia in human population, characterized by a spectrum neuropathological abnormalities that results memory impairment and loss other cognitive processes as well presence non-cognitive symptoms. Transcriptomic analyses provide an important approach to elucidating pathogenesis complex diseases like AD, helping figure out both pre-clinical markers identify susceptible patients early pathogenic mechanisms serve therapeutic targets. This...
Sequential optimality conditions have recently played an important role on the analysis of global convergence optimization algorithms towards first-order stationary points, justifying their stopping criteria. In this article, we introduce a sequential condition that takes into account second-order information and allows us to improve assumptions several algorithms, which is our main goal. We also present companion constraint qualification less stringent than previous associated methods, like...
Mathematical programs with complementarity constraints (MPCCs) are difficult optimization problems that do not satisfy the majority of usual constraint qualifications (CQs) for standard nonlinear optimization. Despite this fact, classical methods behave well when applied to MPCCs. Recently, Izmailov, Solodov, and Uskov proved first order augmented Lagrangian methods, under a natural adaption linear independence qualification MPCC setting (MPCC-LICQ), converge strongly stationary...
Multidimensional Projection is a fundamental tool for high-dimensional data analytics and visualization. With very few exceptions, projection techniques are designed to map from space visual so as preserve some dissimilarity (similarity) measure, such the Euclidean distance example. In fact, although adopting distinct mathematical formulations favor different aspects of data, most multidimensional methods strive measures that encapsulate geometric properties distances or proximity relation...
Abstract Agile Methods propose a new way of looking at software development that questions many the beliefs conventional Software Engineering. methods such as Extreme Programming (XP) have been very effective in producing high-quality real-world projects with strict time constraints. Nevertheless, most university courses and industrial training programs are still based on old-style heavyweight methods. This article, our experiences teaching XP academic environments, presents ways students...
We propose a method for solving nonlinear second-order cone programs (SOCPs), based on continuously differentiable exact penalty function. The construction of the function is given by incorporating multipliers estimate in augmented Lagrangian SOCPs. Under nondegeneracy assumption and strong sufficient condition, we show that generalized Newton has global superlinear convergence. also present some preliminary numerical experiments.
We propose a new algorithm for optimal MAE stack filter design. It is based on three main ingredients. First, we show that the dual of integer programming formulation design problem minimum cost network flow problem. Next, present decomposition principle can be used to break this into smaller subproblems. Finally, specialization Simplex column generation solve these Using our method, were able efficiently instances with window size up 25 pixels. To best knowledge, largest dimension which was...
.We present a framework for analyzing convergence and local rates of class descent algorithms, assuming the objective function is weakly convex. The general, in sense that it combines possibility explicit iterations (based on gradient or subgradient at current iterate), implicit (using next iteration, like proximal schemes), as well when associated specially constructed does not correspond either to point (this case steps bundle methods). Under subdifferential-based error bound distance...
In this work we present new weak conditions that ensure the validity of necessary second-order optimality (SOC) for nonlinear optimization. We are able to prove and strong SOCs hold all Lagrange multipliers using Abadie-type assumptions. also at least one multiplier imposing Mangasarian–Fromovitz constraint qualification a constant rank assumption.
This paper presents a convergence proof technique for broad class of proximal algorithms in which the perturbation term is separable and may contain barriers enforcing interval constraints. There are two key ingredients analysis: mild regularity condition on differential behavior barrier as one approaches an boundary lower stepsize limit that takes into account curvature term. We give applications our approach. First, we prove subsequential very minimization convex optimization, where...
Robot Dance is a computational optimization platform developed in response to the COVID-19 outbreak, support decision-making on public policies at regional level. The tool suitable for understanding and suggesting levels of intervention needed contain spread infectious diseases when mobility inhabitants through network concern. Such case SARS-CoV-2 virus that highly contagious and, therefore, makes it crucial incorporate circulation people epidemiological compartmental models. anticipates an...