DOCSLIB.ORG

FICO Xpress Optimization Suite Webinar

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
FICO Xpress Optimization Suite Webinar FICO Xpress Optimization Suite Webinar Oliver Bastert Senior Manager Xpress Product Management September 22 2011 Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation's express consent. 1 © 2011 Fair Isaac Corporation. Agenda » Introduction to FICO » Introduction to FICO Xpress Optimization Suite » Performance » Distributed Modelling and Solving » Case Q&A 2 © 2011 Fair Isaac Corporation. Confidential.© 2011 Fair Isaac Corporation. Confidential. Introduction to FICO 3 © 2011 Fair Isaac Corporation. Confidential. FICO Snapshot The leader in predictive analytics for decision management Founded: 1956 Profile NYSE: FICO Revenues: $605 million (fiscal 2010) Scores and related analytic models Products Analytic applications for risk management, fraud, marketing and Services Tools for decision management Clients and 5,000+ clients in 80 countries Markets Industry focus: Banking, insurance, retail, health care #1 in services operations analytics (IDC) Recent #7 in worldwide business analytics software (IDC) Rankings #26 in the FinTech 100 (American Banker) 20+ offices worldwide, HQ in Minneapolis, USA Offices 2,200 employees Regional Hubs: San Rafael (CA), New York, London, Birmingham (UK), Munich, Madrid, Sao Paulo, Bangalore, Beijing, Singapore 4 © 2011 Fair Isaac Corporation. Confidential. FICO delivers superior predictive analytic solutions that drive smarter decisions. Thousands of businesses worldwide, including 9 of the top Fortune 10, rely on FICO to make every decision count. 5 © 2011 Fair Isaac Corporation. Confidential. Transforming Decision Management Sharpen customer-centric Increase customer decisions profitability Predict customer needs and behavior Reduce losses from fraud and risk Pinpoint best offer and action Connect all decisions about a customer PREDICT PROFIT ADAPT IMPROVE Change faster and Continually improve respond flexibly strategy performance Change business rules instantly Model decisions for greater control Create a test-and-learn culture Optimize strategies to grow faster 6 © 2011 Fair Isaac Corporation. Confidential. IMPROVE Strategy Performance Model decisions for greater control » Identify the decision drivers and the effects of every action » Use the decision model as a planning Uses FICO optimization software to tool to test changes in the business create analytically driven decisions on environment fleet distribution and utilization Deployed across every key market in Optimize strategies to grow faster continental Europe » Create analytically derived strategies to • Benefit estimated at $19 million meet specified business objectives » Design strategies with millions of “FICO™ Xpress tells us, for example: variables – instantly On Friday morning, bring only four cars from Heathrow to Mayfair, and bring another four from Stansted Airport. The utilization of our fleet has gone up by one or two percentage points.” 7 © 2011 Fair Isaac Corporation. Confidential. FICO: Game-Changing Analytics FICO holds 100+ patents in analytic First credit line optimization and decision management technology, solutions with 150 more patents pending First predictive systems for insurance fraud First analytic First cross- systems for bureau credit retailers to scores optimize offers First small First adaptive business scoring analytics for First insurance systems fraud underwriting scoring systems First neural First credit network-based capacity scores First First automated First adaptive fraud solutions commercially origination control systems First score for available credit systems with for managing First cardholder prescription scoring systems analytics card accounts profiling for fraud adherence 1960s 1970s 1980s 1990s 2000s 8 © 2011 Fair Isaac Corporation. Confidential. Building an Analytic Advantage Summarize Past and Current Behavior Predict Future Behavior and Adapt Tools Solutions Scores Tools Solutions Decision Optimization Predictive Analytics Descriptive Decision Value Decision Business Analytics Intelligence Analytic Capability Understand Make different Target each Automatically the trends in offers to groups decision to a take the ideal the business of customers customer’s future action on each behavior individual 9 © 2011 Fair Isaac Corporation. Confidential. FICO Product Portfolio For Lifecycle Specific Decision Processes Customer Collections and Fraud Marketing Origination Management Recovery Management FICO® Precision FICO® FICO® TRIAD® FICO® Debt FICO™ Falcon® Marketing Origination Customer Manager Manager™ Fraud Manager Applications Manager Manager FICO™ Recovery FICO™ Insurance FICO® Retail Management Fraud Manager Action Manager System™ For Any Decision Process B2B: FICO® Score FICO® Credit Capacity Index™ Scores FICO® Insurance Risk Scores B2C: myFICO® Business Rules Management: FICO™ Blaze Advisor® Tools Predictive Analytics: FICO™ Model Builder Optimization: FICO™ Xpress Optimization Suite FICO™ Decision Optimizer Custom Analytics Professional Operational Best Practices Services Strategy Design and Optimization 10 © 2011 Fair Isaac Corporation. Confidential. Introduction to FICO Xpress Optimization Suite 11 © 2011 Fair Isaac Corporation. Confidential. Xpress Optimization Suite GUI XAD Graphical user interface development using Mosel Programming Solver API Mosel API BCL* Deployment Interfaces .NET/Java/C/C++/VB Development IVE IVE-XAD Development Environment GUI development Modelling Mosel MOdelling and Solving Environment Language MIP MIQP MIQCQP MISLP MINLP Solvers CP LP QP QCQP SLP NLP * Builder Component Library for modelling in a programming language 12 © 2011 Fair Isaac Corporation. Confidential. Xpress-IVE: Mosel & Optimizer » Editor » Debugger » Profiler » Progress graphs » Visualization » Wizards » Mosel extensions » Deployment 13 © 2011 Fair Isaac Corporation. Confidential. Production Planning 14 © 2011 Fair Isaac Corporation. Confidential. Product Portfolio & Pricing Optimization FICO Optimization Dashboard: Debt Consolidation Module Confidential – do not copy 15 © 2011 Fair Isaac Corporation. Confidential. Portfolio Rebalancing Solution 16 © 2011 Fair Isaac Corporation. Confidential. Facility Location with Google Maps integration 17 © 2011 Fair Isaac Corporation. Confidential. Key Features and Benefits of Xpress-Mosel Features Benefits » Advanced programming languages: » Entire Mathematical Model can be stored » Algebraic modeling language in one place for rapid development and » Procedural programming language easy maintenance. » Utilize different solvers in the » From Mosel you can solve LPs, MIPs, MIQPs, same model Non-Linear problems, Stochastic problems, and Constraint problems » Decompose & parallelize a model to » Faster solve times take advantage of multiple CPUs/cores » Make full use of your computing infrastructure through distributed computing » Build a GUI exclusively within » Decreases development time, gets optimization Mosel code in front of business user quicker » Portable across operating systems » Mosel Model compiled in one OS can be deployed on all other supported Operating Systems, decreasing development time » Open, modular architecture, » User flexibility to solve the most complicated User extensible optimization problems » not limited to/by predefined language features » Compiled » Protects intellectual property » Offers a variety of APIs and data » Easy deployment and works in heterogeneous 18 © 2011connectors Fair Isaac Corporation. Confidential. environments Xpress History and Product Focus » 26 years of experience in modelling and optimization » 24 years of experience in mixed integer optimization » 12 years of experience in nonlinear optimization » 10 years Xpress-Mosel, modelling and solving environment » Integration of modelling and solving » Focus on (potentially) exact solution methods » Xpress-Solvers often can prove optimality of the solution » They always give you information on the quality of the solution 19 © 2011 Fair Isaac Corporation. Confidential. Xpress Innovations » Solving » 1983: LP solver running on PCs » 1992: parallel MIP (1997 on distributed PC/Linux networks) » 1995/1996 : commercial branch and cut algorithm » 1998: bound switching in dual simplex » 2003: lift-and-project cuts » 2009: parallel MIP heuristics » 2010: LP/MIP solver crosses 64-bit coefficient indexing threshold » Modelling » 1983: general purpose algebraic modelling language (mp-model) » 2001: algebraic modelling language combining modelling, solving, and programming (Mosel) » 2005: profiler and debugger for a modelling language » 2005: user-controlled parallelism at the model level » 2010: algebraic modelling language supporting distributed computing 20 © 2011 Fair Isaac Corporation. Confidential. Xpress differentiators » Unique capabilities for large scale optimization including ability to solve ultra-large problems (true64bit capabilities) and support for distributed modeling and optimization » Complete set of state-of-the-art optimization engines that are robust, reliable and faster than competing solutions » An easy-to-learn, powerful modeling and programming language, Xpress-Mosel » The premier visual development environment, IVE, for developing mathematical models » An intuitive drag-and-drop editor for creating GUIs that seamlessly integrate with the model for rapid prototyping and deployment » A partner committed to solving all of your most difficult optimization problems 21 © 2011 Fair Isaac Corporation. Confidential. Xpress Optimization Suite Users 22 © 2011 Fair Isaac Corporation.
Load more

Recommended publications
  • Multi-Objective Optimization of Unidirectional Non-Isolated Dc/Dcconverters
    MULTI-OBJECTIVE OPTIMIZATION OF UNIDIRECTIONAL NON-ISOLATED DC/DC CONVERTERS by Andrija Stupar A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto © Copyright 2017 by Andrija Stupar Abstract Multi-Objective Optimization of Unidirectional Non-Isolated DC/DC Converters Andrija Stupar Doctor of Philosophy Graduate Department of Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto 2017 Engineers have to fulfill multiple requirements and strive towards often competing goals while designing power electronic systems. This can be an analytically complex and computationally intensive task since the relationship between the parameters of a system’s design space is not always obvious. Furthermore, a number of possible solutions to a particular problem may exist. To find an optimal system, many different possible designs must be evaluated. Literature on power electronics optimization focuses on the modeling and design of partic- ular converters, with little thought given to the mathematical formulation of the optimization problem. Therefore, converter optimization has generally been a slow process, with exhaus- tive search, the execution time of which is exponential in the number of design variables, the prevalent approach. In this thesis, geometric programming (GP), a type of convex optimization, the execution time of which is polynomial in the number of design variables, is proposed and demonstrated as an efficient and comprehensive framework for the multi-objective optimization of non-isolated unidirectional DC/DC converters. A GP model of multilevel flying capacitor step-down convert- ers is developed and experimentally verified on a 15-to-3.3 V, 9.9 W discrete prototype, with sets of loss-volume Pareto optimal designs generated in under one minute.
    [Show full text]
  • Xpress Nonlinear Manual This Material Is the Confidential, Proprietary, and Unpublished Property of Fair Isaac Corporation
    R FICO Xpress Optimization Last update 01 May, 2018 version 33.01 FICO R Xpress Nonlinear Manual This material is the confidential, proprietary, and unpublished property of Fair Isaac Corporation. Receipt or possession of this material does not convey rights to divulge, reproduce, use, or allow others to use it without the specific written authorization of Fair Isaac Corporation and use must conform strictly to the license agreement. The information in this document is subject to change without notice. If you find any problems in this documentation, please report them to us in writing. Neither Fair Isaac Corporation nor its affiliates warrant that this documentation is error-free, nor are there any other warranties with respect to the documentation except as may be provided in the license agreement. ©1983–2018 Fair Isaac Corporation. All rights reserved. Permission to use this software and its documentation is governed by the software license agreement between the licensee and Fair Isaac Corporation (or its affiliate). Portions of the program may contain copyright of various authors and may be licensed under certain third-party licenses identified in the software, documentation, or both. In no event shall Fair Isaac Corporation or its affiliates be liable to any person for direct, indirect, special, incidental, or consequential damages, including lost profits, arising out of the use of this software and its documentation, even if Fair Isaac Corporation or its affiliates have been advised of the possibility of such damage. The rights and allocation of risk between the licensee and Fair Isaac Corporation (or its affiliates) are governed by the respective identified licenses in the software, documentation, or both.
    [Show full text]
  • Julia, My New Friend for Computing and Optimization? Pierre Haessig, Lilian Besson
    Julia, my new friend for computing and optimization? Pierre Haessig, Lilian Besson To cite this version: Pierre Haessig, Lilian Besson. Julia, my new friend for computing and optimization?. Master. France. 2018. cel-01830248 HAL Id: cel-01830248 https://hal.archives-ouvertes.fr/cel-01830248 Submitted on 4 Jul 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. « Julia, my new computing friend? » | 14 June 2018, IETR@Vannes | By: L. Besson & P. Haessig 1 « Julia, my New frieNd for computiNg aNd optimizatioN? » Intro to the Julia programming language, for MATLAB users Date: 14th of June 2018 Who: Lilian Besson & Pierre Haessig (SCEE & AUT team @ IETR / CentraleSupélec campus Rennes) « Julia, my new computing friend? » | 14 June 2018, IETR@Vannes | By: L. Besson & P. Haessig 2 AgeNda for today [30 miN] 1. What is Julia? [5 miN] 2. ComparisoN with MATLAB [5 miN] 3. Two examples of problems solved Julia [5 miN] 4. LoNger ex. oN optimizatioN with JuMP [13miN] 5. LiNks for more iNformatioN ? [2 miN] « Julia, my new computing friend? » | 14 June 2018, IETR@Vannes | By: L. Besson & P. Haessig 3 1. What is Julia ? Open-source and free programming language (MIT license) Developed since 2012 (creators: MIT researchers) Growing popularity worldwide, in research, data science, finance etc… Multi-platform: Windows, Mac OS X, GNU/Linux..
    [Show full text]
  • Solving Mixed Integer Linear and Nonlinear Problems Using the SCIP Optimization Suite
    Takustraße 7 Konrad-Zuse-Zentrum D-14195 Berlin-Dahlem fur¨ Informationstechnik Berlin Germany TIMO BERTHOLD GERALD GAMRATH AMBROS M. GLEIXNER STEFAN HEINZ THORSTEN KOCH YUJI SHINANO Solving mixed integer linear and nonlinear problems using the SCIP Optimization Suite Supported by the DFG Research Center MATHEON Mathematics for key technologies in Berlin. ZIB-Report 12-27 (July 2012) Herausgegeben vom Konrad-Zuse-Zentrum f¨urInformationstechnik Berlin Takustraße 7 D-14195 Berlin-Dahlem Telefon: 030-84185-0 Telefax: 030-84185-125 e-mail: [email protected] URL: http://www.zib.de ZIB-Report (Print) ISSN 1438-0064 ZIB-Report (Internet) ISSN 2192-7782 Solving mixed integer linear and nonlinear problems using the SCIP Optimization Suite∗ Timo Berthold Gerald Gamrath Ambros M. Gleixner Stefan Heinz Thorsten Koch Yuji Shinano Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany, fberthold,gamrath,gleixner,heinz,koch,[email protected] July 31, 2012 Abstract This paper introduces the SCIP Optimization Suite and discusses the ca- pabilities of its three components: the modeling language Zimpl, the linear programming solver SoPlex, and the constraint integer programming frame- work SCIP. We explain how these can be used in concert to model and solve challenging mixed integer linear and nonlinear optimization problems. SCIP is currently one of the fastest non-commercial MIP and MINLP solvers. We demonstrate the usage of Zimpl, SCIP, and SoPlex by selected examples, give an overview of available interfaces, and outline plans for future development. ∗A Japanese translation of this paper will be published in the Proceedings of the 24th RAMP Symposium held at Tohoku University, Miyagi, Japan, 27{28 September 2012, see http://orsj.or.
    [Show full text]
  • Numericaloptimization
    Numerical Optimization Alberto Bemporad http://cse.lab.imtlucca.it/~bemporad/teaching/numopt Academic year 2020-2021 Course objectives Solve complex decision problems by using numerical optimization Application domains: • Finance, management science, economics (portfolio optimization, business analytics, investment plans, resource allocation, logistics, ...) • Engineering (engineering design, process optimization, embedded control, ...) • Artificial intelligence (machine learning, data science, autonomous driving, ...) • Myriads of other applications (transportation, smart grids, water networks, sports scheduling, health-care, oil & gas, space, ...) ©2021 A. Bemporad - Numerical Optimization 2/102 Course objectives What this course is about: • How to formulate a decision problem as a numerical optimization problem? (modeling) • Which numerical algorithm is most appropriate to solve the problem? (algorithms) • What’s the theory behind the algorithm? (theory) ©2021 A. Bemporad - Numerical Optimization 3/102 Course contents • Optimization modeling – Linear models – Convex models • Optimization theory – Optimality conditions, sensitivity analysis – Duality • Optimization algorithms – Basics of numerical linear algebra – Convex programming – Nonlinear programming ©2021 A. Bemporad - Numerical Optimization 4/102 References i ©2021 A. Bemporad - Numerical Optimization 5/102 Other references • Stephen Boyd’s “Convex Optimization” courses at Stanford: http://ee364a.stanford.edu http://ee364b.stanford.edu • Lieven Vandenberghe’s courses at UCLA: http://www.seas.ucla.edu/~vandenbe/ • For more tutorials/books see http://plato.asu.edu/sub/tutorials.html ©2021 A. Bemporad - Numerical Optimization 6/102 Optimization modeling What is optimization? • Optimization = assign values to a set of decision variables so to optimize a certain objective function • Example: Which is the best velocity to minimize fuel consumption ? fuel [ℓ/km] velocity [km/h] 0 30 60 90 120 160 ©2021 A.
    [Show full text]
  • TOMLAB –Unique Features for Optimization in MATLAB
    TOMLAB –Unique Features for Optimization in MATLAB Bad Honnef, Germany October 15, 2004 Kenneth Holmström Tomlab Optimization AB Västerås, Sweden [email protected] ´ Professor in Optimization Department of Mathematics and Physics Mälardalen University, Sweden http://tomlab.biz Outline of the talk • The TOMLAB Optimization Environment – Background and history – Technology available • Optimization in TOMLAB • Tests on customer supplied large-scale optimization examples • Customer cases, embedded solutions, consultant work • Business perspective • Box-bounded global non-convex optimization • Summary http://tomlab.biz Background • MATLAB – a high-level language for mathematical calculations, distributed by MathWorks Inc. • Matlab can be extended by Toolboxes that adds to the features in the software, e.g.: finance, statistics, control, and optimization • Why develop the TOMLAB Optimization Environment? – A uniform approach to optimization didn’t exist in MATLAB – Good optimization solvers were missing – Large-scale optimization was non-existent in MATLAB – Other toolboxes needed robust and fast optimization – Technical advantages from the MATLAB languages • Fast algorithm development and modeling of applied optimization problems • Many built in functions (ODE, linear algebra, …) • GUIdevelopmentfast • Interfaceable with C, Fortran, Java code http://tomlab.biz History of Tomlab • President and founder: Professor Kenneth Holmström • The company founded 1986, Development started 1989 • Two toolboxes NLPLIB och OPERA by 1995 • Integrated format for optimization 1996 • TOMLAB introduced, ISMP97 in Lausanne 1997 • TOMLAB v1.0 distributed for free until summer 1999 • TOMLAB v2.0 first commercial version fall 1999 • TOMLAB starting sales from web site March 2000 • TOMLAB v3.0 expanded with external solvers /SOL spring 2001 • Dash Optimization Ltd’s XpressMP added in TOMLAB fall 2001 • Tomlab Optimization Inc.
    [Show full text]
  • COSMO: a Conic Operator Splitting Method for Convex Conic Problems
    COSMO: A conic operator splitting method for convex conic problems Michael Garstka∗ Mark Cannon∗ Paul Goulart ∗ September 10, 2020 Abstract This paper describes the Conic Operator Splitting Method (COSMO) solver, an operator split- ting algorithm for convex optimisation problems with quadratic objective function and conic constraints. At each step the algorithm alternates between solving a quasi-definite linear sys- tem with a constant coefficient matrix and a projection onto convex sets. The low per-iteration computational cost makes the method particularly efficient for large problems, e.g. semidefi- nite programs that arise in portfolio optimisation, graph theory, and robust control. Moreover, the solver uses chordal decomposition techniques and a new clique merging algorithm to ef- fectively exploit sparsity in large, structured semidefinite programs. A number of benchmarks against other state-of-the-art solvers for a variety of problems show the effectiveness of our approach. Our Julia implementation is open-source, designed to be extended and customised by the user, and is integrated into the Julia optimisation ecosystem. 1 Introduction We consider convex optimisation problems in the form minimize f(x) subject to gi(x) 0; i = 1; : : : ; l (1) h (x)≤ = 0; i = 1; : : : ; k; arXiv:1901.10887v2 [math.OC] 9 Sep 2020 i where we assume that both the objective function f : Rn R and the inequality constraint n ! functions gi : R R are convex, and that the equality constraints hi(x) := ai>x bi are ! − affine. We will denote an optimal solution to this problem (if it exists) as x∗. Convex optimisa- tion problems feature heavily in a wide range of research areas and industries, including problems ∗The authors are with the Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK.
    [Show full text]
  • Introduction to Mosek
    Introduction to Mosek Modern Optimization in Energy, 28 June 2018 Micha l Adamaszek www.mosek.com MOSEK package overview • Started in 1999 by Erling Andersen • Convex conic optimization package + MIP • LP, QP, SOCP, SDP, other nonlinear cones • Low-level optimization API • C, Python, Java, .NET, Matlab, R, Julia • Object-oriented API Fusion • C++, Python, Java, .NET • 3rd party • GAMS, AMPL, CVXOPT, CVXPY, YALMIP, PICOS, GPkit • Conda package, .NET Core package • Upcoming v9 1 / 10 Example: 2D Total Variation Someone sends you the left signal but you receive noisy f (right): How to denoise/smoothen out/approximate u? P 2 P 2 minimize ij(ui;j − ui+1;j) + ij(ui;j − ui;j+1) P 2 subject to ij(ui;j − fi;j) ≤ σ: 2 / 10 Conic problems A conic problem in canonical form: min cT x s:t: Ax + b 2 K where K is a product of cones: • linear: K = R≥0 • quadratic: q n 2 2 K = fx 2 R : x1 ≥ x2 + ··· + xng • semidefinite: n×n T K = fX 2 R : X = FF g 3 / 10 Conic problems, cont. • exponential cone: 3 K = fx 2 R : x1 ≥ x2 exp(x3=x2); x2 > 0g • power cone: 3 p−1 p K = fx 2 R : x1 x2 ≥ jx3j ; x1; x2 ≥ 0g; p > 1 q 2 2 2 x1 ≥ x2 + x3; 2x1x2 ≥ x3 x1 ≥ x2 exp(x3=x2) 4 / 10 Conic representability Lots of functions and constraints are representable using these cones. T jxj; kxk1; kxk2; kxk1; kAx + bk2 ≤ c x + d !1=p 1 X xy ≥ z2; x ≥ ; x ≥ yp; t ≥ jx jp = kxk y i p i p 1=n t ≤ xy; t ≤ (x1 ··· xn) ; geometric programming (GP) X 1 t ≤ log x; t ≥ ex; t ≤ −x log x; t ≥ log exi ; t ≥ log 1 + x i 1=n det(X) ; t ≤ λmin(X); t ≥ λmax(X) T T convex (1=2)x Qx + c x + q 5 / 10 Challenge Find a • natural, • practical, • important, • convex optimization problem, which cannot be expressed in conic form.
    [Show full text]
  • AJINKYA KADU 503, Hans Freudenthal Building, Budapestlaan 6, 3584 CD Utrecht, the Netherlands
    AJINKYA KADU 503, Hans Freudenthal Building, Budapestlaan 6, 3584 CD Utrecht, The Netherlands Curriculum Vitae Last Updated: Oct 15, 2018 Contact Ph.D. Student +31{684{544{914 Information Mathematical Institute [email protected] Utrecht University https://ajinkyakadu125.github.io Education Mathematical Institute, Utrecht University, The Netherlands 2015 - present Ph.D. Candidate, Numerical Analysis and Scientific Computing • Dissertation Topic: Discrete Seismic Tomography • Advisors: Dr. Tristan van Leeuwen, Prof. Wim Mulder, Prof. Joost Batenburg • Interests: Seismic Imaging, Computerized Tomography, Numerical Optimization, Level-Set Method, Total-variation, Convex Analysis, Signal Processing Indian Institute of Technology Bombay, Mumbai, India 2010 - 2015 Bachelor and Master of Technology, Department of Aerospace Engineering • Advisors: Prof. N. Hemachandra, Prof. R. P. Shimpi • GPA: 8.7/10 (Specialization: Operations Research) Work Mitsubishi Electric Research Labs, Cambridge, MA, USA May - Oct, 2018 Experience • Mentors: Dr. Hassan Mansour, Dr. Petros Boufounos • Worked on inverse scattering problem arising in ground penetrating radar. University of British Columbia, Vancouver, Canada Jan - Apr, 2016 • Mentors: Prof. Felix Herrmann, Prof. Eldad Haber • Worked on development of framework for large-scale inverse problems in geophysics. Rediff.com Pvt. Ltd., Mumbai, India May - July, 2014 • Mentor: A. S. Shaja • Worked on the development of data product `Stock Portfolio Match' based on Shiny & R. Honeywell Technology Solutions, Bangalore, India May - July, 2013 • Mentors: Kartavya Mohan Gupta, Hanumantha Rao Desu • Worked on integration bench for General Aviation(GA) to recreate flight test scenarios. Research: Journal • A convex formulation for Discrete Tomography. Publications Ajinkya Kadu, Tristan van Leeuwen, (submitted to) IEEE Transactions on Computational Imaging (arXiv: 1807.09196) • Salt Reconstruction in Full Waveform Inversion with a Parametric Level-Set Method.
    [Show full text]
  • Robert Fourer, David M. Gay INFORMS Annual Meeting
    AMPL New Solver Support in the AMPL Modeling Language Robert Fourer, David M. Gay AMPL Optimization LLC, www.ampl.com Department of Industrial Engineering & Management Sciences, Northwestern University Sandia National Laboratories INFORMS Annual Meeting Pittsburgh, Pennsylvania, November 5-8, 2006 Robert Fourer, New Solver Support in the AMPL Modeling Language INFORMS Annual Meeting, November 5-8, 2006 1 AMPL What it is . A modeling language for optimization A system to support optimization modeling What I’ll cover . Brief examples, briefer history Recent developments . ¾ 64-bit versions ¾ floating license manager ¾ AMPL Studio graphical interface & Windows COM objects Solver support ¾ new KNITRO 5.1 features ¾ new CPLEX 10.1 features Robert Fourer, New Solver Support in the AMPL Modeling Language INFORMS Annual Meeting, November 5-8, 2006 2 Ex 1: Airline Fleet Assignment set FLEETS; set CITIES; set TIMES circular; set FLEET_LEGS within {f in FLEETS, c1 in CITIES, t1 in TIMES, c2 in CITIES, t2 in TIMES: c1 <> c2 and t1 <> t2}; # (f,c1,t1,c2,t2) represents the availability of fleet f # to cover the leg that leaves c1 at t1 and # whose arrival time plus turnaround time at c2 is t2 param leg_cost {FLEET_LEGS} >= 0; param fleet_size {FLEETS} >= 0; Robert Fourer, New Solver Support in the AMPL Modeling Language INFORMS Annual Meeting, November 5-8, 2006 3 Ex 1: Derived Sets set LEGS := setof {(f,c1,t1,c2,t2) in FLEET_LEGS} (c1,t1,c2,t2); # the set of all legs that can be covered by some fleet set SERV_CITIES {f in FLEETS} := union {(f,c1,c2,t1,t2)
    [Show full text]
  • Julia: a Modern Language for Modern ML
    Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement.
    [Show full text]
  • Open Source Tools for Optimization in Python
    Open Source Tools for Optimization in Python Ted Ralphs Sage Days Workshop IMA, Minneapolis, MN, 21 August 2017 T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Outline 1 Introduction 2 COIN-OR 3 Modeling Software 4 Python-based Modeling Tools PuLP/DipPy CyLP yaposib Pyomo T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Outline 1 Introduction 2 COIN-OR 3 Modeling Software 4 Python-based Modeling Tools PuLP/DipPy CyLP yaposib Pyomo T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Caveats and Motivation Caveats I have no idea about the background of the audience. The talk may be either too basic or too advanced. Why am I here? I’m not a Sage developer or user (yet!). I’m hoping this will be a chance to get more involved in Sage development. Please ask lots of questions so as to guide me in what to dive into! T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Mathematical Optimization Mathematical optimization provides a formal language for describing and analyzing optimization problems. Elements of the model: Decision variables Constraints Objective Function Parameters and Data The general form of a mathematical optimization problem is: min or max f (x) (1) 8 9 < ≤ = s.t. gi(x) = bi (2) : ≥ ; x 2 X (3) where X ⊆ Rn might be a discrete set. T.K. Ralphs (Lehigh University) Open Source Optimization August 21, 2017 Types of Mathematical Optimization Problems The type of a mathematical optimization problem is determined primarily by The form of the objective and the constraints.
    [Show full text]