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Relevant bibliographies by topics / Linear search

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Academic literature on the topic 'Linear search'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Linear search"

1

Hohzaki, Ryusuke. "A SEARCH GAME TAKING ACCOUNT OF LINEAR EFFECTS AND LINEAR CONSTRAINTS OF SEARCHING RESOURCE." Journal of the Operations Research Society of Japan 55, no. 1 (2012): 1–22. http://dx.doi.org/10.15807/jorsj.55.1.

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Mäkinen, Erkki. "On linear search heuristics." Information Processing Letters 29, no. 1 (September 1988): 35–36. http://dx.doi.org/10.1016/0020-0190(88)90129-9.

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Hester, J. H., and D. S. Hirschberg. "Self-organizing linear search." ACM Computing Surveys 17, no. 3 (September 1985): 295–311. http://dx.doi.org/10.1145/5505.5507.

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Arora, Nitin, Garima Bhasin, and Neha Sharma. "Two way Linear Search Algorithm." International Journal of Computer Applications 107, no. 21 (December 18, 2014): 6–8. http://dx.doi.org/10.5120/19137-9622.

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Limoncelli, Thomas A. "10 optimizations on linear search." Communications of the ACM 59, no. 9 (August 24, 2016): 44–48. http://dx.doi.org/10.1145/2980976.

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Limoncelli, Thomas A. "10 Optimizations on Linear Search." Queue 14, no. 4 (August 2016): 20–33. http://dx.doi.org/10.1145/2984629.2984631.

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Korf, Richard E. "Linear-space best-first search." Artificial Intelligence 62, no. 1 (July 1993): 41–78. http://dx.doi.org/10.1016/0004-3702(93)90045-d.

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Foley, R. D., T. P. Hill, and M. C. Spruill. "Linear search with bounded resources." Naval Research Logistics 38, no. 4 (August 1991): 555–65. http://dx.doi.org/10.1002/1520-6750(199108)38:4<555::aid-nav3220380408>3.0.co;2-8.

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Kumari, Anchala. "Linear Search Versus Binary Search:A Statistical Comparison For Binomial Inputs." International Journal of Computer Science, Engineering and Applications 2, no. 2 (April 30, 2012): 29–39. http://dx.doi.org/10.5121/ijcsea.2012.2203.

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Balkhi, Zaid T. "THE GENERALIZED LINEAR SEARCH PROBLEM, EXISTENCE OF OPTIMAL SEARCH PATHS." Journal of the Operations Research Society of Japan 30, no. 4 (1987): 399–421. http://dx.doi.org/10.15807/jorsj.30.399.

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Dissertations / Theses on the topic "Linear search"

1

Rees, Leigh H. "Chirality : in search of organometallic second order non-linear optic materials." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265324.

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Hepburn, Ian David. "Development of linear InSb array instrumentation and the search for brown dwarfs." Thesis, Imperial College London, 1991. http://hdl.handle.net/10044/1/46816.

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Bothwell, Brian P. "An Iterative Linear Programming Approach to Solving Large Cumulative Search-Evasion Games." Thesis, Monterey, California. Naval Postgraduate School, 1990. http://hdl.handle.net/10945/30678.

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Approved for public release, distribution is unlimited
Cumulative search-evasion games (CSEGs) involve two players, a searcher and an evader, who move among some finite set of cells. Neither player is aware of the other player's position during any stage of the game. When the payoff for the game is assumed to be the number of times the searcher and evader occupy the same cell, Eagle and Washburn proposed two solution techniques: one by fictitious play and the other by solving equivalent linear programming formulations. However, both have proved to be time consuming even for moderately sized problems. This thesis considers two alternate linear programming formulations for CSEGs. Since both contain a large number of variables and constraints, the linear programming problems are initially solved with many of the constraints removed. If the solution to this relaxed problem is not a feasible optimal solution, additional constraints are added and the problem is solved again. This process continues until a feasible optimal solution is found. The results from a numerical experimentation with various solution techniques are also presented.

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Madabushi, Ananth R. "Lagrangian Relaxation / Dual Approaches For Solving Large-Scale Linear Programming Problems." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36833.

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This research effort focuses on large-scale linear programming problems that arise in the context of solving various problems such as discrete linear or polynomial, and continuous nonlinear, nonconvex programming problems, using linearization and branch-and-cut algorithms for the discrete case, and using polyhedral outer-approximation methods for the continuous case. These problems arise in various applications in production planning, location-allocation, game theory, economics, and many engineering and systems design problems. During the solution process of discrete or continuous nonconvex problems using polyhedral approaches, one has to contend with repeatedly solving large-scale linear programming(LP) relaxations. Thus, it becomes imperative to employ an efficient method in solving these problems. It has been amply demonstrated that solving LP relaxations using a simplex-based algorithm, or even an interior-point type of procedure, can be inadequately slow ( especially in the presence of complicating constraints, dense coefficient matrices, and ill-conditioning ) in comparison with a Lagrangian Relaxation approach. With this motivation, we present a practical primal-dual subgradient algorithm that incorporates a dual ascent, a primal recovery, and a penalty function approach to recover a near optimal and feasible pair of primal and dual solutions. The proposed primal-dual approach is comprised of three stages. Stage I deals with solving the Lagrangian dual problem by using various subgradient deflection strategies such as the Modified Gradient Technique (MGT), the Average Direction Strategy (ADS), and a new direction strategy called the Modified Average Direction Strategy (M-ADS). In the latter, the deflection parameter is determined based on the process of projecting the unknown optimal direction onto the space spanned by the current subgradient direction and the previous direction. This projected direction approximates the desired optimal direction as closely as possible using the conjugate subgradient concept. The step-length rules implemented in this regard are the Quadratic Fit Line Search Method and a new line search method called the Directional Derivative Line Search Method in which we start with a prescribed step-length and then ascertain whether to increase or decrease the step-length value based on the right-hand and left-hand derivative information available at each iteration. In the second stage of the algorithm (Stage II), a sequence of updated primal solutions is generated using some convex combinations of the Lagrangian subproblem solutions. Alternatively, a starting primal optimal solution can be obtained using the complementary slackness conditions. Depending on the extent of feasibility and optimality attained, Stage III applies a penalty function method to improve the obtained primal solution toward a near feasible and optimal solution. We present computational experience using a set of randomly generated, structured, linear programming problems of the type that might typically arise in the context of discrete optimization.
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Kim, Seongsu. "A BAYESIAN EVIDENCE DEFINING SEARCH." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429789001.

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Law, Glenn W. (Glenn Woodrow). "Measurements of ocular counterrolling during linear accelerations using an electromagnetic scleral search coil system." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/43236.

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Stokes, Klara. "Combinatorial structures for anonymous database search." Doctoral thesis, Universitat Rovira i Virgili, 2011. http://hdl.handle.net/10803/52799.

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This thesis treats a protocol for anonymous database search (or if one prefer, a protocol for user-private information retrieval), that is based on the use of combinatorial configurations. The protocol is called P2P UPIR. It is proved that the (v,k,1)-balanced incomplete block designs (BIBD) and in particular the finite projective planes are optimal configurations for this protocol. The notion of n-anonymity is applied to the configurations for P2P UPIR protocol and the transversal designs are proved to be n-anonymous configurations for P2P UPIR, with respect to the neighborhood points of the points of the configuration. It is proved that to the configurable tuples one can associate a numerical semigroup. This theorem implies results on existence of combinatorial configurations. The proofs are constructive and can be used as algorithms for finding combinatorial configurations. It is also proved that to the triangle-free configurable tuples one can associate a numerical semigroup. This implies results on existence of triangle-free combinatorial configurations.

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Vasireddy, Jhansi Lakshmi. "Applications of Linear Algebra to Information Retrieval." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/71.

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Some of the theory of nonnegative matrices is first presented. The Perron-Frobenius theorem is highlighted. Some of the important linear algebraic methods of information retrieval are surveyed. Latent Semantic Indexing (LSI), which uses the singular value de-composition is discussed. The Hyper-Text Induced Topic Search (HITS) algorithm is next considered; here the power method for finding dominant eigenvectors is employed. Through the use of a theorem by Sinkohrn and Knopp, a modified HITS method is developed. Lastly, the PageRank algorithm is discussed. Numerical examples and MATLAB programs are also provided.

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Lopez, Soto Claudia Orquidea. "Formulation space search for two-dimensional packing problems." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7455.

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The two-dimension packing problem is concerned with the arrangement of items without overlaps inside a container. In particular we have considered the case when the items are circular objects, some of the general examples that can be found in the industry are related with packing, storing and transportation of circular objects. Although there are several approaches we want to investigate the use of formulation space search. Formulation space search is a fairly recent method that provides an easy way to escape from local optima for non-linear problems allowing to achieve better results. Despite the fact that it has been implemented to solve the packing problem with identical circles, we present an improved implementation of the formulation space search that gives better results for the case of identical and non-identical circles, also considering that they are packed inside different shaped containers, for which we provide the needed modifications for an appropriate implementation. The containers considered are: the unit circle, the unit square, two rectangles with different dimension (length 5, width 1 and length 10 width 1), a right-isosceles triangle, a semicircle and a right-circular quadrant. Results from the tests conducted shown several improvements over the best previously known for the case of identical circles inside three different containers: a right-isosceles triangle, a semicircle and a circular quadrant. In order to extend the scope of the formulation space search approach we used it to solve mixed-integer non-linear problems, in particular those with zero-one variables. Our findings suggest that our implementation provides a competitive way to solve these kind of problems.

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Lazic, Jasmina. "New variants of variable neighbourhood search for 0-1 mixed integer programming and clustering." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4602.

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Many real-world optimisation problems are discrete in nature. Although recent rapid developments in computer technologies are steadily increasing the speed of computations, the size of an instance of a hard discrete optimisation problem solvable in prescribed time does not increase linearly with the computer speed. This calls for the development of new solution methodologies for solving larger instances in shorter time. Furthermore, large instances of discrete optimisation problems are normally impossible to solve to optimality within a reasonable computational time/space and can only be tackled with a heuristic approach. In this thesis the development of so called matheuristics, the heuristics which are based on the mathematical formulation of the problem, is studied and employed within the variable neighbourhood search framework. Some new variants of the variable neighbourhood searchmetaheuristic itself are suggested, which naturally emerge from exploiting the information from the mathematical programming formulation of the problem. However, those variants may also be applied to problems described by the combinatorial formulation. A unifying perspective on modern advances in local search-based metaheuristics, a so called hyper-reactive approach, is also proposed. Two NP-hard discrete optimisation problems are considered: 0-1 mixed integer programming and clustering with application to colour image quantisation. Several new heuristics for 0-1 mixed integer programming problem are developed, based on the principle of variable neighbourhood search. One set of proposed heuristics consists of improvement heuristics, which attempt to find high-quality near-optimal solutions starting from a given feasible solution. Another set consists of constructive heuristics, which attempt to find initial feasible solutions for 0-1 mixed integer programs. Finally, some variable neighbourhood search based clustering techniques are applied for solving the colour image quantisation problem. All new methods presented are compared to other algorithms recommended in literature and a comprehensive performance analysis is provided. Computational results show that the methods proposed either outperform the existing state-of-the-art methods for the problems observed, or provide comparable results. The theory and algorithms presented in this thesis indicate that hybridisation of the CPLEX MIP solver and the VNS metaheuristic can be very effective for solving large instances of the 0-1 mixed integer programming problem. More generally, the results presented in this thesis suggest that hybridisation of exact (commercial) integer programming solvers and some metaheuristic methods is of high interest and such combinations deserve further practical and theoretical investigation. Results also show that VNS can be successfully applied to solving a colour image quantisation problem.

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Books on the topic "Linear search"

1

Laguna, Manuel. Scatter search: Methodology and implementation in C. Boston, MA: Kluwer Academic Publishers, 2003.

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Rafael, Martí, ed. Scatter search: Methodology and implementations in C. Boston: Kluwer Academic Publishers, 2003.

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H, Hoos Holger, Birattari Mauro, and SpringerLink (Online service), eds. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: Second International Workshop, SLS 2009, Brussels, Belgium, September 3-4, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Dalle navi bianche alla Linea gotica, 1941-1944. Milano: Mursia, 2011.

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Nakov, Svetlin. Fundamentals of Computer Programming with C#: The Bulgarian C# Book. Sofia, Bulgaria: Svetlin Nakov, 2013.

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Serafimova, Vera, Ivan Pankeev, and L. G. Tyurina. History of Russian literature of the XX-XXI centuries. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1866868.

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The textbook consists of review and monographic chapters, presents a modern view of the literary process of the XX — early XXI century, examines the work of poets, novelists, playwrights who caused an extraordinary rise in spirituality and culture of the period under consideration. The analysis of the top works of Nobel Prize laureates is given: I. Bunin, B. Pasternak, M. Sholokhov, A. Solzhenitsyn, I. Brodsky, writers- front—line poets and prose writers. Attention is paid to the work of writers of Russian emigration. The section "Modern prose" includes materials on philosophical and aesthetic searches in the works of such writers as V. Rasputin, L. Borodin, Yu. Polyakov, B. Ekimov, A. Bitov, V. Makanin, A. Kabakov, V. Tokareva, etc. It offers questions and tasks for independent work, topics of abstracts, term papers and theses, a list of bibliographic sources. Meets the requirements of the federal state educational standards of higher education of the latest generation. Designed for students of higher educational institutions.

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Serafimova, Vera. History of Russian literature of XX-XXI centuries. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1138897.

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The textbook consists of review and monographic chapters, presents a modern view of the literary process of the XX-beginning of the XXI century, examines the work of poets, prose writers, playwrights who caused an extraordinary rise in spirituality and culture of the period under consideration. The analysis of the top works of Nobel prize winners: I. Bunin, B. Pasternak, M. Sholokhov, A. Solzhenitsyn, V. Shalamov, I. Brodsky, writers-front — line poets and prose writers is given. Attention is paid to the work of writers of Russian emigration. The section "Modern prose" includes materials about philosophical and aesthetic searches in the works of such writers As V. Rasputin, L. Borodin, Yu. Polyakov, B. Ekimov, A. Bitov, V. Makanin, A. Kabakov, V. Tokareva, etc. It offers questions and tasks for independent work, topics of essays, term papers and theses, a list of bibliographic sources. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for students of higher educational institutions.

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Aron, Richard M., Luis Bernal-Gonzalez, Daniel M. Pellegrino, and Juan B. Seoane Sepulveda. Lineability: The Search for Linearity in Mathematics. Taylor & Francis Group, 2015.

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Aron, Richard M., Luis Bernal-Gonzalez, Daniel M. Pellegrino, and Juan B. Seoane Sepulveda. Lineability: The Search for Linearity in Mathematics. Taylor & Francis Group, 2015.

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Aron, Richard M., Luis Bernal-Gonzalez, Daniel M. Pellegrino, and Juan B. Seoane Sepulveda. Lineability: The Search for Linearity in Mathematics. Taylor & Francis Group, 2015.

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Book chapters on the topic "Linear search"

1

Weik, Martin H. "linear search." In Computer Science and Communications Dictionary, 898. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_10241.

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Feijen, W. H. J., and A. J. M. van Gasteren. "The Parallel Linear Search." In Monographs in Computer Science, 201–6. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3126-2_18.

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Subero, Armstrong. "Linear and Binary Search." In Codeless Data Structures and Algorithms, 63–69. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5725-8_6.

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Thordsen, Erik, and Erich Schubert. "On Projections to Linear Subspaces." In Similarity Search and Applications, 75–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17849-8_7.

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Spirtes, Peter, Clark Glymour, and Richard Scheines. "Elaborating Linear Theories with Unmeasured Variables." In Causation, Prediction, and Search, 323–53. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-2748-9_11.

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Collet, Jean-François. "Linear algebra and search engines." In Discrete Stochastic Processes and Applications, 33–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74018-8_2.

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Zhang, Shiwei, Guomin Yang, and Yi Mu. "Linear Encryption with Keyword Search." In Information Security and Privacy, 187–203. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40367-0_12.

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Boytsov, Leonid. "Super-Linear Indices for Approximate Dictionary Searching." In Similarity Search and Applications, 162–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32153-5_12.

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Hünemörder, Maximilian Archimedes Xaver, Daniyal Kazempour, Peer Kröger, and Thomas Seidl. "SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge." In Similarity Search and Applications, 221–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32047-8_20.

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Munro, J. Ian. "On the Competitiveness of Linear Search." In Algorithms - ESA 2000, 338–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45253-2_31.

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Conference papers on the topic "Linear search"

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Hossain, Md Emran, Syed Bayazid Hossain, Md Sha Alam Tutul, and Shamsun Nahar. "Optimized Search Functionality with Linear Search Algorithm." In International Conference on Emerging Trends in Engineering and Advanced Science. AIJR Publisher, 2022. http://dx.doi.org/10.21467/proceedings.123.6.

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Linear search is the essential search algorithm used in fact structures. If is likewise called sequential search. Linear search used to discover a specific detail in an array. It is today no longer obligatory to display an array in any order (Ascending or Descending) as in the case of binary search. Linear search initiates advanced with the support of using sequentially scanning the influences within the array and if the detail has been found, if will display the unique detail and the index value of that detail in that array. In this paper we contemporary a unique searching algorithm of modified Linear Search, which is an improved form of linear search algorithm and its created by likening the influences from each termination. We have additionally in comparison the new Linear search set of rules with Linear Search Algorithm. We used a system for implementation and Analysis of time taken with the aid of using each the algorithms. Result shows that Two Way Searching Algorithm is working glowing for all comments standards and it takes lesser time if the detail to be search is after the middle of the array, otherwise it takes same time as if of linear search. Remaining primarily based totally on the linear search and binary search algorithms; one algorithm designed to act on connected linear list.

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Francis, Alwin, and Rajesh Ramachandran. "Modulo Ten Search- An Alternative to Linear Search." In 2011 International Conference on Process Automation, Control and Computing (PACC). IEEE, 2011. http://dx.doi.org/10.1109/pacc.2011.5979034.

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Spampinato, Daniele G., Upasana Sridhar, and Tze Meng Low. "Linear algebraic depth-first search." In the 6th ACM SIGPLAN International Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3315454.3329962.

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Jacob, Asha Elza, Nikhil Ashodariya, and Aakash Dhongade. "Hybrid search algorithm: Combined linear and binary search algorithm." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389704.

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Kong, Taeyong, Taeri Kim, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, and Sang-Wook Kim. "Linear, or Non-Linear, That is the Question!" In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3488560.3498501.

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Jones, Colin N., and Jan M. Maciejowski. "Reverse Search for Parametric Linear Programming." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377799.

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Heide, Friedhelm Meyer auf der. "Nondeterministic versus probabilistic linear search algorithms." In 26th Annual Symposium on Foundations of Computer Science (sfcs 1985). IEEE, 1985. http://dx.doi.org/10.1109/sfcs.1985.38.

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Heydari, Javad, and Ali Tajer. "Quickest linear search over correlated sequences." In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282558.

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Tekli, Joe, Gilbert Tekli, and Richard Chbeir. "Almost Linear Semantic XML Keyword Search." In MEDES '21: International Conference on Management of Digital EcoSystems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3444757.3485079.

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Venkatesan, Sibi, James K. Miller, Jeff Schneider, and Artur Dubrawski. "Scaling Active Search using Linear Similarity Functions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/401.

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Active Search has become an increasingly useful tool in information retrieval problems where the goal is to discover as many target elements as possible using only limited label queries. With the advent of big data, there is a growing emphasis on the scalability of such techniques to handle very large and very complex datasets. In this paper, we consider the problem of Active Search where we are given a similarity function between data points. We look at an algorithm introduced by Wang et al. [Wang et al., 2013] known as Active Search on Graphs and propose crucial modifications which allow it to scale significantly. Their approach selects points by minimizing an energy function over the graph induced by the similarity function on the data. Our modifications require the similarity function to be a dot-product between feature vectors of data points, equivalent to having a linear kernel for the adjacency matrix. With this, we are able to scale tremendously: for n data points, the original algorithm runs in O(n^2) time per iteration while ours runs in only O(nr + r^2) given r-dimensional features. We also describe a simple alternate approach using a weighted-neighbor predictor which also scales well. In our experiments, we show that our method is competitive with existing semi-supervised approaches. We also briefly discuss conditions under which our algorithm performs well.

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Reports on the topic "Linear search"

1

Ghosh, Subir. Influential Nonegligible Parameters under the Search Linear Model. Fort Belvoir, VA: Defense Technical Information Center, April 1986. http://dx.doi.org/10.21236/ada170079.

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Ghosh, Subir. Influential Nonnegligible Parameters Under the Search Linear Model. Fort Belvoir, VA: Defense Technical Information Center, April 1986. http://dx.doi.org/10.21236/ada172007.

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Hough, P. D. Stable computation of search directions for near-degenerate linear programming problems. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/477537.

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Lewis, Robert Michael, Virginia Joanne Torczon, and Tamara Gibson Kolda. A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints. Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/893121.

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Chen, Z., S. E. Grasby, C. Deblonde, and X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.

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The objective of this study is to search for features and indicators from the identified geothermal resource sweet spot in the south Mount Meager area that are applicable to other volcanic complexes in the Garibaldi Volcanic Belt. A Landsat 8 multi-spectral band dataset, for a total of 57 images ranging from visible through infrared to thermal infrared frequency channels and covering different years and seasons, were selected. Specific features that are indicative of high geothermal heat flux, fractured permeable zones, and groundwater circulation, the three key elements in exploring for geothermal resource, were extracted. The thermal infrared images from different seasons show occurrence of high temperature anomalies and their association with volcanic and intrusive bodies, and reveal the variation in location and intensity of the anomalies with time over four seasons, allowing inference of specific heat transform mechanisms. Automatically extracted linear features using AI/ML algorithms developed for computer vision from various frequency bands show various linear segment groups that are likely surface expression associated with local volcanic activities, regional deformation and slope failure. In conjunction with regional structural models and field observations, the anomalies and features from remotely sensed images were interpreted to provide new insights for improving our understanding of the Mount Meager geothermal system and its characteristics. After validation, the methods developed and indicators identified in this study can be applied to other volcanic complexes in the Garibaldi, or other volcanic belts for geothermal resource reconnaissance.

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Koch, J. A. Imaging crystal/spectral line search. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/670066.

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Lewis, Robert Michael, ), Joshua D. Griffin, and Tamara Gibson Kolda. Asynchronous parallel generating set search for linearly-constrained optimization. Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/891372.

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Kolda, Tamara G., Joshua Griffin, and Robert Michael Lewis. Asynchronous parallel generating set search for linearly-constrained optimization. Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/1139970.

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Lewis, Robert Michael, Virginia Joanne Torczon, and Tamara Gibson Kolda. Stationarity results for generating set search for linearly constrained optimization. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/918255.

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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Madrid: Banco de España, February 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area.

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